SLAS2020 International Conference & Exhibition

The SLAS2020 course package contains 118 presentations including:

95 presentations from ten scientific tracks
Advances in Bioanalytics and Biomarkers
Assay Development and Screening
Automation and High-Throughput Technologies
Biologics Discovery
Cellular Technologies
Data Analysis and Informatics
Drug Target Strategies
Micro- and Nanotechnologies
Molecular Libraries
Precision Medicine Technologies
2 keynote speakers
3 Ignite Panel Discussions
4 Ignite Academic Collaboration Presentations 
14 Ignite Award Finalist Presentations

Based on presenter permission, 118 of the 161 total SLAS2020 presentations are available on-demand.
The SLAS Scientific Program Committee selects conference speakers based on the innovation, relevance and applicability of research as well as those that best address the interests and priorities of today’s life sciences discovery and technology community. All presentations are published with the permission of the presenters.

Nicola Richmond, Ph.D.



Nicola trained as an algebraist up to Ph.D. level then moved into chemoinformatics via a two year role in the Statistics and Modelling Group at Unilever R&D. After completing a post-doctoral fellowship with Prof. Peter Willett at the University of Sheffield, Nicola joined GSK’s then Cheminformatics group in 2004 where she held a number of roles including developing methodologies for chemical database searching and hit identification in the presence of process error for high-throughput, high-content screening and both supporting and leading early, small molecule, drug discovery programmes. Nicola then joined one of GSK’s fledgling data science groups and led the Biopharm Digital, Data and Analytics focus area, overseeing a number of high-impact projects and continuing with methods development. She is now a director in the newly-formed AI and Machine Learning team where she is predominantly focussed on AI research.

Ellen Berg, Ph.D.

Chief Scientific Officer, Translational Biology

Eurofins Discovery

Ellen L. Berg, PhD, is Chief Scientific Officer, Translational Biology at EurofinsDiscovery. She leads the scientific direction of the company’s in vitro phenotypic profiling services including the BioMAP® human primary cell-based assay platform. Dr. Berg was co-founder and CSO of BioSeek (acquired by DiscoverX) and before that led a research team developing biotherapeutics at Protein Design Labs (now AbbVie). She received her PhD from Northwestern University and was a postdoctoral fellow at Stanford University. Dr. Berg has served in various positions with the Society of Laboratory Automation and Screening (SLAS), is a board member of the American Society for Cellular and Computational Toxicology (ASCCT), and a member of the Society of Toxicology (SOT) and Inflammation Research Association. Her research interests include drug and toxicity mechanisms of action, phenotypic drug discovery, and predictive toxicology. Dr. Berg holds several patents in the field of inflammation, and has authored over 80 publications.

Stephen Previs


Responsible for conducting pharmacology and mechanism of action studies, activities span early discovery target screening and development of hits for in vivo studies. Primarily involved in experimental design strategies and interpretation of metabolic flux data.

Loren Olson


Martin Giera, Ph.D.

Head of the Metabolomics Group at the Center for Proteomics and Metabolomics

Leiden University Medical Center

Martin studied pharmacy in Heidelberg and Munich. He is the head of the Metabolomics group at the Center for Proteomics and Metabolomics at the Leiden University Medical Center (LUMC). He holds a PhD in Pharmaceutical Chemistry obtained from the Ludwig-Maximilians-University in Munich (Germany) . With a postdoctoral fellowship (DAAD stipend), he joined the group of Prof. Hubertus Irth at the VU University Amsterdam. Following a research stay in the laboratory of Prof. Charles Serhan at Harvard Medical School, he moved to the LUMC where he today heads the Metabolomics group. He is editor of the book “Clinical Metabolomics”. Martin is a permanent committee member of the inter-disciplinary panel of the FWO and served in several EU committees. His main interests lie in clinical and fundamental disease-related research, using metabolomics-based approaches and notably lipidomics.

Milan Mrksich, Ph.D.

Henry Wade Rogers Professor

Northwestern University

Dr. Milan Mrksich, the inventor of the SAMDI technology, is the Henry Wade Rogers Professor at Northwestern University and holds appointments in the departments of chemistry, biomedical engineering and cell biology. His research combines synthetic chemistry with materials science to study important problems in cell biology. Dr. Mrksich develops biochips for a host of biological and biotechnological applications and develops mimics of the extracellular matrix for studies of cell adhesion and migration.

Having authored 150 peer-reviewed articles, many based on the SAMDI technology, Dr. Mrksich was named by Technology Review magazine as one of the 100 Top Young Innovators in 2002. He is also a recipient of the ACS Arthur C. Cope Young Scholar Award (2003) and a Fellow, American Institute for Medical and Biological Engineering (2012).

Dr. Mrksich has a doctorate in chemistry from California Institute of Technology and served a postdoctoral fellowship at Harvard University.

Associate Principal Scientist

Senior Scientist


Aarti  was trained as an organic chemist, obtaining her M.S. from the UGA at Athens in 2005 with Prof. Geert-Jan Boons. She started her industrial career as a medicinal chemist at Vertex, and  she moved to AstraZeneca as a lead chemist in both oncology and the fragment based lead generation group contributing to the discovery of several clinical candidates. In 2013 she joined AstraZeneca’s newly created Chemical Biology group, focusing on small molecule target deconvolution and target engagement, supporting multiple drug discovery projects at different stages.    
In the chemical biology group, Aarti has championed the application of the CETSA and in-cell Chemoproteomics to measure cellular target engagement and target validation for multipass transmembrane protein receptors. Aarti’s efforts in studying difficult targets was recognized in 2017 with AstraZeneca’s Breakthrough Scientist of the Year award. Again in 2020, she was awarded for finding the target of a small molecule modulator of inflammation.

Joshua L. LaBaer, M.D., Ph.D.

Executive Director for the Biodesign Institute

Arizona State University

Joshua L. LaBaer, M.D., Ph.D., a leading researcher in cancer and personalized medicine, is the executive director for the Biodesign Institute at Arizona State University.

Joshua LaBaer is one of the nation’s foremost investigators in the rapidly expanding field of personalized medicine. His efforts involve the discovery and validation of biomarkers — unique molecular fingerprints of disease — that can provide early warning for those at risk of major illnesses, including cancer and diabetes.

LaBaer also serves as director of Biodesign’s Virginia G. Piper Center for Personalized Diagnostics, where he leads a staff of nearly 100 faculty and biologists, microbiologists, engineers, informaticists and students who combine their expertise to find ways to decrease the impact of human disease. As director, he holds the university’s first Piper Chair in Personalized Medicine.

Andrea Peier


Over 15 years in industry working in various therapeutic areas with a focus on in vitro models for disease. Currently leading cell-based assay development and screening for target- and phenotypic-based screens.

Laurie Parker

University of Minnesota

Dr. Parker was trained as a synthetic chemist before moving into chemical biology, proteomics, and kinase assay design. Her lab develops new substrates and read-out methods for measuring kinase activity in vitro and in live cells.

Gianluca Pegoraro

National Cancer Institute

Dr. Pegoraro heads the High-Throughout Imaging Facility (HiTIF), which provides CCR investigators with the expertise and the technology needed to set-up, optimize and implement high-content imaging (HCI) assays. HCI assays are used to analyze large numbers of experimental conditions, such as in high-throughput RNAi or chemical compounds screens. In addition, the HiTIF applies HCI in combination with single-cell image and data analysis to quantify exceedingly rare but biologically important events in heterogeneous populations of cells.

Shane Buker

Head of Lead Discovery

Accent Therapeutics

Currently head of lead discovery at Accent Therapeutics, where I coordinate the development of various novel biochemical assays and as well as their use in high throughput screening. Formerly at FORMA Therapeutics and Constellation Pharmaceuticals.

Charles Lesburg

Merck & Co. Inc.

Charles Lesburg trained in the laboratory of David Christianson, where he studied structure-function relationships in CAII. He also kicked off a collaboration studying sesquiterpene synthases. Employed at Schering-Plough in Kenilworth NJ, he published the first structure of HCV polymerase, and contributed to many drug discovery programs. After acquisition by Merck & Co., he moved to MRL/Boston to enhance the drug discovery efforts and has been involved in many programs – mainly concerning oncology and immunology – including identification and optimization of small molecule inhibitors and agonists, as well as peptides and oligonucleotides. He remains passionate about the development and use of computational tools for the organization and dissemination of macromolecular structural information, from experiment to analysis. Since 2005, Dr. Lesburg has served on the board (Chair, 2007-2009) of the Industrial Macromolecular Crystallographic Association, dedicated to the funding and operation of a synchrotron X-ray beamline.

Timothy Spicer, Ph.D.

Co-Director, Scripps Molecular Screening Center

Scripps Research-Florida

Tim Spicer joined Scripps early on at its onset in Jupiter Florida and has been working there for 10 years. Prior to that he was employed at Bristol-Myers Squibb Co. for nine years as a Research Scientist in the Department of Lead Discovery and Profiling, Discovery Technologies and Infectious Diseases. Tim received an MS in microbiology at the SUNY Health Science Center at Syracuse. He has authored 75 publications and 4 patents. Tim works across multiple organizations and currently co-directs the Scripps Molecular Screening Center. He leads a team that implements assays and supports screening on fully automated platforms. He has experience developing anti-microbial, GPCR, kinase, protease, nuclear receptor, and/or ion channel assays in a variety of HTS-compatible formats (reporter-gene, TR-FRET, HTRF, fluorescence, luminescence, and absorbance). He is recognized at Scripps and by collaborators as a leader in evaluating new assay technologies for the purpose of making UHTS the standard.

Oivin Guicherit, Ph.D.

Director of Applications


Since obtaining his PhD from Baylor College of Medicine and postdoctoral training at Stanford University, Dr. Oivin Guicherit, Director of Applications at StemoniX, has invested 20+ years in developing the biopharma industry, designing, implementing, and managing drug discovery efforts. Over the years he has cultivated extensive knowledge and expertise in applying chemical and oligonucleotide tools to cell-based (2D/3D/co-culture) and organotypic platforms for target-identification and validation, screening and lead optimization, and MOA dissection. Before joining StemoniX, Dr. Guicherit worked in development at Janssen Pharmaceuticals, Regulus Therapeutics, Pfizer Global R&D, and ChemDiv.

Paul Johnston, Ph.D.

University of Pittsburgh Dept. Pharmaceutical Sci.

Dr. Johnston obtained a B.Sc. with Honors (2.1) (1978) and a Ph.D. (1983) in Biochemistry, from the University of East Anglia, Norwich, England. Postdoctoral positions at the University of North Carolina, Duke University, and the University of Texas Southwestern Howard Hughes Institute provided diverse experience in biochemistry, molecular biology, cell biology, immunology, protein purification and recombinant protein expression. Dr Johnston has twenty-nine years of drug discovery experience in the Pharmaceutical, Biotechnology, and Academic sectors. He is an innovator of cell based lead generation, and founding member of the Society for Biomolecular Imaging and Informatics. In 2005, he joined the University of Pittsburgh to design and build the Pittsburgh Molecular Library Screening Center where he led 21 HTS campaigns and the NCI 60 drug combination screen. In 2011, he established independent chemical biology laboratories to discover new drugs or drug combinations for prostate cancer, melanoma, and head and neck cancers.

Marie Schwinn, Ph.D.

Advanced Technologies Group

Promega Corporation

Dr. Schwinn is a member of the cell biology team within the Advanced Technologies Group at Promega where she has helped develop luciferase-based technologies for studying intracellular protein interactions, abundance, and post-translational modifications. Most recently, she has focused on combining these technologies with CRISPR/Cas9 to study protein dynamics at the endogenous level. Prior to joining Promega, Dr. Schwinn earned her doctorate in Biochemistry from the University of Wisconsin-Madison in the lab of Dr. Hector F. DeLuca, and she received post-doctoral training from Dr. Donna M. Peters in the Department of Pathology and Laboratory Medicine.

Sapna Desai

Biological Investigator


Sapna Desai is a Biological Investigator within the Screening, Profiling and Mechanistic Biology group at GlaxoSmithKline. Her focus is using molecular and cellular biology techniques to design, build and execute complex and recombinant cellular screening assays supporting a variety of therapeutic areas across preclinical drug discovery. Prior to joining GlaxoSmithKline in 2004 she completed her BSc in Molecular Biology from the University of Hertfordshire.

Kristin Riching, Ph.D.

Senior Scientist

Promega Corporation

Kristin received her PhD in Biomedical Engineering from the University of Wisconsin – Madison, where she studied the structural and mechanical properties of collagen fibers and their effects on breast cancer cell migration in invasive ductal carcinoma. She joined Promega in 2014 as a postdoctoral researcher and is currently a Senior Scientist developing technologies to characterize degradation and protein interactions within the ubiquitin proteasomal pathway in living cells.

Feng Liang, Ph.D.

Group Leader

Cystic Fibrosis Foundation

Feng Liang is a skilled biologist with pharmaceutical experience and a successful track record in target validation, assay development, screening, and SAR. He received B.S. (Biochemistry) at Peking University and Ph.D. (Molecular Biology) at Cornell University. Experienced in both pharmaceuticals and start-up biotech, Feng is current a group leader in CF Foundation Lab.

Pavan Chandra Konda, Ph.D.

Duke University

I am currently a postdoctoral researcher in the computational optics lab at the Duke university led by Dr. Roarke Horstmeyer. Before my current position, I worked as a postdoctoral researcher in the school of physics and astronomy at the university of Glasgow, where I have also conducted my PhD research. I received my bachelor’s degree in Engineering Physics from the Indian Institute of Technology Guwahati.

My current research interests are in the areas of computational imaging, novel microscopy methods, low-cost microscopy, high-speed gigapixel imaging, micro-endoscopy and in vivo imaging. Other areas of expertise include multi-spectral imaging, extended depth-of-field imaging, compressive sensing and imaging using speckle.

Thierry Dorval, Ph.D.

Head of Data Science Lab


Thierry Dorval received a B.S. degree in physic and graduated in image processing and artificial intelligence at Pierre & Marie Curie University, Paris, France. He received a Ph.D.d then joined the Institut Pasteur Korea in 2005 first as researcher in biological image analysis then as a group leader specialized in High Content Screening applied to cellular differentiation as well as toxicity prediction.

In 2012 he joined AstraZeneca, UK. His activities were about developing and advising on quantitative image and data analysis solutions in support of high content phenotypic screens in addition to cellular and tissue imaging.

In 2015 he joined Servier, France, as leader of the High Content Screening group within CPCB CentEX. He is currently still working on phenotypic approaches to improve drug discovery pipeline using high content strategies and currently lead the Data Science Lab.

Lynn Rasmussen, B.S., M.S.

Associate Director

Southern Research

Lynn Rasmussen holds a B.S. in both Chemistry and Biology from Virginia Tech and a M.S. in Biomedical Sciences from Hood College. She has worked on Rickettsia at the University of Maryland, emerging infectious diseases at USAMRIID and Lentiviruses at the Frederick Cancer Research and Development Center. She served as coordinator for the DNA Sequencing Core facility at the FCRDC where she designed automation systems to handle the increasing throughput requirements of the Core. She is now Associate Director of the Southern Research HTS Center where she has overseen the screening of a wide range of HTS campaigns. These include infectious disease, cancer and obesity targets. She designed the automation platforms currently in use at the Southern Research HTS Center, including one housed in a bio-containment enclosure for infectious disease work

Shushant Jain

Group Leader

Charles River

Shushant Jain is currently a group leader at Charles River where he utilizes high throughput – high content methodology for target identification, target validation, lead identification, and lead optimization in physiologically relevant cellular model systems such as primary or stem cells. Prior to Charles River, he was the lead developer of phenotypic assays to enable discovery of novel pathways or targets in numerous neurodegenerative diseases as well aid in the understanding the role of common genetic variation in disease pathogenesis.

Paul Harper

Principal Scientist


Paul Harper is a Principal Scientist within the Global High Throughput Screening (HTS) Centre at AstraZeneca. Over the past two decades he has held positions at GlaxoSmithKline, Pfizer and AstraZeneca, during which he has undertaken cellular and biochemical assay development, small molecule screening and an ever-increasing focus on new technology, automation and facility development. Since 1999 within AstraZeneca Paul has successfully deployed over 15 large scale automation projects, across a variety of research functions with multiple integration vendors. In his current role Paul is tasked to design the UK Centre for Lead Discovery, creating the next generation of automated screening technology for AstraZeneca’s upcoming Cambridge research facility.

James Beck

Eli Lilly

Mitchell Hull

Calibr at Scripps Research

Mitch began his screening career in 1997 at Tularik Inc. Here, he worked in assay development and championed the company’s move from 96 to 384 well assays. He left Tularik in 2001 to pursue a short career in web development. Returning to biotech and in 2005, he joined the Advanced Automation Technologies group at the Genomics Institute of the Novartis Research Foundation (GNF). Here, in addition to screening, he worked with project teams to integrate large uHTS systems, profiling systems and an automated protein production platform. He left GNF in 2012 to join the California Institute for Biomedical Research (Calibr) and to lead it’s HTS group. In addition to HTS, he has built and maintained customized Laboratory Information Management Systems (LIMS) for compound management and screening.

David Dambman

Director of Software Development

Biosero, Inc.

avid Dambman, Director of Software Development for Biosero has devoted his time, knowledge and efforts into software and engineering development for the company over the past 5 years. As a lifelong engineer and programmer, David has implemented his vast knowledge and capabilities into the development and integration of Biosero's Green Button Go scheduling software. Under his direction, Biosero has developed an easy and intuitive software for users that has the capability of controlling simple to complex workstations using the most advanced software technology available. David's vast knowledge and programming capabilities have assisted customers in customizing the runtime environment within their lab as well as improving their throughput efficiency and accuracy.

Silvio Di Castro, M.Sc.


A chemical engineer by training, Silvio joined Compound Management at AstraZeneca in 2002. During the time at AZ, Silvio has developed a deep expertise in laboratory automation, from programming simple benchtop units to developing large automated platforms for high-throughput screening. Working across functions and countries, Silvio has developed a large network of strong connections, and implemented solutions for automated sample handling in several areas of the business.

In his current role  as associate principal scientist, Silvio is leading the deployment of the first fully acoustic Sample Management system in the world, developed by AZ in a collaborative
partnership with Labcyte and Brooks. He is also aligned with global initiatives to improve sample storage, handling and usage of new therapeutic modalities (peptides, RNAs, DNAs and more).

Prior to joining AstraZeneca, Silvio gained an MSc in Chemical Engineering at the "Federico II" University, Naples (Italy), under the supervision of Prof. Guido Greco.

Adam Corrigan

Image Analytics team in Discovery Sciences


I currently work in the Image Analytics team in Discovery Sciences at AstraZeneca. Since joining AZ three years ago, the field of image analysis has been transformed by the explosion of machine learning and AI methods, and I have fortunate enough to develop and apply these cutting edge techniques to explore and shape the future of cellular screening and imaging.

Prior to joining AstraZeneca I worked in academia developing quantitative microscopy methods to solve a range of biological problems, including gene expression, cell division, and amyloid plaque formation.

Mahnaz Maddah

Dana Solutions

Dana Solutions LLC

Mahnaz is a Managing Member of Dana Solutions, and has 16+ years of experience in artificial intelligent algorithms for products in life sciences. She is an inventor of multiple products including an AI-based cell counting software for an infertility medical device, an AI-based phenotypic drug screening platform, and a structural toxicity testing platform that is the basis of a current research collaboration with the FDA. As a co-founder and CTO of Cellogy, she led the technical development of Pulse, the first computer vision system for video-based characterization of stem cell-derived cardiomyocytes. She has been co-PI on an NIH Phase-II SBIR that supported the development of Pulse. As a lead scientist at Auxogyn, GE Research, and SRI, she developed novel algorithms for different applications. She received her master's degree from Univ. of Tehran and her PhD in Electrical Engineering and Computer Science from MIT, Computer Science and Artificial Intelligence Lab (CSAIL).

Avtar Singh

The Broad Institute

Postdoctoral associate at the Broad Institute of Harvard and MIT working in the lab of Paul Blainey.

Gregory Vladimer, Ph.D.



Gregory Vladimer is the CSO of Allcyte, a startup in Vienna, Austria. He received his PhD from UMass Medical School where he studied inflammation, and was a Senior Postdoctoral Fellow at CeMM in Vienna. Together with the Department of Hematology of the MedUniWien, spearheaded the use of single-cell image-based screening for the personalized identification of treatments for hematological cancers through a prospective clinical trial, and several retrospective studies. His work is published in journals including Nature, Lancet-Hematology, NEJM, and Cell, and he is on the steering-committee for the precision haematology working group of EHA advocating for standards around functional testing.

Jonathan Wingfield


Joined AZ in 2000, as part of a team responsible for delivering automation solutions and technology into the disease area teams post high-throughput screening. Was invited to establish a Lead Generation Automation team within Oncology and this evolved into a centralised biochemical screening team in 2006. The centralised team utilized leading edge technology to deliver high quality data to global projects, this included delivery of acoustic droplet ejection technology. In 2008 the team won the Microsoft Innovation in Pharma Award for deployment of a LIMS system. In my current science role, I remain interested in landing technologies that can add value to our core business, one such example is the acoustic mass spectrometry collaboration between Labcyte and AZ. This project won the SLAS Innovation award (2015) and has generated a significant amount of external interest.

Jeffrey Gross


Laura Riva, Ph.D.

Sanford Burnham Prebys Medical Discovery

Laura graduated and got a Master in Molecular Biology from the University of Padova (Italy) in 2009. She received her PhD in virology from the University of Liege (Belgium) in 2013, with a thesis on the tegument protein ORF9p of the Varicella Zoster virus (VZV). She then moved to France for a postdoctoral opportunity at the Pasteur Institute of Lille. During her training, she worked on the Hepatitis C virus (HCV) with a focus on the entry and replication of the virus, becoming familiar with high-content and small chemical compound screening.

She joined the SBP Medical Discovery Institute in 2017 as a postdoctoral associate. Her current main focus is the identification and development of antivirals targeting emergent RNA viruses, including Flaviviruses and ebolavirus, through high-throughput screening approaches.

Steven van Helden, Ph.D

Pivot Park Screening Centre

Steven van Helden studied chemistry at Utrecht University and, after obtaining his Ph.D. , worked in various roles in pharmaceutical industry for 20 years. Since 2003 he has been responsible for High Throughput Screening (HTS) operations and strategy at Organon/MSD. After the closure of those research facilities he developed a business plan for continuation of the screening activities in Oss, The Netherlands. This led to the formation of the Pivot Park Screening Centre (PPSC) and a central role of this company in the European Lead Factory. Steven is now Chief Technology Officer at PPSC.

Pierre Baillargeon

The Scripps Research Institute

Senior Robotics Engineer

Pierre is currently the Senior Robotics Engineer within the Lead Identification lab at Scripps Research where he supports Compound Management, High Throughput Screening, Assay Development and Informatics efforts by developing, assembling and integrating novel automated hardware and software. Pierre has worked at Scripps Research since the establishment of the lab in 2005. The Lead Identification lab at Scripps is responsible for supporting both industrial and academic drug discovery efforts with a proprietary >600,000 sample library and the NIH's >300,000 sample MLPCN collection.

Over the past decade at Scripps, Pierre has lead engineering efforts on several novel laboratory instruments including the Plate Auditor microplate inspection platform and most recently the Microplate Assistive Pipetting Light Emitter.

Peter Bajcsy

NIST National Institute of Standards and Technology

Rob Howes, Ph.D.


Rob completed his PhD at the MRC Laboratory of Molecular Biology understanding EGFR signalling pathways in Drosophila followed by post-doctoral positions at Stanford University, USA and Cambridge University. In 2001 he joined Vernalis, a UK-based drug development company where he was in charge of the Screening group working across several disease areas including Oncology and Neuroscience.

In 2008 he helped start Horizon Discovery, a translational genomics company that uses the adeno-associated virus (AAV) and other gene targeting technologies such as CRISPR, to generate isogenic human cell lines. At Horizon he also established their Centres of Excellence program to enable academic groups with the gene targeting technologies which included such groups as the National Institutes of Health, Cambridge
University and Yale University. 

In 2013 he joined MedImmune, the global biologics division of AstraZeneca, leading the High Throughput Screening team based in Cambridge, UK. 

Markus Muellner, Ph.D.

Chief Technology Officer


Markus is the Chief Technology Officer at PhoreMost, a Cambridge UK Biotech with a novel target discovery platform to drug previously undruggable targets. Markus has a background in engineering, IT and Life sciences. He has a PhD from the Medical University of Vienna (Medical Chemistry) that was followed by a postdoc at the Center for Molecular Medicine (CeMM) where he established a functional genetics platform that led to the discovery of novel cancer vulnerabilities. He joined PhoreMost in 2015, set up the SITESEEKER technology platform and is heading the R&D and bioinformatics teams.

Gregory Alberts, Ph.D.

Global Subject Matter Expert

Lonza Inc.

Dr. Gregory Alberts is currently the Global Subject Matter Expert for Lonza, and has worked at Lonza (and previously Amaxa) since 2003, and has considerable experience in Nucleofection, primary cells (including stem cells and iPSC generation), and endotoxin testing solutions, and is well versed in the use of CRISPR and other genome-editing technologies with Nucleofection. He received a Ph.D. from the George Washington University in Molecular Biology, and an M.S. in Bacterial Genetics from UIC.

David Gilham


Thomas Miller

Institut Paoli Calmettes

Exploring the druggability of CD47 signaling using small molecules and novel discovery methodologies. Focused on the development of "second-generation" CD47 inhibitors for anti-tumor immunotherapy. Principal Investigator with expertise in assay development, high-throughput screening, structure-based drug development, tumor immunobiology, public and private fundraising, and international team leadership.

Torben Gjetting


Aram Chung, Ph.D.

Associate Professor / CEO

Korea University / MxT Biotech

Aram Chung is an associate professor in the School of Biomedical Engineering at Korea University. He received his undergraduate degree in mechanical engineering from Seoul National University, completed a Ph.D. in mechanical engineering from Cornell University, and carried out postdoctoral studies in bioengineering at UCLA. He then joined the Department of Mechanical Engineering at RPI as an assistant professor until he moved to Korea University. His lab investigates microscale flows to develop a new class of microfluidic systems for biomedicine. He is a founder/CEO of MxT Biotech, which is focused on developing transformative microfluidics-based cell therapies for cancer patients.

Joseph de Rutte

University of California, Los Angeles

Graduate Student Researcher in the Di Carlo Lab working on microparticle-based technologies for next generation diagnostics and therapeutics.

Raehyun Kim

University of North Carolina at Chapel Hill

Nicholas Geisse

Chief Science Officer

NanoSurface Biomedical

Nick Geisse is the Chief Science Officer at NanoSurface Biomedical, a spin-out company from the University of Washington Bioengineering department in Seattle. Nick graduated from Boston University with a B.A., Biochemistry and Molecular Biology. He completed his graduate studies (Ph.D.) in Pharmacology at Cambridge University in England under Dr. RM Henderson, followed by a postdoctoral fellowship in cardiac cell and tissue engineering at the Harvard University School of Engineering and Applied Sciences under Prof. Kevin Kit Parker. After his postdoc, Nick went into industry and worked for Asylum Research (a manufacturer of Atomic Force Microscopes) as a scientist and project manager. At NanoSurface, Nick is part of the executive management team and is specifically tasked with guiding the overall scientific strategy of the company in addition to developing and bringing to market NanoSurface’s next-generation of innovative products aimed at increasing the predictive power of in vitro cell based assays.

Scott Magness

UNC Chapel Hill

Lisa Mohamet, Ph.D.


Lisa obtained her PhD in cell biology from The University of Manchester in 2006 and has over a decade of experience in human stem cell biology in both academia and industry. She is an Enterprise Fellow of the Royal Society of Edinburgh/BBSRC and is co-founder of a spin-out company, StrataStem Ltd. Lisa was named as one of the Biobeat 2016 ‘rising stars’ of the top 50 UK women entrepreneurs and leaders in Biobusiness. In 2017, Lisa joined GSK in preclinical R&D to head up the advanced cellular model platform group (including iPSC and 3D models) to support target validation and lead drug discovery programs. More recently, she moved to the newly formed Functional Genomics department to lead large-scale CRISPR screening platforms for novel target ID. Lisa has passion for the application of novel technologies to support the translation of scientific discoveries into potential therapeutics.

Richard Cheng, B.Sc., M.A.Sc.

University of Toronto

Samuel Berryman

University of British Columbia

Samuel Berryman is a master’s student in the Multi-Scale Design lab under the supervision of Dr. Hongshen Ma and is a recipient of a graduate award funded by the Centre for Blood Research. Samuel’s research focuses on developing new assays for cellular informatic and micro-scale automation. His work involves utilizing deep-learning for the identification and segmentation of cellular phenotypes in fluorescent microscopy images. Samuel is also working on integrating these informatic approaches with microscope automation for single-cell isolation, separation and sequencing.

Lauren DeMeuse

Roam Analytics

Viral Vyas, MSIS

Lead IT Business Partner Translational Medicine

Bristol Myers Squibb

Viral Vyas is a Lead IT Business Partner at Bristol Myers Squibb responsible for leading teams that deliver informatics capabilities to broad range of scientific stakeholders in the Translational Medicine organization. He received his undergraduate education in Microbiology and Masters in Information Management. Viral Joined Bristol Myers Squibb in 2000 as an Associate Research Scientist in DMPK group and transitioned into R&D IT in 2004. Viral has worked on numerous informatics initiatives that streamlined laboratory workflows. He is passionate about creating informatics strategies that allow scientists to focus on creating scientific knowledge rather than manually assembling and reporting data.

Patrick Cullen

Yahara Software

James Smagala

Revolutionary Informatics

Umesh Katpally

Director - Data Advisory for Data Sciences

BC Platforms

Umesh Katpally is currently Director - Data Advisory for Data Sciences at BC Platforms. Previously, he worked at Novartis focusing on research informatics and before that he worked at various bio-pharma companies and academic institutions focusing on viral vaccines research. He is interested in enabling innovation with the use of new and evolving technologies that fall under the Lab of the Future umbrella. In his current role at BC platforms he supports potential and current Data Science customers with advising on the strategy needed to answer their research questions, inclusive of data sources and data points. This is to help with integrating and mining clinical phenotypic data combined with genotypic profiling data sets from various data sources to augment drug development pipeline. BC Platforms is a world leader in providing powerful genomic data management and analysis solutions to address some of the biggest healthcare challenges today by leveraging the convergence of genomics and healthcare information technologies.

Christoph Otto

TU Dresden

Christoph Otto studied Bioprocess Engineering at the TU Dresden, Germany and graduated in 2015. Since then he is a PhD student in the SmartLab-systems research group at the Chair of Bioprocess Engineering. He investigates the logistics in current laboratory environments and designs solutions towards automation and higher troughput in the field of laboratory automation and automated sample treatments focusing on samples in culture dish environments.

Guru Singh


Guru joins LabTwin with several years of experience as a biotech researcher and a lifetime’s worth of passion for entrepreneurship. Beginning his journey as a biotech engineer in India, he published numerous research papers and co-founded life science startups and foundations. After earning his Professional Science Master’s in Biotechnology from the University of Delaware, Guru served as a marketing leader at two California-based life science SaaS startups.

Michael Shanler

Gartner, Inc.

Michael Shanler advises CIOs and IT leaders on industry technologies and trends impacting the life science and healthcare industry. Prior to Gartner, Michael worked at BD Biosciences, where he developed scientific and analytical products. He holds several patents for life science products used by researchers across the globe, today. He also worked at Genetics Institute in Cambridge, MA, and specialized in laboratory automation and laboratory informatics. He is a biomedical engineer by training (Boston University) and lived on four continents as a child. He lives with his lovely wife and three children in Sudbury, MA USA, is an avid skier, home head chef, guitarist, and race car-junkie.

Oren Kraus, BASc, MASc, Ph.D.

Cofounder CTO

Phenomic AI

Oren Kraus co-founded Phenomic AI after completing his Ph.D. in Dr. Brendan Frey's lab at the University of Toronto. His research focused on applying deep learning to high-throughput microscopy screens used in drug discovery and cell biology research. Together with Jimmy Ba and collaborators at the Donnelly Centre for Cellular and Biomolecular Research (CCBR), Oren was one of the first to publish the application of deep learning to microscopy data. Oren founded Phenomic AI with the goal of accelerating the interpretation of phenotypes in bio-medical images with machine learning for applications in drug discovery and cellular diagnostics.

Laurence Arnold

Pelago Bioscience

Drug discovery scientist with interests in assay development and target engagement studies. A Background of biophysics and biochemistry within the drug discovery field.

Kirsten Tschapalda


I have a strong interest in cell based high throughput screening (HTS) technologies. Over the years I have worked in both academic and industry HTS campaigns. I started screening during my PhD at the Max-Planck Institute in Dortmund and specialized further during my postdoctoral time at the Karolinska Institute/Scilifelab in Stockholm. Since 2017 I am employed as a senior research scientist in the Global High Throughput Screening Centre at AstraZeneca. Here, I am delivering to both internal and external HTS projects and explore the use of different CETSA technologies in high throughput.

Matthew Robers


Matthew Robers is a Senior Research Scientist and Group Leader at Promega Corporation. Following his post-graduate training at University of Wisconsin-Madison, Matthew has authored nearly 40 peer-reviewed publications and published patents on the application of novel assay chemistries to measure intracellular protein dynamics. Matthew's team currently focuses on the development of new technologies to assess target engagement, and has developed a biophysical techniques for quantifying compound affinity and engagement kinetics at selected targets within intact cells.

Matthew Disney, Ph.D.


Scripps Research

Matt Disney is a Professor in the Department of Chemistry at Scripps Research. His laboratory works on small molecule targeting of RNA and seeks to answer fundamental questions on molecular recognition between RNA and small molecules to study problems of biomedical importance. The group developed a strategies to: (i) design structure-specific small molecules from the RNA’s sequence; (ii) synthesize drugs on-site in disease-affected cells to affect their function and to image them; (iii) study the biology of coding and non-coding RNAs, with a focus on incurable rare diseases and difficult-to-treat cancers; and (iv) interface RNAs with quality control machinery using small molecules and chimeras thereof to eliminate them from cells and animal models of disease. The lab’s research has garnered various awards including the Sackler Prize, Barry Cohen Award in Medicinal Chemistry, NIH Director’s Pioneer Award, the Tetrahedron Young Investigator Award, the Eli Lily Award in Biological Chemistry, and others.

Jay Schneekloth


Robert Blake, DPhil


Robert A. Blake (DPhil) is a scientist in drug and target discovery specializing in oncology drug development, cellular and biochemical assays for high throughput screening, automated fluorescence imaging, signal transduction and protein degradation. He is currently a scientist in the department of Biochemical and Cellular Pharmacology at Genentech and worked previously at Sugen, Exelixis, and iPierian. He has published on the discovery of various types of drugs including: novel selective kinase inhibitors, HSP90 inhibitors and estrogen receptor degraders. His current focus is the development of drugs whose mechanism of action includes the degradation of the target protein.

Emery Smith

Scientific Associate

Scripps Research Institute Florida

Emery Smith is an Scientific Associate at the Scripps Research Institute Molecular Screening Center located in Jupiter, FL, USA. Since joining the group in 2010, Emery has interacted both with corporate and academic partners to implement miniaturized, ultra-high-throughput screens of collections of up to 1 million compounds. The screens have been focused around GPCRs. Prior to joining the group in 2010, Emery was working with Dr. Charles Weissmann at Scripps working on Prion disease. This work started in 2006. Prior to 2006, Emery began his research career as a Research Associate at Athersys Inc. located in Cleveland, OH, USA. There he worked in both the VT (validated targets) group and the Regenerative Medicine group. The focus of this work was in the isolation and validation of adult human stem cells, currently known as Multistem.

Delphine Collin, Ph.D.

Cedilla Therapeutics

Delphine Collin joins Cedilla from HarkerBIO, where she served as their chief innovation officer. Prior to HarkerBIO, she served as senior principal scientist at Boehringer Ingelheim (BI) and led a biophysics group focused on lead identification and optimization in the small molecule drug discovery group. While at BI, Delphine also started her own consulting firm, Delphine Collin Consulting, LLC., where she supported companies with specialized biophysical approaches to drug discovery. Prior to BI, Delphine was a research fellow at Merck & Co. Delphine holds a Ph.D. in biophysical chemistry and doctorate in pharmaceutical sciences from the University of Paris XI, France.

Maciej Grajewski


SG Papertronics B.V. and University of Groningen

Maciej Grajewski studied Biotechnology at Poznań University of Life Sciences. After graduation, he moved to Groningen for his PhD studies on microfluidic cell cultures and optical real-time cell monitoring. This work was performed with professor Verpoorte, in the Pharmaceutical Analysis group. It was also part of a larger EU collaborative network (LiPhos), which aimed to design, develop and test an optical tool for endothelial cell behaviour. Currently, Maciej’s interests revolve around real-time monitoring systems for diagnostic and environmental purposes.

Brian Musselman

​CEO and CSO

IonSense, Inc.

CEO and CSO of IonSense which produces ambient ionization sources and automation devices for mass spectrometry SciMarket Strategies, Inc. President of primary marketing business for instrumentation startups Marketing Director for MALDI technology, Applied BioSystems Director of Michigan State University / NIH Mass Spectrometry Resource Facility PhD Biochemistry, Michigan State Univ., BS Chemistry, University of Connecticut

Daniel Austin, Ph.D.

Professor of Chemistry

Brigham Young University

Daniel Austin is a Professor of Chemistry at Brigham Young University, Provo, Utah. He received a PhD from the California Institute of Technology and has also worked at Sandia National Laboratories. His research focuses on chemical processes in high-velocity impacts relevant to the space environment, and also in development of miniaturized mass spectrometers for portable and field applications.

J. Michael Ramsey

Minnie N. Goldby Distinguished Professor of Chemistry Chair

UNC Chapel Hill

J. Michael Ramsey holds the Minnie N. Goldby Distinguished Professor of Chemistry Chair at the UNC - Chapel Hill. He is also on the faculty of the Departments of Biomedical Engineering and Applied Physical Sciences. He is a member of the National Academy of Engineering and a Fellow of the Optical Society of America, the American Chemical Society, and the American Institute for Medical and Biological Engineering. Moreover, Dr. Ramsey is the scientific founder of Caliper Technologies (NASDAQ:CALP), renamed Caliper Life Sciences and acquired by PerkinElmer in 2011. He is also the scientific founder of the venture-backed companies 908 Devices Inc., a company developing revolutionary compact mass spectrometry and chemical separations-based products, and Genturi Inc., a genomics tools provider. Prof. Ramsey has published over 300 peer-reviewed papers (H-index = 64) and presented over 500 invited, plenary, or named lectures. In addition, he has over 150 issued and 20 pending patents.

Pak Kin Wong

Professor of Biomedical Engineering, Mechanical Engineering, and Surgery

The Pennsylvania State University

Dr. Pak Kin Wong is a Professor of Biomedical Engineering, Mechanical Engineering, and Surgery at the Pennsylvania State University. Dr. Wong’s research focuses on biomimetic materials and systems for elucidating collective cell migration in tissue regeneration and cancer metastasis, and developing molecular diagnostic systems. He has published over 100 peer-reviewed journal articles across multiple disciplines, including Proceedings of National Academy of Sciences, Nature Communications, ACS Nano, and Advanced Materials. Among other honors, Dr. Wong was awarded the NIH Director's New Innovator Award in 2010, Arizona Engineering Faculty Fellow in 2011, AAFSAA outstanding Faculty Award in 2013, and SLAS Tech 10 – A Top 10 Breakthrough in Innovation in 2015. Dr. Wong is elected as a Fellow of the Royal Society of Chemistry, American Institute for Medical and Biological Engineering, and Society for Laboratory Automation and Screening for his contributions in biomedical engineering and nanomedicine.

Georges Muller, Ph.D.

SEED Biosciences SA

Dr. Georges Muller is the CEO and cofounder of SEED Biosciences SA, a spin-off from Ecole Polytechnique Federale de Lausanne (EPFL), developing and commercializing enabling technologies for personalized medicine at a single cell resolution. He is co-inventor of SEED Biosciences’ core technology called DispenCell and co-authored several patents. He is the recipient of multiple awards for young entrepreneurs, including the Venture Leaders Prize in 2017 (given to top Swiss entrepreneurs) as well as the Ignite Award at SLAS2019, Washington, DC. Georges holds a masters and a Ph.D. in bioengineering from EPFL.

John Griffin, Ph.D.

Integral Health

John Griffin is Chief Scientific Officer at Numerate, which has developed and applies an AI-driven approach to small molecule drug design. He was formerly Co-Founder and Chief Scientific Officer of Theravance and Assistant Professor of Chemistry at Stanford University. John is the author of 39 publications, an inventor of 27 issued patents, and the recipient of awards including a Cope Scholar Award from the ACS and a Dean's Award for Teaching from Stanford. He was recently elected a Fellow of the American Association for the Advancement of Science. John received a B.S. in Chemistry from Hope College, a Ph.D. in Chemistry from Caltech and was a Postdoctoral Fellow at Harvard Medical School.

Carleen Klumpp-Thomas


Carleen, an expert in automation engineering by profession, earned her B.S. degree in Bioengineering from Syracuse University and her M.S. in Biomedical Engineering from NYU Polytechnic School of Engineering. Prior to joining NCATS, Carleen has been has been with NCATS for 14 years.

Carleen manages and runs all of the automated screening platforms for their Research Services Core (RSC). RSC's multi-disciplinary research technologies enables the ongoing operation of all of NCATS’ research activities. These automated platforms perform a wide variety of experiment types ranging from biochemical, cell based, RNAi and other existing and novel assay technologies. The advanced instrumentation, laboratory techniques, protocols and methods necessary to keep NCATS at the leading edge of scientific research require staying up to date with new and emerging technologies, and in constant communication with all researchers. This comes naturally to Carleen and no detail is too small for her to overlook.

Raphael Franzini, Ph.D.

University of Utah

Raphael Franzini received his M.Sc. in chemistry from EPFL and his Ph.D. in organic chemistry from Stanford University under the guidance of Prof.Eric Kool. In 2012, he joined the group of Prof. Dario Neri at ETHZ as a VPFW-ETH postdoctoral fellow. Since 2015, he has been an Assistant Professor in the Department of Medicinal Chemistry at the University of Utah. His research interests involve the development of bioorthogonal chemistry for applications in chemical biology and drug delivery and the advancement of DNA-encoded chemical libraries for the discovery of drug hit compounds and chemical probes.

Michael Kossenjans, Ph.D.

Principal Scientist


Michael Kossenjans is a Principal Scientist within the Discovery Sciences unit at AstraZeneca Gothenburg, Sweden. He is currently leading a group of scientists focussing on the implementation of automation in early phase drug discovery.

Michael has been working with AstraZeneca since 2004. Before joining the Discovery Sciences unit in 2012 he occupied various roles in the Medicinal Chemistry department of the cardiovascular, renal and metabolic disease research area. Michael has been key in the discovery of a novel treatment of diabetic nephropathy and played a significant role in the build-up of AstraZeneca’s small molecule screening collection, among others through innovative compound exchange programs with peers.

Michael holds a PhD in organic chemistry and performed research in academic labs in Germany, Spain and the US before starting his industrial career at Sanofi in 2000.

David Lancia

FORMA Therapeutics

Jarrod Walsh


Jarrod graduated in 2000 from the University of Liverpool with a degree in Genetics and joined the High Throughput Screening (HTS) department of AstraZeneca in 2001. His current role is Associate Principal Scientist within the Hit Discovery department that forms part of AstraZeneca’s Discovery Sciences function. During his 18 years in company Jarrod has worked extensively with screening applications, assay development activities and biophysical technologies. Since 2012 Jarrod has focused on devising approaches to identify and eliminate compounds that function via undesirable mechanisms of action to improve the quality of hit to lead compounds. His work interests include the application of biophysical techniques and development of new technologies to improve drug discovery screening and hit identification activities.

Kelly Frazer

University of California, San Diego

Dr. Frazer is an internationally renowned leader in the field of genome biology. She is the director of UC San Diego Institute for Genomic Medicine and is a professor and founding chief of the Division of Genome Information Sciences in the Department of Pediatrics at UC San Diego. Over the past seven years, Dr. Frazer’s lab has systematically derived and characterized a unique collection of iPSC lines from 222 individuals – referred to as iPSCORE (iPSC Collection for Omic Research). The iPSCORE resource is currently being used to conduct genotype – molecular phenotype correlations in both pluripotent stem cells and iPSC-derived cell types. We recently established that iPSC-derived cardiovascular progenitor cells (CVPCs) are fetal-like, and are leveraging this model system to identify and characterize developmental genetic factors which affect cardiovascular disease later in life by acting in specific fetal cardiac cell types.

Witold Postek

Institute of Physical Chemistry of the Polish Academy of Sciences

Witold Postek graduated from Warsaw University of Life Sciences in 2012 as BSc and in 2014 as Msc in Biotechnology. Later he joined the group of Piotr Garstecki at the Institute of Physical Chemistry of the Polish Academy of Sciences, where he has been working on microfluidic droplet-based systems for automation and miniaturization of biochemical and microbiological assays. Witold had worked in a number of labs, including those in Wageningen UR and TU Delft in NL, EPFL in CH, TU Dresden in DE, University of Cambridge in UK.

Evangelos Kiskinis, Ph.D.

Assistant Professor of Neurology and Physiology

Northwestern University - Feinberg School of Medicine

Evangelos Kiskinis is an Assistant Professor of Neurology and Physiology at the Northwestern University Feinberg School of Medicine. He earned a degree in Molecular Biology from the University of Surrey, a PhD from Imperial College London and trained as a postdoctoral fellow at Harvard University. His laboratory harnesses the power of human pluripotent stem cells to study neuronal development as well as to understand how neuronal function is impaired as a result of injury or disease. The overarching goal of his research is to identify points of targeted and effective therapeutic intervention for epilepsy and ALS. At Northwestern Evangelos also serves as the Scientific Director of the Stem Cell Core Facility and the Co-Director of the Stem Cell and Regenerative Biology Initiative.

Kevin Eggan, Ph.D.

Professor in the Department of Stem Cell and Regenerative Biology

Harvard University

Kevin Eggan is currently a Professor in the Department of Stem Cell and Regenerative Biology at Harvard University, a principal investigator at the Harvard Stem Cell Institute and an Institute member at the Broad Institute’s Stanley Center for Psychiatric Research. As a young investigator in the burgeoning field of stem cell biology, Dr. Eggan garnered international recognition for his seminal work and a number of high profile awards for his creativity and productivity, including the MacArthur Foundation “Genius Grant” in 2006. In 2009, he was selected as one of 50 Howard Hughes Medical Institute (HHMI) Early Career Scientists who received six years of dedicated support to conduct transformative research. His current research applies stem cell technologies to study the mechanisms through which genetic variants confer risk for psychiatric and neurodegenerative diseases.

In 1996, Dr. Eggan received his bachelor’s degree in microbiology from the University of Illinois. He completed a two-year predoctoral fellowship at the National Institutes of Health, before pursuing his doctoral studies in biology at The Massachusetts Institute of Technology. There he actively pursued projects focused on cloning, stem cells, and reprogramming after nuclear transfer under the guidance of genetics pioneer Rudolf Jaenisch. After completing his doctoral studies in 2002, Eggan stayed in Jaenisch’s lab for one year of postdoctoral collaboration with Richard Axel from Columbia University. In 2003, he moved to Harvard University as an independent Junior Fellow and then became an Assistant Professor of Molecular and Cellular Biology in 2005. In 2013 he was promoted to the Rank of Full Professor in the Department of Stem Cell and Regenerative Biology, the first Harvard academic department to span the Medical School and School of Arts and Sciences. 

Kamran Honarnejad

​Group Leader for High-Throughput Drug and Target Discovery

Fraunhofer ITEM

Group Leader for High-Throughput Drug and Target Discovery at Fraunhofer ITEM, KCDC co-chair and NPA judge panel member at SLAS

Anna Greka, MD, Ph.D.


Anna Greka is a physician-scientist leading the translation of scientific discoveries from the laboratory to clinical trials. She is an Associate Professor at Harvard Medical School (HMS); an Associate Physician in the Renal Division in the Department of Medicine at Brigham and Women’s Hospital (BWH); and the founding director of Kidney-NExT, a Center for Kidney Disease and Novel Experimental Therapeutics at BWH. Dr. Greka is also an Institute Member of the Broad Institute of MIT and Harvard, where she directs the institute’s Kidney Disease Initiative (KDI) and the ion channel therapeutics interest group (CHAnnel Therapeutics, CHArT).

Jackie Hunter, Ph.D.

Board Director


Jackie Hunter is currently a Board Director of BenevolentAI as well as CE of Clinical and Strategic Partnerships. BenevolentAI uses AI to augment the research capabilities of drug scientists, radically changing the way R&D is done. She has over thirty years of experience in the bioscience research sector, working across academia and industry including leading neurology and gastrointestinal drug discovery and early clinical development for GlaxoSmithKline. She has also served on numerous academic, industry and government Boards and panels in both Europe and America e.g. in the establishment of the Innovative Medicines Initiative (IMI), a 2B euro public-private partnership between the pharmaceutical industry and the European Commission and she was one of the first Board members of IMI.

For 3 years she was the CEO of the BBSRC which was the major funder of bioscience research and its translation. In that capacity she led on developing an Equality & Diversity Strategy for all the UK Research Councils (now UKRI). She is an Honorary Professor at St George's Hospital Medical School and Imperial College, was awarded a CBE in the Queen's Birthday Honours list for Services to the Pharmaceutical Industry and was recently recognized by Forbes Magazine as one of the top 20 Women Advancing AI Research. She is passionate about diversity in all its forms!

Leroy Cronin

Regius Professor of Chemistry

University of Glasgow

Leroy (Lee) Cronin FRSE is the Regius Professor of Chemistry in Glasgow. Since the age of 9 Lee has wanted to explore chemistry using electronics to control matter, understand the origin of life, and generally confuse people with ideas that may or may not make sense one day. He strives to use his imagination to create new ideas that might tell us something about the universe. His research has four main aims 1) the construction of an artificial life form / work out how inorganic chemistry transitioned to biology / searching for new life forms; 2) the digitization of chemistry; and 3) the use of artificial intelligence in chemistry including the construction of ‘wet’ chemical computers; 4) The exploration of complexity and information in chemistry. Lee does not like hierarchy but likes organisation and well-defined actions.Nothing is impossible until it is tried. See

Jimmy Wu

Translational Research Institute for Space Health

Jimmy Wu is the Senior Biomedical Engineer at the Translational Research Institute for Space Health (TRISH). His role is to facilitate delivery of project deliverables to TRISH and NASA. Previously, Jimmy worked at NASA Johnson Space Center for fourteen years providing engineering, integration, operations, research and development, information technology, and project management support to projects addressing human health and performance during space flight missions.

Rekha Hemrajani


Rekha Hemrajani is an experienced strategic leader helping biotech companies with corporate strategy and corporate finance, as well as building product pipelines through corporate development (both buy side and sell side). She is a strong team builder with extensive senior management experience within public and private biotechnology and pharma companies, investment funds and bulge bracket investment banks.

Recently, as CFO & COO of Arcus Biosciences, a publicly-traded clinical stage company focused on developing cancer therapeutics, Rekha led finance, investor relations, corporate communications, business and corporate development, strategic planning and human resources. Previously as COO of FLX Bio, a venture funded immune-oncology company, Rekha was responsible for corporate strategy, corporate development, finance, investor & public relations, corporate communications, strategic marketing and G&A operations including legal, facilities and IT. Following the spin-off of FLX from its predecessor company, she has helped FLX restructure and rebuild the product portfolio, grow the company from ~35 to ~65 employees and lead the company through multiple rounds of financing raising > $140 M in private capital.

Previously, in various roles, Rekha has helped companies prepare for and execute IPOs, corporate communication and investor relations strategies, in- and out-licensing of products, mergers & acquisitions and portfolio prioritization. Rekha has experience in the therapeutic areas of oncology, immunology, metabolic diseases as well as endocrinology.

Rekha holds an MBA from the Kellogg School of Management at Northwestern University and a Bachelor of Science from University of Michigan. She serves on the Board of Directors of Hemmo Pharmaceuticals, Pvt Ltd, Mumbai, India, on the Investment Advisory Board of University of Michigan’s Monroe-Brown Seed Fund in Ann Arbor, Michigan as well as on the Board of Regents of Junipero Serra High School in San Mateo, California.

Elizabeth Iorns, Ph.D.

Founder & CEO

Science Exchange

Dr. Elizabeth Iorns is the Founder & CEO of Science Exchange, the Co-Director of the Reproducibility Initiative, and is a part-time partner at Y Combinator. Elizabeth has a Ph.D. in Cancer Biology from the Institute of Cancer Research (UK), and before starting Science Exchange in 2011 was an Assistant Professor at the University of Miami (where she remains an Adjunct Professor). Elizabeth has received a range of honors and recognition, including the Kauffman Foundation Emerging Entrepreneur Award, one of Nature Magazine’s ‘Ten People Who Mattered’, and one of WIRED’s '50 Women Who Are Changing The World'. Elizabeth is focused on the development of innovative models to promote the quality and efficiency of scientific research.

Phyllis Whiteley, PhD.

Wildcat Venture Partners

Phyllis Whiteley, PhD., is a Venture Capitalist and a Life Sciences consultant. She is currently a Venture Partner with Wildcat Venture Partners where she provides hands-on leadership in building, investing, and creating companies in the digital health space. She is also a Venture Partner with Mohr Davidow Ventures where she focused on diagnostics, life science tools, digital health, and personalized medicine including Verinata Health, acquired by Illumina (NASDAQ: ILMN). Phyllis’ career is grounded on her passion for personalized medicine, global healthcare, and transformative life science innovations. She has founded and built multiple companies in the United States and abroad. She is on the Board of Carrum Health and Balance Therapeutics.

Her executive expertise includes strategic planning, R&D, start-ups, M&A, restructuring, and building executive teams. Prior to joining WVP and MDV, Phyllis was an EIR with 5am Ventures where she co-founded Anaphore (now Bird Rock Bio). Prior to VC, Phyllis was an Officer and SVP, Business Development, Licensing, and Alliance Management at Perlegen Sciences, Inc. Before Perlegen, she served as VP, Strategic Portfolio Management for Roche. Over her ten-year career with Roche she held various positions in Business Development based in Basel, Switzerland coupled with R&D leadership positions in the US. Phyllis also held a Senior Research Immunology role with Merck.
Phyllis is dedicated to helping entrepreneurs address global problems. She sits on the advisory boards of UCSF Leadership Council for Global Health, University of Michigan School of Nursing Board for Science and Innovation, is a Global Social Benefits Mentor at Santa Clara University and chairs the advisory board for the University of Texas Horizon Fund. Phyllis also serves on the board of Alydia Health, a medical device company treating Post-Partum Hemorrhage.

Phyllis holds a BA in Chemistry and Ph.D. in Pharmacology from Washington University, St. Louis, Missouri.

Nancy Allbritton, M.D., Ph.D.

Frank & Julie Jungers Dean of Engineering and Professor of Bioengineering

University of Washington

Nancy L. Allbritton is the Frank & Julie Jungers Dean of Engineering and Professor of Bioengineering at the University of Washington in Seattle (2019- current). From 2009-2019, she was the Kenan Professor of Chemistry and Biomedical Engineering and Chair of the Joint Department of Biomedical Engineering at the University of North Carolina at Chapel Hill (UNC) and North Carolina State University (NC State). Her research focuses on the development of novel technologies for applications in single-cell analysis, micro-arrays and fluidics, and organ-on-chip and has resulted in over 180 full-length journal publications and patents and led to 15 commercial products. Four companies have been formed based on her research discoveries: Protein Simple (acquired by Bio-Techne in 2014), Intellego, Cell Microsystems (, and Altis Biosystems ( Dr. Allbritton is a Fellow of the American Association for the Advancement of Science, the American Institute for Medical & Biological Engineering, and the National Academy of Inventors. She obtained her B.S. in physics from Louisiana State University, M.D. from Johns Hopkins University, and Ph.D. in Medical Physics/Medical Engineering from the Massachusetts Institute of Technology, with a postdoctoral fellowship at Stanford University.

June Lee, M.D.

Myokardia, Inc.

June Lee, M.D., has significant experience leading early clinical development programs and is also a recognized leader in academic translational research. Dr. Lee is an adjunct professor at the University of California, San Francisco (UCSF) School of Medicine, where she formerly served as Director of Translational Research and Director of Catalyst Program which is an internal accelerator at UCSF for early stages technologies in therapeutics, devices, diagnostics, and digital health. A key focus of Dr. Lee’s work was identifying the most compelling discovery research and enabling and supporting its commercialization. Previously, she worked in early clinical development in cardiovascular, metabolism, respiratory, and infectious diseases at Genentech. Dr. Lee serves as chair of the Board of Directors for the Council of Korean Americans. She is also a member of the Translational Research Advisory Council of the MOGAM Research Institute, and the MTRAC for Life Sciences Innovation Hub Therapeutic/Diagnostic Oversight Committee, a unit associated with The University of Michigan Medical School.

Dr. Lee completed her undergraduate work in chemistry at the Johns Hopkins University, earned her M.D. degree at the School of Medicine at University of California, Davis, and her clinical training in internal medicine and pulmonary/critical care and UCLA and UCSF.

Raphael Werding

Global Product Manager for Genomic Services


Raphael Werding is the Global Product Manager for Genomic Services at QIAGEN. He is in charge of initiating master collaboration agreements with leading pharmaceutical and biotech companies to create Sample to Insight solutions for Precision Medicine. Each patient’s unique genomic and epigenomic characteristics are used to guide diagnostic assay development and eventually clinical decision-making for treatment. Raphael’s focus is to support customers on their journey from biomarker discovery over translational research up to clinical diagnostic assay development.

Anne Carpenter

Institute Scientist

Broad Institute of Harvard and MI

Dr. Anne Carpenter is an Institute Scientist at the Broad Institute of Harvard and MIT. Her research group develops algorithms and strategies for large-scale experiments involving images. The team’s open-source CellProfiler software is used by thousands of biologists worldwide ( Carpenter is a pioneer in image-based profiling, the extraction of rich, unbiased information from images for a number of important applications in drug discovery and functional genomics.

Carpenter focused on high-throughput image analysis during her postdoctoral fellowship at the Whitehead Institute for Biomedical Research and MIT’s CSAIL (Computer Sciences/Artificial Intelligence Laboratory). Her PhD is in cell biology from the University of Illinois, Urbana-Champaign. Carpenter has been named an NSF CAREER awardee, an NIH MIRA awardee, a Massachusetts Academy of Sciences fellow (its youngest at the time), a Genome Technology “Rising Young Investigator”, and is listed in Deep Knowledge Analytics’ top-100 AI Leaders in Drug Discovery and Advanced Healthcare.

Yilian Wang

UCLA Bioengineering

Jonathan Hull, Ph.D.

Vice President


Per Setterberg



Sebastian Metz, Ph.D.


Byonoy GmbH

Hugo Sinha, MASc



Lowry Curley, Ph.D.


AxoSim Inc.

Patrick Walsh, M.S.


Anatomi Corp.

Tei Newman-Lehman


DeepDiveBio, Inc.

Michele Zagnoni, Ph.D.

Chief Scientific Officer


Mark Harfouche, Ph.D.


Ramona Optics, Inc.

Dan Close, Ph.D.

490 BioTech, Inc.

Dan Close obtained his doctorate in 2011 through the development of a synthetic luciferase-based imaging technology that enabled autonomous bioluminescent production in human cells. Following Dr. Close’s development of this technology, it was successfully commercialized by 490 BioTech, Inc. Upon completion of his graduate work, Dr. Close took a position as a Postdoctoral Research Associate in the Joint Institute for Biological Sciences, a partnership between The University of Tennessee and Oak Ridge National Laboratory. In 2012 Dr. Close was selected as a Eugene P. Wigner Fellow by the Department of Energy’s Oak Ridge National Laboratory in Oak Ridge, Tennessee. Following the completion of his Eugene P. Wigner Fellowship appointment, Dr. Close continued his work as a Research Staff Scientist in the laboratory’s Biosciences Division. In 2018, Dr. Close become the chief scientific officer of 490 BioTech, the company founded on the autobioluminescent technology he developed during his graduate work.

Paul Hung, Ph.D.



Paul Hung has 11 years of experience developing life science research tools using microfluidic technology. After receiving his PhD from UC Berkeley in 2005, he has successfully grown CellASIC Corporation, which he co-founded in 2006, to self-sufficiency with the commercialization of the ONIX live cell imaging platform, and sold to MillporeSigma (then EMD Millipore) in 2012. After the acquisition, he worked as a senior R&D manager to gain more knowledge in systematic product development in a large corporate. He founded COMBiNATi in 2016 to continue driving the vision of disrupting the life science industry with microfluidic technology, one consumable at a time.

Karen Wu, Ph.D.

Co-Founder and the President

Lucerna, Inc

Dr. Karen Wu is a Co-Founder and the President at Lucerna. She has over 20 years of experience in RNA technology and has launched several research tool products into the life science reagents market.

Arne Vandenbroucke, Ph.D.

Sr. Automation Engineer

Synthace Ltd

Akwasi Apori, Ph.D.


Correlia Biosystems


Advances in Bioanalytics and Biomarkers
Can lattice theory help find a cure for paralysis?
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Open to view video. With the advent of the Human Genome Project came the industrialisation of the drug discovery process and a belief that combinatorial chemistry and high throughput screening would deliver molecules with increased potency, against a single target of interest. Yet the attrition rate is still at the 10% mark and there remain many human diseases for which no effective treatment exists. As Swinney et al showed [1], there is compelling evidence that first-in-class drugs are more likely to be found by assays that measure a clinically meaningful phenotype in a physiologically relevant system rather than a single target-based screening approach in an artificial setting. One perceived issue with phenotypic screening is the lack of mechanistic knowledge. Whilst understanding mechanism of action (MOA) is not a prerequisite for FDA approval, it can guide a medicinal chemistry effort, predict potential toxicities and help define patient populations for clinical trials and ultimately the market place. There are a number of in vitro approaches to target deconvolution. However, these tend to be of lower throughput and better placed later in a screening cascade. So there is a real need for in silico-based approaches that can be deployed early on in a drug discovery programme to identify potential MOAs. Using publicly available data on the Published Kinase Inhibitor Set (PKIS) [2,3], we describe the application of Formal Concept Analysis (FCA), an association mining technique with roots in set theory, to the problem of deconvoluting a phenotypic screen. We describe each compound in the PKIS by the set of kinases it inhibits. We then construct a Galois Lattice, whose nodes correspond to a set of compounds inhibiting a common set of kinases and where two nodes are connected if the compound set of the child node is a subset of the compound set of the parent node. Lattice nodes enriched with compounds that promote neurite outgrowth in rat inform which kinases should be targeted when seeking small molecules that encourage CNS axon repair following injury. The targets we identify using this push-button approach, that can be placed in the hands of the bench scientist, are in line with those identified in [3] and confirmed in siRNA studies.1. Swinney DC, Anthony J: How were new medicines discovered? Nat Rev Drug Discov 2011, 10(7):507-19. 2. Drewry DH, Willson TM, Zuercher WJ: Seeding collaborations to advance kinase science with the GSK Published Kinase Inhibitor Set (PKIS). Curr Top Med Chem 2014, 14(3):340-2. 3. Al-Ali H, Lee DH, Danzi MC, Nassif H, Gautam P, Wennerberg K, Zuercher WJ, Drewry DH, Lee JK, Lemmon VP, Bixby JL: Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth. ACS Chem Biol 2015, 10(8): 1939-51.
Combining large-scale in vitro pharmacological profiling and human cell-based phenotypic profiling identifies novel mechanisms of cardiovascular toxicity
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Open to view video. We have previously described a phenotypic signature associated with cardiovascular toxicity relevant to vascular calcification and atherosclerosis from a human primary cell-based coronary artery smooth muscle cell model of vascular inflammation (BioMAP® CASM3C system). The key biomarker activity in this signature is increased cell surface levels of serum amyloid A (SAA) protein. Analysis of a large reference database (BioMAP Phenotypic Profile Reference Database) of >3400 drugs and chemicals tested in this assay identified 147 compounds exhibiting the signature at one or more concentrations. For some of these compounds, specific mechanisms could be implicated and include MEK inhibition, HDAC inhibition, glucocorticoid (GR)/mineralocorticoid (MR) receptor agonism, IL-6 pathway agonism, as well as modulation of mitochondrial NAD+/NADH ratios. To further characterize the mechanisms underlying this toxicity-associated signature, we took advantage of a second large reference database (BioPrint® Pharmacology Profile Database) comprised of in vitropharmacological profiles of drugs and chemicals screened against a broad range of targets (~148 receptors, ion channels, enzymes and transporters). We evaluated the in vitropharmacology profiles for compounds exhibiting the phenotypic signature associated with cardiovascular toxicity(data was available for 85 of 147 compounds). Target activities (in binding assays) enriched among the phenotypic actives include glucocorticoid receptor (GR), androgen receptor (AR), Chloride channel (Cl-channel), ML2 (MT3), (5-Hydroxytryptamine receptor 2B (5-HT2B), peripheral benzodiazepine receptor (BZD), MT1 and ML1. The identification of ML2 (MT3), also known as NAD(P)H quinone dehydrogenase 2 or NQO2, and MT1 receptors is interesting as these are receptors for melatonin. Melatonin has been reported to reduce blood pressure and also to reduce NAD+ levels through effects on NAMPT (nicotinamide phosphoribosyltransferase). Recent studies have suggested that NAMPT may play a role in the pathogenesis of atherosclerosis in experimental mouse models. In humans, serum concentrations of NAMPT have been shown to be an independent predictor of symptomatic carotid stenosis in patients undergoing carotid endarterectomy. These results show how the combined analysis of phenotypic and pharmacology profiling data can confirm and extend our understanding of potential mechanisms associated with risk of cardiovascular toxicity. Pairing of target-based and phenotypic assays is an efficient and effective means to improve confidence in non-animal based screening of new drug leads for potential liabilities.
Connecting high-throughput screening and clinical pharmacology using stable isotope tracer kinetics and mass spectrometry: Moving from enzyme activity to in vivo pathway flux with the same assay.
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Open to view video. Stable isotope labeled substrates can be of broad use in cases where target-based high-throughput screening aims to identify compounds that can modulate enzyme activity. For example, depending on the source of a given enzyme target, the presence of endogenous substrates or products can limit one’s ability to follow substrate®product conversions; utilization of a labeled substrate(s) can help overcome background contamination. These same isotope flux assays can then be used to follow the progression of hits in later stages of development, including cell-based assays and in vivo studies. Although stable isotope tracer kinetics, coupled with mass spectrometry-based detection, can therefore connect all phases of drug discovery there are caveats that should be recognized to ensure reliable data interpretations. Our presentation will highlight key areas where the logic surrounding tracer kinetics diverges as the application of flux analyses moves across different stages of drug discovery. We will consider a case study that is focused on lipid biology, i.e. modulating the level of glycosylated ceramides. We will first outline how labeled substrates can be used to circumvent problems that arise in early screening. We will then outline how tracers can be used to progress molecules into later phases, including in vivo studies. Although one can use virtually the same back-end mass spectrometry assay to measure the formation of labeled products, several parameters change with regards to dosing the labeled substrates. For example, when measuring enzyme activity in early biochemical screening one needs to only measure the labeled product. In contrast, in vivo studies must contend with the fact that substantial amounts of “cold” (endogenous) substrate can exist, in addition, it may not be possible to maintain a steady-state exposure to the labeled substrate. Consequently, strategies need to account for temporal tracer dilution, most of which may not be immediately obvious and/or difficult to correct. In summary, the ability to measure stable isotope flux from precursors to products can provide a bridge that spans the entire spectrum of drug discovery and development. However, changes in the generation of a labeled product do not immediately reflect changes in the metabolic activity of a given target enzyme or pathway, it is possible to observe differences in the abundance of a labeled product which reflect an unexpected modulation of precursor metabolism. Although the example described here is focused on a targeted screen, the logic has immediate implications with regards to phenotypic screening; attention to a few details can influence essential decision points.
Novel Approaches to Quantitative Metabolomics
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Open to view video. The major challenge in the field of metabolomics is to accurately identify and quantify hundreds of metabolites in a single run. Recently variable window SWATH acquisition has shown to identify a higher number of metabolites compared to the traditional Data Dependent Acquisition (DDA) approach, thus enabling broader metabolome coverage. Here we have implemented a variable window SWATH acquisition method for enhanced quantitation of selected metabolites using MS/MS, with reduced matrix interferences and improved signal-to-noise. Using MS/MS fragments for metabolite quantitation provides better selectivity, and ultimately increased sensitivity. Variable window SWATH Acquisition provided quality quantitative data for metabolites in complex matrix. Due to many coeluting metabolites in complex matrix, using only the MS spectrum and retention time is often not sufficient for metabolite identification. MS/MS information is necessary to obtain further structural knowledge about the metabolite. Complete full scan MS and MS/MS data is available in every SWATH file for improved ID. In addition, MS/MS quantitation of metabolites often leads to lower detection limits due to significantly improved signal to noise ratios vs MS data. Measuring the whole MS/MS spectrum allows selection of the best fragments for metabolite quantitation. SCIEX OS software combines comprehensive qualitative and quantitative data analysis, making data processing easier and more efficient. SWATH Acquisition on all detectable metabolites is successfully utilized for identification, and accurate MS/MS level quantification of metabolites in urine. .
NMR based Metabolomics in Drug Research - Cancer Metabolism
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Open to view video. Metabolism and in particular central energy metabolism have evolved as promising drug targets. A cutting edge technology which has become widely used for studying oxygen consumption and extracellular acidification is the Seahorse™ analyzer. While this technology allows for rapid label free screening it does not provide further details on the involved metabolites and pathways. The quantitative analysis of these pathways, mainly involving glycolysis, the tricarboxylic acid cycle (TCA) and adjacent pathways is intrinsically very challenging. Several commercial solutions have evolved over the years predominantly using mass spectrometry and a series of labeled internal standards. However, many of these approaches suffer from long analytical procedures and the need for special internal standards or kits.As an alternative we will discuss our NMR based workflow allowing the quantitative analysis of several important pathways as for example glycolysis, TCA cycle, OxPhos, one-carbon metabolism and others. Our NMR based workflow allows for the rapid and quantitative analysis of >80 metabolites without the need for specialized kits or internal standards. The workflow can partially be operated in an automated fashion using a KNIME workflow such as KIMBLE. Moreover, flux analysis using 13C labeled materials can easily be adapted resulting in kinetic information. As an example for the usefulness of this workflow we will discuss the discovery of choline kinase α (CHKA) as a possible target for the prevention of epithelial to mesenchymal transition (EMT) an important metastatic process. Using NMR based analysis of metabolic changes during TGFβ induced EMT we could observe significant alterations in choline phosphorylation. Following up on these results by using experimental inhibitors we could identify CHKA as crucial enzyme for the EMT phenotype.
Combining Arrays and Mass Spectrometry for High Throughput Experiments in Chemistry and Biology
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Open to view video. This talk will describe an approach for using mass spectrometry and arrays of self-assembled monolayers to perform quantitative experiments in high throughput. The arrays are prepared by immobilizing small molecules, proteins, peptides and carbohydrates to self-assembled monolayers of alkanethiolates on gold. This arrays are then treated with reactants—either chemical reagents or enzymes—and then analyzed using the SAMDI technique to identify the masses of substituted alkanethiolates in the monolayer and therefore a broad range of reactivities and post-translational modifications—including kinase, protease, methyltransferase and carbohydrate-directed modifications—and for discovering chemical reactions. This talk will describe applications to high throughput experiments, including the discovery of reactions, the use of carbohydrate arrays to discover novel enzymes, the preparation of peptide arrays to profile the enzyme activities in cell lysates and high-throughput screening to discover novel reactions and small molecular modulators. These examples illustrate the broad capability of the SAMDI method to profile and discover molecular activities in the molecular sciences.
CETSA® beyond soluble targets: a broad application to multi-pass transmembrane proteins
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Open to view video. Demonstration of target binding is a key requirement for understanding the mode of action of new therapeutics. The cellular thermal shift assay (CETSA®) has been introduced as a powerful label-free method to assess target engagement in physiological environments. Here, we present the application of live-cell CETSA® to different classes of integral multi-pass transmembrane proteins using three case studies: the first showing a large and robust stabilization of the outer mitochondrial five-pass transmembrane protein TSPO, the second being a modest stabilization of SERCA2, and the last describing an atypical compound-driven stabilization of the GPCR PAR2. Our data demonstrated that using modified protocols with detergent extraction after the heating step, CETSA® can reliably be applied to several membrane proteins of different complexity. By showing examples with distinct CETSA® behaviors, we aim to provide the scientific community with an overview of different scenarios to expect during CETSA® experiments, especially for challenging, membrane bound targets.
Assay Development and Screening
Functional Proteome Array Screening Strategies for Biomarker Discovery
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Open to view video. Self-assembling protein microarrays can be used to study protein-protein interactions, protein-drug interactions, search for enzyme substrates, and as tools to search for disease biomarkers. In particular, recent experiments have focused on using these protein microarrays to search for antibody responses in patients with cancer, autoimmune and infectious diseases. This approach has led to the first CLIA-certified blood test for the early detection of breast cancer, Videssa™. Recent work has focused on using the arrays to explore post translational modification of proteins and its role in producing neoantigens in disease.
NanoClick Assay: A high throughput, target-agnostic cell permeability assay that combines NanoBRET technology with intracellular Click chemistry
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Open to view video. Macrocyclic peptides open new opportunities to target intracellular protein-protein interactions (PPIs) that are often considered non-druggable by traditional small molecules. Specifically, peptides have the potential to bind to highly expansive binding surfaces (orthosteric blocking) of such PPIs and/or other unique allosteric binding sites. However, their clinical development may be limited by their ability to efficiently penetrate into cells to modulate their cognate PPI targets. The ability to have a predictive, high-throughput assay to assess cell permeability is a critical tool to support peptide drug discovery programs. We developed a high throughput, quantitative, target-agnostic cell permeability assay that essentially measures the cumulative cytosolic exposure of a peptide in a concentration-dependent manner. The assay has been named NanoClick as it combines in-cell Click chemistry and monitoring of a NanoBRET signal in cells. The assay is based on cellular expression of the NanoLuc-HaloTag system and relies on the Click reaction of azide-containing peptides with DiBac-chloroalkane (CA) anchored to the HaloTag. Subsequent introduction of an azido-dye followed by the NanoLuc substrate allows the detection of a BRET signal that is reduced by the presence of Click-reactive peptides in the cytosol. The readout can be expressed as a permeability ratio of EC50s when compared to the response of a low permeability control. We validated the assay using known cell penetrating peptides and were further able to demonstrate correlations to cellular activity using a p53/MDM2 model system. The assay has been applied across multiple programs and has been used to guide and establish structure-permeability relationships in the optimization of macrocyclic peptides for cellular potency across intracellular PPI target programs.
Building toolkits for the orphan kinome
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Open to view video. Protein phosphorylation by kinases is a major mechanism of cell signaling, and is involved in almost all aspects of cell biology. Kinase dysregulation is a key factor in diseases like cancer, and kinases are one of the major drug targets in oncology. However, despite decades of research and billions of dollars in drug discovery efforts on kinases, relatively few are well characterized. The majority of the ~90 tyrosine kinases are considered “orphans,” for which few to no substrates, and thus few details about biological pathways and roles, are known. Without substrates to use as activity probes, inhibitors for use as tool compounds or potential therapeutics cannot be discovered. We have developed a strategy to incorporate empirically-determined substrate profiling data into our KINATEST-ID bioinformatics pipeline to efficiently tackle the orphan kinase problem, determine substrate preferences and design novel substrate tools. Protease-digested peptides from cell lysates are stripped of pre-existing phosphates, then re-phosphorylated with a kinase of interest. The resulting phosphopeptides are enriched and analyzed using mass spectrometry. Phosphopeptide sequences are extracted from the peptide ID list and funneled through the KINATEST-ID pipeline using a set of scripts implemented in the open-source user interface GalaxyP, to define substrate sequence preferences and propose candidate optimal substrate peptides. Those are then synthesized and tested for phosphorylation efficiency by the target kinase. Using this approach, we have characterized substrate preferences for several understudied kinases for which few validated substrates were known, including FLT3 and two clinically relevant mutants, and BTK. Current and future efforts are to broaden the scope of kinases characterized using this streamlined phosphoproteomics/bioinformatics pipeline and proceed with systematically defining substrate information and developing novel tools for other orphaned kinases in the kinome.
Hybridization Chain Reaction for single-cell visualization of RNA in high content imaging assays
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Open to view video. The precise regulation of gene expression programs is responsible for the establishment and maintenance of cell, tissue and organ identity, for cellular responses to signaling cues and injuries, and, when disrupted or rewired, for diseases such as cancer and inflammation. Measuring gene expression in high-throughput assays often requires reporter cell line engineering, or using antibodies against endogenous protein markers, which involves a lengthy development process, and can also suffer from batch-to-batch variation. On the other hand, single molecule RNA Fluorescence In Situ Hybridization (smRNA-FISH) detects endogenous transcripts, and is based on DNA oligonucleotide probes that can be rapidly designed in silico, chemically synthetized, tested, and scaled up. For these reasons, smRNA-FISH has the potential to be a useful additional tool for High-Content Imaging (HCI) in chemical or functional genetics screens for the identification of gene expression regulatory pathways. However, visualization of RNA at the single cell level via smRNA-FISH has not been optimized for HCI assays. To address these limitations, we adapted the single-step, enzyme free RNA Hybridization Chain Reaction (RNA HCR) to a 384-well format using an HCI platform. First, we used RNA HCR probes against IFIT3, an interferon stimulated gene (ISG), to demonstrate that high-throughput RNA HCR can quantitatively measure gene expression changes at the single cell level in a 384-well format. As a proof of principle, we performed a focused RNAi screen against 521 human genes involved in epigenetics regulation to identify novel factors mediating the transcriptional response to interferon-γ. The results of this primary screen suggest that multiple components of the MOF acetylase complex are involved in the upregulation of IFIT3 upon interferon stimulation. Finally, we applied high-throughput RNA HCR in other HCI assays to measure expression levels of specific mRNA splicing isoforms of the FGFR2 gene, to monitor the effect of steroid treatment on the expression of inflammation regulators in primary human monocytes, and to determine the effect of steroid treatment on a variety of GR-responsive genes in mouse cells. Altogether, these results indicate that RNA HCR can be miniaturized in 384-well assays to semi-quantitatively detect several endogenous RNA species via HCI in physiologically relevant systems, at the single-cell level, and in a medium- to high-throughput format. In the future, we expect that high-throughput RNA HCR will be useful for the discovery and validation of diverse targets regulating gene expression.
Mass Spectrometric Assay of METTL3/METTL14 Methyltransferase Activity
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Open to view video. A variety of covalent modifications of RNA have been identified and demonstrated to affect RNA processing, stability and translation. Methylation of adenosine at the N6 position (m6A) in mRNA is currently the most well-studied RNA modification and is catalyzed by the RNA methyltransferase complex METTL3/METTL14. Once generated, m6A can modulate mRNA splicing, export, localization, degradation and translation. Although potent and selective inhibitors exist for several members of the Type I S-adenosylmethionine (SAM)-dependent methyltransferase family, no inhibitors have been reported for METTL3/METTL14 to date. To facilitate drug discovery efforts, a sensitive and robust mass spectrometry-based assay for METTL3/METTL14 using self-assembled monolayer desorption/ionization (SAMDI) technology has been developed. The assay uses an 11-nucleotide single-stranded RNA compared to a previously reported 27-nucleotide substrate. IC50 values of mechanism-based inhibitors S-adenosylhomocysteine (SAH) and sinefungin (SFG) are comparable between the SAMDI and radiometric assays that use the same substrate. This work demonstrates the SAMDI technology is amenable to RNA substrates and can be used for high-throughput screening and compound characterization for RNA modifying enzymes.
Identification and biophysical characterization of STING modulators
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Open to view video. The second messenger cyclic dinucleotide (CDN) cGAMP is produced by the cGAS protein in response to activation by cytoplasmic dsDNA. Upon recognition of cGAMP by the stimulator of interferon genes (STING) protein, STING undergoes a substantial conformational change which leads to downstream upregulation of proinflammatory cytokines. Modulation of the cGAS/STING pathway is therefore considered a promising route for the treatment of inflammatory diseases as well as a potential partner for immune-oncology therapies. This presentation will describe the identification and optimization of compounds which were found to antagonize STING signaling as well as the identification and conformational characterization of non-CDN STING agonists.Techniques employed include surface plasmon resonance and X-ray crystallography.
Drug Combination and Gene Network Analysis in 3D models of Brain and Pancreas Cancer towards Precision Medicine
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Open to view video. Molecular pathology approaches for clinical oncological care is routinely performed on cancer patients with recurrent or metastatic disease. While these “omic” diagnostics seemingly improved prognostication and prediction, some molecular 'signatures' are not useful in clinical practice because of their inability to independently validate treatment options. By nature, associations between genomic profiles and clinical response are correlative rather than mechanistic resulting in poor prediction for needed care. Advances in our lab, in combination with our academic and industry partners, has made possible in-vitro/ex-vivo 3 dimensional (3D) models of cancer biology for use in a rapid, highly miniaturized, and cost-effective fashion that permits direct drug response profiling to be generated in a phenotypic manner that is patient specific. By integrating genomic diagnostics with drug response testing a significant breakthrough toward advancing precision medicine, using tumor biopsies, is now technologically possible and is referred to as Precision Medicine Therapeutic Profiling. Glioblastomas and cancer of the pancreas represent two of the most lethal malignancies with survival typically less than two years from diagnosis. These models of malignancy combined with genetic profiling have been tested in 3D cultures to validate the best drug, or combination of drugs, for individualized care in a time frame that is meaningful to clinical application. It is hypothesized that the comprehensive data generated will afford physicians with a powerful new insight that is actionable for patient care.
New innovation to solve unmet needs: Implementing human induced pluripotent stem cell-derived neural spheroids as a robust screening platform for phenotypic-based central nervous system drug discovery
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Open to view video. Central nervous system (CNS)-based drug discovery has been hampered by a lack of relevant, high-throughput experimental platforms. Complex, three-dimensional (3D), experimental preparations with multiple cell types better represent the native, in vivo biology, thus providing relevant material for CNS investigations. Unfortunately, these preparations traditionally have not been able to support the throughput necessary for early-stage discovery programs. The ideal preparation would provide consistent native tissue function in high throughput plates. To meet this need, we have developed 96- and 384-well assay-ready, 3D neural spheroid platforms; each spheroid is composed of cortical glutamatergic and GABA-ergic neurons co-cultured with astrocytes to provide a more complex, biologically relevant, and predictive preparation in a high throughput platform for compound screening, safety evaluation, and toxicity studies. Whole genome RNAseq profiling demonstrated neural tissue expression patterns, and high content imaging validated neuronal and astrocytic cell populations while showing highly reproducible spheroid size across both 96 and 384-well platforms. Functional neuronal activity was confirmed with MEA recordings and visualized under high-throughput conditions as robust spontaneous, synchronized calcium oscillations with consistent and reproducible baseline activity patterns across wells and plates. Functional circuitry was confirmed by challenging the system with specific ion channel and neurotransmitter receptor agonists and antagonists. To validate the capabilities of the platform for compound profiling and discovery, a library of 1622 FDA approved compounds was screened in single point at 10 μM final concentration examining Ca2+ oscillations as a functional phenotypic readout. The library included drugs covering a wide spectrum of targets such as CNS biology, oncology, cardiology, anti-inflammatory, immunology, neuropsychiatry and analgesia with DMSO as a vehicle control. Hits were identified as responses that were at least 3 standard deviations from DMSO control responses. As expected, the highest number of hits were from targets associated with neuronal signaling (serotonin, dopamine, GABA, and adrenergic receptors), neural biology, and second messengers such as cAMP. Of note was the identification of several compounds that led to increases in peak count similar to that of 4-AP, a known pro-convulsant. The results validated a robust screening platform with a vehicle control standard deviation of ~9% across all plates and a Z’ score of 0.73 across the entire screen. In conclusion, performing a high-throughput functional screening assay on our human iPSC-derived 3D neural spheroid platform demonstrated the ability to identify a wide range of hits spanning multiple target areas. This model may serve as a phenotypic and target-based platform for overcoming traditional hurdles of CNS-based drug discovery and improving outcomes in for novel CNS-targeted drug discovery and development efforts. Moreover, the model can be created from both wild type and disease individuals, providing relevant human platforms for disease-specific drug discovery.
Maximizing the Value of Cancer Drug Screening in Multicellular Tumor Spheroid Cultures – Are you Analyzing your 3D Tumor Models Appropriately?
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Open to view video. Historically, cancer drug leads are identified in high throughput screening (HTS) growth inhibition assays performed in tumor cell line panels maintained and assayed in 2 dimensional cultures. However, the overall probability for success in oncology clinical trials is a dismal 3.4%. To improve clinical development success rates for solid tumors, more physiologically relevant in vitro 3-dimensional models are being deployed in lead generation to identify better cancer drug candidates. Multicellular tumor spheroids (MCTSs) resemble avascular tumor nodules, micro-metastases, or the intervascular regions of large solid tumors with respect to morphology, volume growth kinetics, and form diverse microenvironments due to gradients of nutrient distribution and oxygen concentration. Head and neck cancers (HNC) are the 8th leading cause of cancer worldwide and in 2019 it’s projected that 53,000 people in the USA will develop oral cavity or pharynx cancer and 10,860 will die of these cancers. Seven drugs are approved for HNC therapy, but only 10-25% of patients respond to single agent therapy, and 5-year survival and/or cure rates have not improved. Although pembrolizumab (Keytruda®) was well tolerated in patients with recurrent or metastatic HNC and produced clinically relevant antitumor activity, only 16% patients responded to treatment. The low response rates and limited efficacy of HNC drugs underscores the need to discover new and effective therapies. We have developed methods to characterize HNC MCTS morphologies, viability and growth phenotypes and to conduct cancer drug HTS. In a total of 95 pairwise cancer drug x HNC cell line experiments only 35.8% of MCTS cultures exhibited a concentration dependent growth inhibitory response using metabolic viability reagents, and only 24.4% produced ≥50% reduction in Calcein AM live cell staining. In contrast, 67.8% increased ethidium homodimer dead cell staining by ≥50% and 89.5% altered ≥1 morphological feature; size, shape/perimeter or density/compactness. These data demonstrate that multiple analysis methods are required to accurately assess the impact of cancer drugs on HNC MCTS cultures and to maximize the value of these physiologically relevant tumor cultures.
Quantitating Endogenous Protein Dynamics with a Bioluminescent Peptide Tag
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Open to view video. There are an estimated 3,000 human genes that constitute the “druggable genome.” However, only a small percentage of proteins coded by these genes are the focus of drug discovery programs. One barrier in investigating these understudied targets is the lack of easily implemented and scalable methods for assaying proteins. The two principal techniques for analyzing proteins are immuno-detection and mass spectrometry. They offer the advantage of generating data from endogenously expressed proteins. However, these methods are limited by the lack of protein-specific reagents, sensitivity, and HTS compatibility. This prompted us to develop a workflow for studying endogenous proteins that was both easy to use and scalable. In recent years, CRISPR technology has been utilized to integrate reporters into host genomes, such that cellular proteins can be monitored in real time through detection of the reporter fusion. CRISPR-mediated knockin of the HiBiT luminescent peptide reporter has been demonstrated on a small-scale using a cloning-free workflow. The high sensitivity and dynamic range associated with HiBiT make it suitable to study most cellular proteins across a range of expression levels. Thus, we wanted to determine if CRISPR-mediated HiBiT tagging would provide an approach to rapidly tag any protein in the human proteome. To explore this strategy, a diverse set of proteins representing a broad range of functions and biophysical properties were targeted for tagging with the HiBiT luminescent peptide tag. The majority of the selected targets showed successful integration and expression of functional fusion protein. Given the high success rate in this initial experiment, we investigated if this strategy could be used for developing a HTS compatible assay for an entire protein family. For this purpose, the cyclin-dependent kinase (CDK) family was targeted for HiBiT tagging and then used to quantitate CDK-specific target engagement. Although the majority of edited CDK-HiBiT cell lines displayed compound pharmacology similar to what was observed in over-expression-based models, a number of differences were found which suggests that endogenous models may provide more accurate information on compound activity. In summary, CRISPR-mediated tagging of endogenous proteins with HiBiT represents an easy and scalable strategy for studying endogenous proteins which enables the analysis of proteins in their appropriate physiological context.
A 384-well workflow to execute an arrayed CRISPR-Cas9 Gene Editing screen in T-cells
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Open to view video. Functional genomics approaches to identify novel therapeutic targets are rapidly gaining traction. Arrayed screening for the phenotypes resulting from gene-knockouts using CRISPR-Cas9 technology can yield results rapidly, with very little need for target deconvolution. Data can be further enhanced by the selection of disease relevant primary cells. We have developed a high-efficiency, arrayed genome-editing screen in primary CD4+ T cells using CRISPR–Cas9 for the identification of genes associated with cytokine release. T-cells are isolated, purified and expanded before genome editing occurs via nucleofection. A 384-well nucleofector is used to deliver RNP complexes consisting of guide RNA (gRNA), transactivating CRISPR RNA (tracrRNA) and Cas9 enzyme. Edited cells are rested and activated before being utilised in downstream assays capturing multi-cytokine release and cell viability. The development of miniaturised, robust nucleofection protocols and assays for T-cell screening allows integration of this challenging cell-type onto well-established liquid handling platforms and demonstrates the potential of genome-wide arrayed CRISPR-Cas9 screening of primary cells in a screening environment.
Quantitative Live Cellular Assays for Screening Degradation Compounds and their Mechanism of Action
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Open to view video. A new generation of heterobifunctional small molecules, termed PROTACs, holds significant therapeutic potential by inducing degradation of target proteins. These compounds consist of two binding regions separated via a linker: one that specifically binds to the target protein, and the other that directly recruits E3 ligase machinery, resulting in ubiquitination and degradation of the target. Characterizing PROTAC degradation efficacy represents a significant challenge, both in terms of understanding the individual mechanistic processes that control whether degradation will result, as well as the ability to screen for target protein loss in high throughput fashion. Here, we present a live-cell, luminescence-based technology platform that enables characterization and screening of PROTAC compounds and their mechanism of action using either ectopic or endogenous target expression formats. We employ CRISPR/Cas9 endogenous tagging of target proteins with the small peptide, HiBiT, which has high affinity for and can complement with the LgBiT protein to produce NanoBiT luminescence. This allows for sensitive detection of endogenous protein levels in living cells, and can also serve as a BRET energy donor to study protein:protein or protein:small molecule interactions. Using this combinatorial approach, we demonstrate the ability to measure permeability effects and binding affinities of PROTAC compounds to both target and E3 ligase, as well as monitor the kinetics of the subsequent ternary complex (target:PROTAC:E3 ligase) formation, target ubiquitination and recruitment to the proteasome in live cells. We further show the power of this technology in extended kinetic monitoring of endogenous target protein levels, quantification of key degradation parameters for rank-ordering, correlation of these parameters to the precise MOA, and the application of these approaches for HTS. This comprehensive technology platform enables rapid, simple and robust screening of functional degrader compounds, ultimately aiding chemical design strategies for optimization of new therapeutic PROTACs.
Cellular assays targeting two mutation classes causing Cystic Fibrosis: through (1) protein misfolding or (2) premature translational termination
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Open to view video. Cystic fibrosis (CF) is a disease caused by mutations in the gene coding for the cystic fibrosis transmembrane conductance regulator (CFTR), a chloride channel. Mutations are classified into six classes with phenotypes from no CFTR protein synthesis to misfolding and/or functional defects. Over the past seven years the FDA approved several novel small molecules that partially correct defects of different mutation classes of CFTR. This has triggered broad efforts to find better and/or different small molecule modulators that address even more CF disease-causing mutations. Here we present screening assays for two classes of CFTR variants: (1) F508del (causing protein misfolding and severely impaired cellular trafficking) and (2) premature termination codon (PTC) mutations, resulting in stop codons in the open reading frame of CFTR and no functional expression. Assays need to address the primary defects of these specific mutation types. A differential screening approach allows the discovery of class-specific hit molecules. CFTR F508del leads to (1) misfolding of the nucleotide-binding domain 1 (NBD1) of CFTR and (2) perturbs normal interdomain interaction in the CFTR protein. An efficient therapy needs to address both protein folding defects for CFTR for rescue of CFTR functional expression. Suppressing one defect may allow identification of modulators of the 2nd defect. Thus, using specific suppressor mutations (R555K to restore NBD1 folding or R1070W to rescue domain-domain interactions, allelic screens were developed to enrich for small molecules that preferentially modulate interdomain interactions or NBD1 folding, respectively. The phenotypic screen relies on mammalian cells expressing CFTR F508del with the suppressor mutations and a reporter gene fused into an extracellular loop of CFTR. Hits from the two assays were further tested for complementary effects on trafficking rescue of CFTR F508del. A different class of CFTR mutations are PTC variants (about 170 reported) that cannot be treated with available medicines. During CFTR protein synthesis, interaction of the ribosome with the PTC (UAA, UAG, or UGA) terminates protein translation. Furthermore, when the ribosome stalls at a PTC, translation-coupled RNA surveillance triggers the nonsense-mediated mRNA decay (NMD) pathway, resulting in a reduction of CFTR mRNA levels. Therefore, an effective therapy for CFTR PTC variants needs to address both premature translation termination and reduced CFTR mRNA. Cell-based assays to assess translational readthrough of PTCs have been developed based on either a reporter or the native CFTR gene. RT-qPCR of CFTR mRNA is utilized to monitor anti-NMD effects. Our data support the concept that combining readthrough modulators and NMD inhibitors may lead to more effective therapy. The CF phenotypes for the above two classes of CFTR mutations derive from defects in different stages of CFTR biogenesis. Specific types of mutations require different screens for the identification of mutation class-specific disease modulators.
Automation and High-Throughput Technologies
SLAS2020 Innovation Award Finalist: Intelligent microscopes using open-source hardware for high-throughput laboratory automation
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Open to view video. Traditional microscopes used for automated imaging and analysis sets one aback with tens of thousands if not hundreds of thousands of dollars. This limits number of microscopes a lab can afford, hence limiting the number of parallel experiments that can be performed. We present a novel approach by combining low-cost, low-resolution microscopes with advanced computational imaging methods that can extract high-resolution image information in the post processing. In addition, we implement novel machine learning methods to jointly optimize the automation task, e.g. cell segmentation, and the data acquisition process, e.g. illumination pattern, to capture less data without losing the performance of the automated task. Our initial prototype costing ~$150 employed a Raspberry Pi as the computer and a modified Raspberry Pi V2 camera as the low-resolution microscope. A low-cost 16x16 LED array developed for display is used to illuminate the sample and 3D printed parts are used for assembly. LEDs in the array are sequentially illuminated to capture 256 low-resolution images, where the high-resolution information is encoded within these low-resolution images using the aperture synthesis concepts. The captured 256 low-resolution images were combined to achieve 0.8µm resolution, for the first time in a low-cost setting, across 4 mm2 field-of-view. The phase of the object is also recovered in the process, making this suitable for imaging cell cultures without any need of staining. In the latest developments, we implemented a new machine learning model to multiplex the illumination to reduce the number of images captured to two, without any loss in performance for tasks such as cell segmentation or detecting malaria infection. This also reduces the image processing time and, exploiting the increasing computing performance on opensource hardware such as Raspberry Pi and Google’s Coral edge TPU, we are currently working towards achieving real-time machine learning based automation on our portable low-cost setup. The 3D printed design of our microscope can be easily modified to the specific requirements of a lab, e.g. imaging stress fibre reorientation in cells under mechanical stimuli require a different setup compared to imaging cell confluency in a petri-dish. Our optics and algorithms still stay valid for all these different configurations and the required modifications in the 3D printed designs are usually minor. This is not possible with commercial systems which are designed for a limited number of imaging applications. Combining latest developments in machine learning makes our approach a powerful tool for laboratory automation and diagnostics in low-resource settings.
A systematic medium-scale comparative study of 2D vs. 3D models using high content imaging approaches
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Open to view video. Development of new pharmaceutical drugs is an expensive and high-risk endeavor for pharmaceutical industry. Major advances in physiologically relevant in vitro cellular assays such as three-dimensional models, induced pluripotent stem cells, organ-on-chip are expected to provide a better ability to predict therapeutic response, hence, reducing clinical attrition. Unlike High Throughput Screening, High Content Screening combines automated fluorescence microscopy with quantitative image analysis allowing phenotypic multiparametric readouts such as cell viability, DNA damage or mitochondria structure among many others. This approach is particularly well suited for complex or partially characterized targets. Moreover, in the oncology field, it has been shown that compound efficacy could be dramatically modulated in 3D models. In this context, we are aiming to perform a medium scale screening campaign using a chemically diverse compounds collection on a cell line derived from non-small cell lung cancer with a specific mutation both in 2D and 3D models. Using High Content Imaging approaches, a large set of parameters will be extracted, leading to a better characterization of various toxicity mechanisms of actions. To provide robust comparable results between cellular models, a specific subset of compounds was selected and screened in dose responses during the assay development workflow. The analysis of this rich set of complex data provided an opportunity to improve the rest of the screening campaign. Hits obtained from both screens will be classified, compared and validated in dose responses for a better understanding of the difference induced by the use of 3D model combined with High Content Imaging. Ultimately this could help assess the relevance of 3D model in drug discovery in oncology.
Acute Myeloid Leukemia Drug Sensitivity Testing Using Patient-Derived Cells in 1536 Format
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Open to view video. Acute Myeloid Leukemia Drug Sensitivity Testing Using Patient-Derived Cells in 1536 Format Lynn Rasmussen M.S. Southern Research Birmingham, AL Co-Authors Christopher Klug, Robert Bostwick, Miranda Burnette, AJ Reece In practice the choice of which drug to prescribe for an individual patient is often made without any information on how that individual will respond to a specific drug. The field of precision medicine is attempting to provide data to fill that information gap in order to match the patient with the most effective treatment for that individual. To try to fill that gap for AML patients we have developed a drug screening process using a panel of FDA approved drugs with patient derived leukemia cells. Because the patient derived cells are an extremely limited resource, a 1536-well assay format was developed to maximize the amount of data that could be generated for each patient. The process of developing this assay will be discussed, including the technical challenges, their solutions and the equipment choices used to achieve a reliable HTS format screening protocol.
Automating a CRISPR based rescue screen for Alzheimer’s phenotypes in iPSC derived neurons
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Open to view video. Shushant Jain is currently a group leader at Charles River where he utilizes high throughput – high content methodology for target identification, target validation, lead identification, and lead optimization in physiologically relevant cellular model systems such as primary or stem cells. Prior to Charles River, he was the lead developer of phenotypic assays to enable discovery of novel pathways or targets in numerous neurodegenerative diseases as well aid in the understanding the role of common genetic variation in disease pathogenesis.
A beginner’s guide to the practicalities of automating chemical synthesis
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Open to view video. In late 2017 AstraZeneca undertook an internal “hack-a-thon”, bringing together a diverse skill set to investigate our ambition of fully automated chemical synthesis for drug-like compounds. Over the past 2 years we have evolved 4 prototypes to better understand the challenges associated with all stages of a multi-step synthesis process. In this presentation we will review our most recent evolution, realising fully automated batch and flow chemistries to fuel automated synthesis. We will describe in detail the integration and optimisation of the Zinsser SOPHAS platform for batch chemical synthesis; along with the varied Waters devices for product purification and analysis. To achieve seamless integration, we have selected a third-party process scheduling software. Here we will cover the development of new drivers, protocols and interfaces for chemistry system control, along with the unusual demand of tracking and scheduling both single vial and associated plate-based activities (and the interplay between the 2 formats). Looking forward to our 5th iteration, we’ll discuss our plan for pre-cursor storage, along with our concepts for how reactions could be constructed using the Zinsser REDI platform.
A Decade and Journey of Lilly’s Discovery Automated Synthesis
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Open to view video. Lilly actively engages Automated Synthesis in it's Medicinal Chemistry portfolio of projects. This brief talk introduces the journey Lilly has been on with the Automated Synthesis Lab (ASL - Indianapolis) and now with the closed-loop and integrated automation capabilities residing within the Lilly Life Sciences Studio (L2S2 - San Diego). Along the way, Automated Synthesis has provided a foundation for other Lilly initiatives including the Proximal Lilly Collection (PLC, published), Idea-to-Data (ItoD, published) and ChemoPrint (in press).
Enabling Nontraditional Screeners from a Centralized uHTS Core
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Open to view video. Large, ultra-high throughput screening systems can lead to an overreliance on simple assays and deny screening access to those with lower throughput needs. There is a temptation to forego slower, complex assays in favor of ones more amenable to HTS and to pursue familiar target families that “plug-in” to know platforms. HTS facilities, sometimes siloed within a department, are often unavailable even to those working in an HTS capable organization. As a result, some organizations have turned to multiple smaller systems designed for small-scale screening. However, data and compound management issues arise in this decentralized approach. Compound spotted assay plates, created by acoustic compound transfer platforms, can mitigate these issues. However, even combined, these cannot match the capabilities of a larger system when a larger campaign is needed. We have set up a hybrid uHTS/modular acoustic transfer platform that can act as one integrated system or three modular systems. Combining this with a high value chemical library and an active pursuit of partners with high-value bioassays, we have pursued an approach to enable high-value, low-throughput assays, while maintaining uHTS capability and centralized compound and data management.
Highly Integrated Modular Systems – Mobile Robots Unlock the Best of Both Architectures
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Open to view video. Choosing the right system architecture for your automation can be challenging. Large, highly integrated systems provide advantages in terms of throughput and operation simplicity, but the system itself becomes a single point of failure and it can be difficult to maintain up-time as well as evolve the system as applications and technologies change. Modular systems provide greater flexibility, are easier to scale and adapt to changing needs, but require more human effort to operate. It can also be challenging to integrate the data from disparate modules and manage efficient utilization across the full workflow. Fortunately, advances in mobile robot AGV (Autonomous Ground Vehicle) technology, coupled with new scheduling and data management architectures can bridge the gap. These provide the means to fully integrate modular, manual or robotic workcells by scheduling and executing operations with the additional capability to transport consumables, samples, and reagents between modules. This enables a truly connected and fully automated lab while still maintaining the advantages of standalone walk-up operations that has the flexibility to evolve as your needs change.
Innovative tube and dispensing technologies enable fully acoustic workflows for drug discovery assays
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Open to view video. Innovative design and deployment of novel labware, instrumentation and software technologies have delivered an automated, fully acoustic platform and a step-change in small molecule Sample Management (SM) processes. For many years, conventional SM workflows have included multiple sample transfers between vessels, using a hybrid of contact and non-contact dispensing, which are cumulatively wasteful. These combine to affect excessive sample consumption, necessitating chemists to synthesise superfluous quantities of compound. Here we show high quality concordant datasets from the first fully acoustic workflow for physico-chemical, enzymatic, cellular and in vitro ADME assays. We also show a reduction in (i) sample usage in these assays, (ii) DMSO usage throughout the process, and (iii) future synthesis requirements. An acoustically compatible storage tube (FluidX™ AcoustiX™ Sample Tubes, Brooks Life Sciences, UK) was designed with optimum geometry for dispensing accuracy and speed, whilst maintaining a working sample volume able to sustain a 10-year screening lifetime. Co-moulded capping technology for these tubes has resulted in increased durability for multiple dispense access and sample longevity, whilst a novel split barcode at the base affords a central opening for transmission of the acoustic pulse. A tube-compatible acoustic liquid handler (Echo® 655T Liquid Handler, Beckman Coulter Life Sciences, USA) has been designed to utilise acoustically compatible storage tubes including a faster drop-transfer rate via a new transducer-focussing mechanism. A new dryer system removes moisture on the exterior of the tube, alongside local humidity control in the drop-transfer zone to maintain sample integrity. The development of a new, fully acoustic workflow has minimised sample handling and waste, enabling miniaturisation of assays and hence reducing the amounts of sample required for synthesis to support drug discovery projects. We have implemented and validated novel labware and instruments for a transformative and sustainable solution to many drug discovery issues applicable across the industry.
Revolutionising Cellular Screening with Artificial-Intelligence-driven Label-free Imaging
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Open to view video. The primary reasons for drug failure in the clinic are a lack of efficacy, and safety. Therefore, in order to drive a better understanding of disease biology and improve translation, cellular imaging assays in early discovery need to be increasingly complex, utilising multiple biomarkers to label several proteins in a pathway, and to quantify multiple sub-populations. The field of image analysis has been transformed by the explosion of machine learning and AI methods, and we are now leveraging recent developments to maximise the information we get from imaging data and enable new experimental approaches. A key limitation of machine learning, and particularly deep learning models, is the requirement for large amounts of annotated training data. We have developed an active learning framework for efficient training data generation, alongside unsupervised phenotype discovery approaches, to build models which can quantify the full complexity of cellular screening data. We are also integrating label-free phase contrast imaging into our cellular screens. A large amount of information on cellular morphology is contained in the phase contrast images, which do not take up a fluorescent colour channel, but human interpretation is very difficult. By training a deep neural network to find and segment nuclei and cells from phase contrast images alone, nuclear and cell markers are no longer required. This allows multiple biomarkers to be combined into a single screen, enabling more complex biology for less cost. In addition to segmentation, we have shown that standard readouts such as cell division and cell death can be predicted from the label-free images, opening the possibility for digital multiplexing of a wide range of biomarkers, in living cells, and without expensive cell engineering. Combining this method with the high-content Cell Painting assay, we are learning how to extract meaningful biological features from label-free images, which can then be used to re-interrogate existing screening data for new insights. This approach is straightforwardly integrated with existing workflows, and is revolutionising the questions being asked through cellular screening.
Applications of Image-Based Artificial Intelligence in Drug Discovery and Safety Testing
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Open to view video. Discovering effective drugs and demonstrating their safety are significant challenges facing the pharmaceutical industry, due to the high costs of development, long lead times, and low success rates of late stage clinical trials. There is a need for new tools and technologies to help identify safe and effective drugs during the early stages of development. Over the last decade, there has been significant progress in using human induced pluripotent stem cells (hiPSCs) for modeling of human disease, drug screening, and toxicity testing. Numerous studies have demonstrated that these cells have physiologically relevant characteristics and can be used for preclinical testing of new drugs using high-throughput assays. In such assays, image and signal analysis algorithms are used to generate quantitative measurements that relate to cell degradation, death, or changes in function. Such approaches may be missing subtle changes that are not easily visualized, are too complex to measure with traditional data analysis methods, and/or suffer from lack of consistent quality control metrics on the input data. Artificial intelligence (AI) techniques, and specifically deep convolutional neural networks, are perfectly suited to address the challenges of these high-throughput assays by analyzing large amounts of imaging data robustly and with a level of sensitivity that has not been previously possible. We present case studies for using AI for high-throughput image-based phenotypic screening, toxicity testing, and quality control. First, we present data from a drug discovery program for dilated cardiomyopathy using high-throughput imaging of sarcomere structure in stem cell-derived cardiomyocytes. We were able to build disease models with high accuracy, which were then deployed to identify small molecules that showed to reverse the disease phenotype. The identified small molecules were further validated with functional assays and preclinical mouse studies. Second, we present data from a pilot toxicity testing study using stem cell-derived cardiomyocytes. Our novel image-based AI method was successful in capturing dose-dependent structural changes on a panel of drugs with known cardiotoxicity profiles, while no change was detected for the negative control. The detected structural changes correlated strongly with contractility. Finally, we present data from a pilot quality control study using current-trace signals from a patch clamp instrument. We successfully built an AI model that can accurately classify signals as good versus poor quality, which enables automated and consistent filtering of data during high-throughput experiments.
Optical Pooled Screens in Human Cells
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Open to view video. Pooled genetic screens have been critical for the systematic identification of genes underlying cellular processes, but have thus far been limited to phenotypes defined by cellular enrichment or comparatively low-throughput single-cell molecular profiling. We have developed a method to make pooled libraries compatible with the rich set of spatially and temporally resolved phenotypes accessible to high-content microscopy by using targeted in situ sequencing to demultiplex genetic perturbations. We applied this technology to screen 952 genes for involvement in NF-κB signaling by imaging p65 nuclear translocation and relaxation, recovering most canonical pathway members and identifying novel candidate regulators of IL-1β/TNFα-stimulated immune responses. We are currently piloting applications with a range of optical assays and cell models and expect that pooled optical screens will have broad utility in identifying genetic components, analyzing genetic circuits, and interrogating disease variants.
High throughput single cell imaging and advanced machine learning supported image analysis of primary tumors enables anticancer therapy development
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Open to view video. The ability to perform high-content screening in a high-throughput fashion is routinely limited to cell lines and other explant model systems, however, there is a risk that these may not be fully representative of the in vivo environment due to culture adaptation or the lack of multi-lineage cell types. The ability to gather high-content data directly from primary samples however, both direct from blood and bone marrow, metastasized cancers, and dissociated solid tumor, without cell outgrowth or selection in a method amenable to laboratory automation can be a more direct system. Further, by combining imaging of these primary sample with an adaptable analysis pipelines robust to micro-aggregates, especially formed in solid tumor biopsy homogenates, vastly different cell shapes and sizes, and that can ultimately harness the features from each cell can become a powerful means to study drug response in a variety of indications using model systems directly derived from the patient. This methodology has been used to prioritize therapy for late-state patients with hematological cancers in a basket trial (Snijder & Vladimer et al 2017, Lancet Hematology), has been integrated with genetic data to further uncover biological understanding and clinical synergy options (Schmidl & Vladimer et al 2019, Nat Chem Bio). Here, this talk will specifically focus on the details of the computational framework, including supervised and unsupervised machine learning approaches for cell identification and feature extraction, and other aspects of necessary infrastructure including cloud-deployment that is used to, in very high-throughput, quantify single-cell phenotypes form primary material from cancer patients for for drug discovery. Further, the use case of understanding single-cell phenotypes after drug screening, both in single-cell suspensions and in micro-aggregate multi-cell / 3D environments, will be highlighted.
Continued development of high throughput MS and applications for cell analysis
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Open to view video. Over recent years, AstraZeneca has worked to develop a high throughput mass spectrometry platform which utilizes the speed and contactless nature of acoustics as a sample introduction technology. Fully automated acoustic mist ionization mass spectrometry platforms are now routinely used to support biochemical HTS campaigns, to date over 10 million samples have been successfully screened against more than 10 different enzyme targets using this technology. Having established a primary role for AMI-MS we are now looking to expand the application space where the technology could add value to early drug discovery. We have recently started to evaluate the impact of AMI-MS for metabolomic analysis of cell lysates, primarily within the early toxicology screening area. In December 2018, AstraZeneca and collaborators at several Swedish academic institutions and SME’s were awarded a phase 2 grant from Sweden’s Innovation Agency, Vinnova. The aim of the collaboration is to develop technologies and workflows to enable primary patient derived disease cells to be utilized in the early phase of drug discovery. This presentation will focus on the continued development of AMI-MS within AZ and how we are applying high throughput mass spectrometry to enable clinical samples to be assessed in the early phases of drug discovery.
Array and microfluidic based cellular assays miniaturized beyond the 1536 well plate
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Open to view video. Small molecule high throughput screening (HTS) in drug discovery traditionally involves microtiter plate screening in 384- and 1536- well formats. While these methods are miniaturized compared to petri dishes or flasks, reagent and labor costs are still significant factors in high throughput screening campaigns. Here we present the development of cellular assays utilizing ultraminiaturized array-based and microfluidic devices. The experiments were aimed at reducing cellular assay volumes from uL to nL volumes. Challenges included maintaining environmental controls for cell health and handling small volumes for cells and compounds. Novel approaches in equipment, device design, and automation were required. Data suggest that cell health, cell morphology, and pharmcological responses to drugs were similar in nL volumes compared to those observed in 50 uL volumes in 384 well plates. Development of processes and automation to industrialize new devices will ultimately enable these technologies to be applied broadly in drug discovery. Cost savings in cell and reagent usage, and the ability to use disease relevant cell systems are paths toward reduced attrition in drug discovery.
Identification of chemical compounds inhibiting Zika virus replication through a large-scale high-content screening approach
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Open to view video. Zika virus (ZIKV) is a human mosquito-borne positive-sense RNA virus, belonging to the Flaviviridae family. World Health Organization (WHO) classified this virus as an Emergency in 2016 and currently identifies Zika as a priority disease. Although symptoms are generally mild, a risk of neurologic complications including Guillain Barré Syndrome is associated with the infection in adults, while infection during pregnancy is responsible for microcephaly and other congenital malformations. Since no vaccine or commercialized antiviral targeting this virus are available, scientific efforts are currently focusing into the development of treatments allowing to efficiently limit ZIKV spread. Prompted by this unmet medical need, we conducted a screen of 51,520 small chemical compounds using a high-content imaging cell-based assay, monitoring Zika virus replication within Huh-7.5 cells by combining DAPI staining of cellular nuclei together with immunostaining of the Zika virus envelope protein. 99 candidates were identified and validated as inhibiting ZIKV replication of at least 50% at a concentration of 10 µM. Subsequent dose-response studies were performed to evaluate the effects of each compound on both virus replication and cytotoxicity and compounds showing a strong dose-response inhibitory effect on replication with weak cell toxicity were then selected for follow-up studies. Two compounds sharing a common structure presented a particularly promising antiviral activity with a selectivity index, calculated as the ratio of 50 % inhibitory (IC50) and 50 % viability (CC50) concentrations, greater than 30. This common chemical scaffold showed to specifically inhibit ZIKV, displaying an antiviral activity against several strains of both African and Asian lineages but no effect on other Flaviviruses tested. Its antiviral activity was confirmed with similar efficacy in more relevant models for ZIKV infection, including human monocyte-derived dendritic cells (hMDDCs), human neural progenitor cells (hNPC) and the placenta-derived choriocarcinoma cell line JEG-3. Time of addition kinetics as well as specific entry and replication assays excluded an inhibitory role during ZIKV entry, highlighting an antiviral role during the RNA replication step. This observation, in addition to the appearance of resistant mutant viruses upon selection in the presence of the drug, strongly suggested a non-structural protein of the virus as a target of the compound. Current efforts are ongoing to identify the specific viral target of the compound and to get more insights about its mechanism of action. In addition, Pharmacokinetics (PK) and in vivo efficacy studies will be performed in the near future to evaluate the therapeutic potential of this compound. In summary, taking advantage of a cell-based large-scale high-content screening approach to identify small chemical compounds showing an antiviral activity against ZIKV, we identified a chemical scaffold specifically targeting this Flavivirus, inhibiting its RNA replication step.
Improving daily operation of a fully automated uHTS system
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Open to view video. Pivot Park Screening Centre (PPSC) is a small company that specializes in ultra-High Throughput Screening (uHTS) for drug discovery. We perform about 25 full deck screening campaigns a year on our own library of 300.000 compounds, the European Lead Factory library of 550.000 compounds and client libraries up to 1.000.000 compounds. In order to support this huge production we have implemented efficient processes on a fully automated screening system consisting of 3 robot pods integrated with a wide variety of instruments. Even in a highly automated environment like our uHTS lab, there is a continuous need for tools and little tricks to support our daily work. These include software tools to run active picking from the online store, re-use of (washed) verification plates, a simple tool for drying compound plates, implementation of a weighing station in the robot for checking dispensing performance, etc. Also, we have implemented cleaning stations for our heavily used certus dispensers. Finally, we make extensive use of a 3D-printer to create all sorts of tools in the lab and to save costs by printing parts of instruments that need repair. This presentation will provide insight in the daily operations in a uHTS lab.
Leveraging Open Source Electronics for Rapid Development of Custom Laboratory Devices
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Open to view video. The Lead Identification team at Scripps Florida routinely leverages open source technologies to meet operational challenges and to provide custom engineering tools for lab use. Most recently, these tools include development of a microsolenoid dispensing QC platform built around the Lee Company VHS series valve. This system was developed to address the unmet need of dispensing of 3D models such as spheroids and to assist with general QC of valves used in ongoing HTS efforts. This QC platform allows users to characterize & optimize the performance of VHS valves under a variety of conditions. This reconfigurable QC platform is built on an optical breadboard and is comprised of three main subsystems: electronics, optical train and motion control. The electronics subsystem allows users to easily control VHS series valve behavior using an Arduino microcontroller through a custom in-house designed Arduino shield. The optical train consists of an off the shelf USB camera combined with an in-house designed open-source illumination panel that allows imaging of individual droplets via the stroboscopic effect. An open source X/Y motion control system further increases the utility of the platform by allowing automated dispensing into microplates. User control of the QC platform is provided via a custom web-based interface that communicates directly with the microcontroller and allows users to easily specify microplate dispense patterns by interacting with graphic representations of microplate wells. Also presented is the development of a custom Arduino-based syringe pump system intended for use alongside the Lee valve QC platform. This syringe pump system utilizes a Tecan Cavro pump and allows for real-time adjustment of pump parameters during mixing and dispensing operations which are critical for mixing and homogeneous distribution of the spheroids. The development of these platforms, lessons learned, and results of initial testing are presented.
Beyond High Content Screening: An Open Next Generation Image Analysis Platform
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Open to view video. There is an increasing interest in discoveries from images acquired by high-throughput and high content microscopy imaging of multi-well plates with biological specimens under a variety of conditions. As multi-dimensional automated imaging increases its throughput to thousands of images per hour, the computational infrastructure for handling the images has become a major bottleneck. The bottleneck associated challenges arise due to big image data, complex phenomena to model, non-trivial computational scalability that leverages advanced hardware and cutting-edge algorithms, and incompatible software tools that vary in the language they were written in, platform they were written for, and capabilities they were designed to execute. To address the above challenges, groups have developed software solutions based on client-server systems with modern web technologies on the client side and a spectrum of databases, computational workflow engines, and communication protocols on the server side to hide the infrastructure complexity. However, these solutions have not focused on inter-operability of imaging specific computational plugins and visual exploratory capabilities of such plugins over very large image collections. To address these inter-operability and visual exploration challenges, the National Institute of Standards and Technology (NIST) and the National Institutes of Health (NIH) - National Center for Advancing Translational Science (NCATS) have formed a close collaboration to develop an open source platform for executing web-based image processing pipelines over very large image collections with interoperable plugins. The plugins developed by both institutes are based on software containers as standardized units for server-side deployment, as well as on dynamically created web user interfaces (UI) to enter parameters needed for the software execution and for advanced visual data explorations on the client side. Each container packages code, with all its dependencies, and has an entry point for running the computation in any computing environment. Each UI description file contains metadata about the plugin container and the computation parameters. We will demonstrate the utility of the platform with algorithmic plugins by analyzing 1536 well plates with three spectral channels and multiple fields of views (FOVs) per well for drug dose response across an array of features. Typical visual data exploration is assisted by algorithmic tools for quality control, stitching of FOVs per well, segmentation, characterization of regions of interest, and scalable visualization using Deep Zoom, a toolkit for browser viewing of gigapixel 2D images. The data explorations are interactive either in a Deep Zoom viewer or in a Jupyter notebook while prototyping pipelines. More demanding computations are supported via batch processing and deep learning-based pipelines are designed for GPU execution. With the NIST and NIH NCATS combined efforts, researchers are enabled to discover quantitative insights from their imaging data and reuse computational tools developed by anyone following the web computational plugin conventions.
Biologics Discovery
SLAS2020 Innovation Award Winner: High-Throughput Encapsulation and Selection of Cells Based on Antibody Secretion Using Lab-on-a-Particle Technology
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Open to view video. We introduce a new approach to collect and quantify single-cell secretions without crosstalk in monodisperse droplets formed by precisely structured microparticles, enabling high-throughput screening based on this critical cell function. The ability to analyze and sort cells based on secretions (antibodies, cytokines, proteases, or other enzymes) has implications in understanding cellular heterogeneity fundamental to biology and creating new biotechnology products, such as biologics and cell therapies. Recently, droplet microfluidics has emerged as a powerful approach to perform single-cell secretion screening in high-throughput, using compartmentalization in a small volume to accumulate secreted factors to high levels for accurate detection. Despite this utility, the necessity of specialized equipment and expertise on the end user hinders its widespread adoption. A platform that is fully compatible with standard lab equipment (e.g. pipettes, flow cytometers) has the potential to dramatically extend the reach of single-cell screening technology. Our particle-templated droplet, i.e. “Dropicle”, approach is unique in that pre-fabricated particles are used to form monodisperse emulsions that encapsulate single cells, requiring only standard lab equipment for the end user. Cavity-containing microparticles are loaded into wellplates and due to their morphology settle upright with their cavities exposed. Cells are loaded into the microparticle cavities and adhere via integrin binding sites. Biocompatible oil and surfactant are added and the suspension is agitated by pipetting to create incrementally smaller water-in-oil droplets. These resulting dropicles are monodisperse, maintaining a size defined by the particle geometry (CV< 6%), while excess fluid is partitioned into surrounding smaller satellite droplets. Secretions from encapsulated cells are captured on the associated particles via protein A binding sites. Particles and associated cells and secretions are transferred back to aqueous phase enabling downstream labeling and screening with standard flow cytometers. It was observed that seeded cells filled the cavities of the particles according to single-poisson statistics (in contrast to typical double-poisson statistics for single-cell, single-particle pairs in drops). After dropicle formation cells maintained high viability over 24 hours ( >80%). Initial tests with anti-IL-8 producing CHO cells demonstrate the ability to capture and label secretions on particles containing cells without crosstalk to neighboring particles. Further we demonstrate the ability to isolate cells associated with high anti-IL-8 signal in high-throughput using commercial flow cytomtery systems ( >100 sorts/s). Using this dropicle platform researchers can perform droplet-based assays using standard lab equipment without sacrificing the precision of droplet microfluidics. Since dropicles are formed simultaneously, compartmentalization is rapid ( >400k in 30s) and can be easily scaled to accommodate large population screens. Further, the associated particle enables additional functionality such as physicochemical cues or cell specific capture antibodies to select out specific cell types. Our results demonstrate new capabilities for lab-on-a-particle technologies that can accelerate automation of single-cell assays.
SLAS2020 Innovation Award Finalist: µ-Hydroporator: A Next Generation Intracellular Delivery Platform
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Open to view video. The introduction of biomolecules and functional nanomaterials into cells is a crucial task in diverse biological situations, including immunotherapy, genome editing, regenerative medicine, and fundamental biological studies. Traditionally, intracellular delivery is achieved by carrier-based or membrane-disruption-based techniques. Carrier-based approaches utilize reconstituted viruses (e.g., lentivirus or AAV), or liposome (e.g., Lipofectamine), and when optimized they offer effective delivery (e.g., DNA delivery for cell transfection). However, carrier-based approaches critically suffer from toxicity, low-throughput, and require time-consuming and/or labor-intensive preparation steps. Alternatively, membrane-disruption-based methods such as electroporation and microinjection create transient discontinuities on the cell membrane for target material diffusion. The physical cell membrane disruption is relatively independent of target and cell type, but they cause excessive damage to cells and suffer from limited throughput. To address these drawbacks, recent advancements in microfluidics and nanotechnologies have provided new solutions; however, identifying an ideal method that offers easy, low-cost, highly efficient, high-throughput, noninvasive, and cell type/target independent delivery, still remains challenging. Here, we present a next-generation intracellular delivery platform termed “µ-Hydroporator,” which introduces macromolecules into any cell type, at high-throughput, in a single-step, without a vector or external apparatus. µ-Hydroporator is purely based on hydrodynamic cell deformation-restoration process, which opens the cell membrane and enables efficient transport of external target biomolecules or functional nanomaterials into the cell. In brief, the cell suspension mixed with target materials is injected into a T-junction microchannel with a micro-cavity where inertial vortices instantaneously deform cell. This rapid hydrodynamic cell deformation creates transient nanopores on the cell membrane, allowing the convective transport of foreign target molecules during the cell restoration process. Using µ-Hydroporator, we have successfully delivered diverse macromolecules (e.g., RNAs, Plasmids, DNAs, DNA origami, CRISPR-Cas9s, proteins, Q-dots, AuNPs, etc.) into various cell lines including difficult-to-transfect primary cell lines such as stem and immune cells, achieving highly efficient intracellular delivery (< 98%) in a high-throughput manner (~1,600,000 cells/min) while maintaining high cell viability (< 95%). Unlike traditional methods that rely on external apparatus, and/or chemical modification of target molecules, µ-Hydroporator only requires a syringe pump (not even a microscope!). This permits easy, robust and simple operation and cost-reduction from not requiring a skilled technician and instrument. We firmly believe that the reported µ-Hydroporator will establish a new paradigm in intracellular delivery, which will immensely benefit cellular engineering research and industry.
Benefits of Chicken-Derived Antibodies for Combination Immunotherapy
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Open to view video. Development of novel antibodies and more powerful therapeutic combinations for immunotherapy is an intense area of focus. However, for difficult and/or conserved targets, finding antibodies with unique functionality, and generating early proof of concept pose challenges to the development of novel antibody therapeutics. Symphogen’s approach to discovery and development of potent antibody combinations for cancer immunotherapy using different species, including chicken, will be presented. Examples from our clinical pipeline will be shown.
Quantitative high-throughput screening assays for the discovery and development of SIRPα-CD47 interaction inhibitors
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Open to view video. CD47 is an immune checkpoint molecule that downregulates key aspects of both the innate and adaptive anti-tumor immune response via its counter receptor SIRPα, and it is expressed at high levels in a wide variety of tumor types. This has led to the development of biologics that inhibit SIRPα engagement including humanized CD47 antibodies and a soluble SIRPα decoy receptor that are currently undergoing clinical trials. Unfortunately, toxicological issues, including anemia related to on-target mechanisms, are barriers to their clinical advancement. Another potential issue with large biologics that bind CD47 is perturbation of CD47 signaling through its high affinity interaction with the matricellular protein thrombospondin-1 (TSP-1). One approach to avoid these shortcomings is to identify and develop small molecule molecular probes and pretherapeutic agents that would (1) selectively target SIRPα or TSP-1 interactions with CD47, (2) provide a route to optimize pharmacokinetics, reduce on-target toxicity and maximize tissue penetration, and (3) provide for more flexible routes of administration. As the first step toward this goal, we report the development of an automated quantitative high throughput screening (qHTS) assay platform capable of screening large diverse drug-like chemical libraries to discover novel small molecules that inhibit CD47-SIRPα interaction. Using time-resolved fluorescent resonance energy transfer (TR-FRET) and bead-based luminescent oxygen channeling assay formats (AlphaScreen), we developed biochemical assays, optimized their performance, and individually tested them in small-molecule library screening. Based on performance and low false positive rate, the LANCE TR-FRET assay was employed in a ~90,000 compound library qHTS, while the AlphaScreen oxygen channeling assay served as a cross-validation orthogonal assay for follow-up characterization. With this multi-assay strategy, we successfully eliminated compounds that interfered with the assays and identified five compounds that inhibit the CD47-SIRPα interaction; these compounds will be further characterized and later disclosed. Importantly, our results validate the large library qHTS for antagonists of CD47-SIRPα interaction and suggest broad applicability of this approach to screen chemical libraries for other protein-protein interaction modulators.
Exploiting natural killer receptors for autologous and allogeneic CAR T cell therapy of cancer
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Open to view video. Chimeric Antigen Receptor (CAR) T-cell therapy has hit the headlines with impressive clinical responses in hematologic B-cell malignancies that have led to the successful licensing of two products that both target CD19. Anti-BCMA CAR T-cell therapies for myeloma might come next but there is a dearth of targets outside of the B-cell malignancy space. Celyad has been exploring the potential of Natural Killer cell receptors to target cancer. Specifically, the company is conducting a series of clinical trials testing the safety and efficacy of CAR-T cells bearing the Natural Killer Group 2D (NKG2D) receptor that has the ability to specifically bind eight stressed induced ligands found on a broad range of cancers, yet largely absent from the surface of non-malignant, healthy cells. The first trial involved giving multiple infusions of autologous NKG2D-based CAR-T cells without pre-conditioning chemotherapy and provided some initial evidence of clinical activity with a good safety profile. Further clinical activity has tested this CAR-T approach with pre-conditioning and standard of care chemotherapy in hematological and solid tumors. Results of these trials and of the company’s trial of allogeneic NKG2D-based CAR T cell therapy will be reviewed; the latter being thought to be the first allogeneic CAR-T approach to be tested in the solid cancers. Aside from NKG2D focused studies, the company is also embarking on a broader allogeneic CAR T cell platform technology exploiting interfering RNA to control graft versus host disease, the primary limitation of using allogeneic cells for therapy. Taken together, these early clinical studies suggest that the NKG2D receptor in both autologous and allogeneic approaches could provide the potential to target a broad range of tumor targeting that could follow the success of CD19 and BCMA CAR T cell therapy. The challenges ahead relate to how to exploit the targeting ability of NKG2D. Some of the next steps including manipulating the memory phenotype of the CAR T cell product will be discussed.
An Innovative Method for the Efficient, High-Throughput Transfection of Primary Human T-Cells
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Open to view video. Cancer with 18.1 million new cases and 9.6 million cancer-related deaths observed in 2018 is still one of the most prevalent threats to human health and well-being. Therefore, there is a strong need for better cancer treatment. Cancer immunotherapy makes use of components of the immune system like antibodies that bind to, and inhibit the function of, proteins expressed by cancer cells. More promising novel immunotherapies rely on patient-derived, genetically modified cells like T-Cells or Natural Killer Cells that express chimeric antigen receptors (CAR). Primary Human T-Cells are difficult to modify genetically using chemical transfection reagents, just as virtually all non-dividing primary cell. Viral transduction methods depend on the cumbersome production of the viral vectors. Classical electroporation methods are often limited in throughput and can result in impaired cell viability and functionality. Therefore, we optimized the transfection and culture procedure for primary human T-Cells using the 4D-Nucleofector™ System and 96-well Shuttle™ Device allowing the high-throughput transfection of up to 96 independent transfection samples in parallel. Human T-Cells enriched from buffy coats were transfected with pmaxGFP™ Vector through a high viability or high efficiency Nucleofector™ program in 20 μl volume. Donor-dependent transfection efficiencies of up to 70% with high cell viability were achieved 48 hours after transfection. Transfection of eGFP mRNA resulted in up to 60% transfection efficiency with more than 90% cell viability 24 hours after transfection. In a second step, we stimulated isolated human T-Cells for 2–3 days prior to transfection via CD3 and CD28. 1.0 x 106 cells were transfected with the high viability program using pmaxGFP™ Vector in 20 μl volume. Cells were analyzed 24 hours post transfection revealing transfection efficiency and cell viability comparable to the results of unstimulated T-Cells. In a last evaluation step, using unstimulated human T-Cells, we could show very low intra- and interplate variability of the 96-well Shuttle™ System. Transfection efficiencies varied between 62% and 77%, while a cell viability of more than 80% compared to non-program control was observed. In summary, we present an efficient and reliable transfection system for primary human T-Cells that allows the parallel processing of up to 96 independent samples. The showcased method will support cell-engineering approaches including screening of siRNA libraries, CRISPR-based genome editing and rapid evaluation of different CAR constructs to advance novel biomedical treatments including immunotherapy approaches.
Target- and Drug discovery in ‘undruggable space’ using functional proteomics
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Open to view video. Despite an increasing spend in drug development, many diseases remain unaddressed with little hope of finding new treatment options by conventional means. One reason for this is the lack of knowledge regarding which proteins are druggable, and which pockets on the protein surface might be most beneficially targeted by small molecules.Current methods for discovering new drug targets rely on genetic knock-out (CRISPR) or knock-down (RNAi) methods. While these techniques can be useful in providing candidate therapeutic target genes, the next step of developing protein-targeting therapies often stalls due to insufficient information on druggability.To address this problem, PhoreMost has developed a functional proteomics / phenotypic screening technology called “Protein Interference” (PROTEINi®) that yields both the target’s identity as well as information on available druggable sites within the target. PROTEINi utilises proprietary large ( >1 Million), diverse, lenti-encoded libraries of small, self-folding, three-dimensional peptide "shapes", which are expressed in live cells and, much like small molecules, interfere and engage with available pockets on target proteins on a proteome-wide scale. In contrast to shRNA or CRISPR screens, PROTEINi works on the same level as most small molecules (the proteome) and is not influenced by gene copynumber, SNPs or genetic buffering. The process discovers novel targets for a given assay system as well as peptides engaging this target functionally as a starting point for drug discovery.PhoreMost currently has internal small molecule programs in Oncology, Immuno-Onc and Neurodegenerative disorders. We have recently also expanded the method into Targeted Protein Degradation space to systematically discover novel and functional E3 linkage sites across a set of 600 ligases
New Modalities for Drug Discovery
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Open to view video. Successfully drugging a target of interest is one of the key issues in drug discovery. Small molecules and antibodies have a long and successful history in drug development as our primary drug modalities. However, there are an increasing number of targets that are not amenable to these drug modalities – the ‘Undruggable Genome’. At AstraZeneca we are investigating new modalities to be able to drug this previously undruggable set of targets. In this talk I will describe our work with a range of new modalities including oligonucleoties, therapeutic proteins, cyclic peptides, antibody mimetics and PROTACS and how these are allowing us to tackle the ‘Undruggable Genome’.
Cellular Technologies
Wound-conformal delivery of dermal tissue constructs for full-thickness burn treatment using a handheld bioprinter
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Open to view video. Full-thickness burns where both the dermal and epidermal layer of the skin are destroyed result in high patient mortality due to infection, dehydration, and shock. The current standard of care involves the direct application of an acellular crosslinked protein scaffold which forms a temporary physical barrier and promotes host cell migration into the wound area; however, this is problematic in severe burns where little healthy skin is available for repair. Delivery of patient-derived autologous or immunoprivileged allogeneic cells are emerging as potential treatment options due to continuous extracellular matrix remodeling and persistent cell signaling, but challenges include homogenous delivery of cells onto a large, non-flat wound topography. Although approaches such as cell spraying and microparticle injecting have been explored in the field, the continuous formation of three-dimensional, hydrogel-based tissue constructs uniformly on a physiological wound surface remains unsolved. Here, we report the development of a handheld bioprinter which delivers wound-conformal dermal tissue constructs to improve wound healing in full-thickness burns. Mesenchymal stromal cell (MSC)-containing fibrinogen bioink and thrombin crosslinker solutions were delivered through on-board syringe pumps to a microfluidic printhead with internal bifurcated channels. Dermal tissue constructs of consistent thickness covered with the crosslinker were obtained at the exit. Wound-conformal delivery of these MSC-laden dermal tissue constructs was achieved by translating the printhead along the wound surface by a soft silicone wheel, while a two-axis gimbal design allowed it to adapt to the wound topology. We observed that the addition of 1% hyaluronic acid (HA) provided desirable shear-thinning behavior of the bioink (1.2 Pas at shear rate 1/s; 0.35 Pas at shear rate 100/s), resulting in 83% of the starting thickness to be maintained for deposition surfaces with inclination angles of 45 degrees. Furthermore, these fibrin-HA hydrogels maintained high biocompatibility with the co-delivered MSCs ( >94%), in addition to long-term preservation of 3D morphology and cell proliferation as shown with Hoechst/Phalloidin+ immunostaining over one week. To demonstrate the clinical utility of this approach, we uniformly distributed 1x10^6 MSCs/ml of the fibrin-HA hydrogel on a porcine 5cm x 5cm full-thickness burn wound model and quantified a 1.4-fold improvement of macroscopic re-epithelialization speed, a 1.3-fold increase in collagen density in the dermal layer, and a 2.5-fold reduction in CD11b+ inflammatory cell activity after 28 days compared to burn controls, as observed via microscopic analysis of H&E histological stains. Taken together, we have shown that the handheld bioprinter can conformally deliver MSC-containing dermal tissue constructs directly on wound substrates with physiological topographies, leading to full thickness burn wound repair as shown in porcine pre-clinical case studies.
Generation, Validation and Application of Induced Pluripotent Stem Cell Models for Functional Genomics
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Open to view video. Myleoid cells play critical roles in adaptive and innate immunity and dysregulation can result in disease pathology, such as neurodegeneration. However, a detailed mechanistic understanding of human myeloid biology has been hampered by the lack of robust and scalable models for cellular and genetic studies. Conventional approaches rely upon immortalised cells that lack biological relevance or primary cells which are limited in number, reproducibility, and genetic perturbation. To overcome these challenges, we developed and industrialised a human induced pluripotent stem cell (iPSC)-derived myeloid platform that permits a robust and continuous supply of progenitors that are subsequently differentiated into macrophage or microglia. Since each iPSC line retains the genetic information of the donor this provides an opportunity to harness human genetics to investigate in vitro disease mechanisms. We performed extensive transcriptomic, epigenetic, proteomic and metabolomic analyses with concomitant phenotypic (e.g. flow cytometry, image analysis) and functional assays (e.g. phagocytosis, cytokine secretion) to support their use as a model to primary counterparts. Here, we demonstrate a combination of conventional and innovative technologies to generate and validate iPSC-derived target cell types as an unlimited source of patient genotype-specific cells to study. We describe implementation of such disease relevant models to enable large scale (epi)genomic functional modelling for improved novel target ID. The human biological samples were sourced ethically and their research use was in accord with the terms of the informed consents under an IRB/EC approved protocol.
High-throughput organoid and monolayer platforms to study intestinal physiology
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Open to view video. Intestinal organoid technologies have revolutionized culture models to study physiology, disease, and injury in vitro. While primary stem cell-driven organoid cultures offer many improvements over conventional cancer cell line models, individual organoids are highly heterogeneous in lineage ratios, morphologies, growth properties, and other physiological parameters. Additionally, the enclosed lumen prohibits easy access to the apical cell surface to study nutrient absorption, the microbiome, and drug interactions with the epithelium. We have developed platforms that address these challenges. Specifically, our group focuses on engineering high-throughput systems to study single-cell stem cell biology, stem cell niche co-cultures, organoid dynamics, luminal physiology, and the microbiome. These platforms can be applied to organoids across any tissue type, are scalable, portable, and represent a high-resolution and statistically robust solution for preclinical models of human disease.
Application of Organ-on-chips and micro-physiological systems
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Novel oxygen-gradient platform for the co-culture of anaerobic gut microbiota with primary human colon epithelium
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Open to view video. Humans have co-evolved with their gut microbiota in a symbiotic relationship essential for health, yet how these thousands of bacterial species influence human biology remains little understood. A better understanding of the interplay between human cells and gut microbiota is required to exploit the complex relationships responsible for the local and distant effects of the microbiome on the human body. Accordingly, significant interest exists in the biotechnology community for improved in vitro models of the human gastrointestinal system, in particular models that support human-microbial co-culture. This feat is complicated by the fact that over 99% of gut bacteria are obligate anaerobes that die in 30-60 min after exposure to room air. Therefore, we have developed an easy-to-use and intuitive platform to replicate the steep O2 gradient across the in vivo colonic epithelium, thus create the appropriate environment required for anaerobes while maintaining viable, healthy epithelial tissue. We have computationally modeled, designed and prototyped the co-culture platform to fit within an SBS standard 12-well plate. The co-culture platform consisted of a basal reservoir and luminal reservoir with a porous polyester membrane and extracellular matrix (ECM) support dividing the two reservoirs. Using our culture methods, colonic epithelial stem cells were expanded on the ECM support and subsequently differentiated into all cell types found in the intestines in a monolayer ideal for compound screens and luminal stimulation/co-culture. Additionally, the ECM could be micromolded to recreate the physical architecture of the colon. Once the colonic epithelial layer was established, the luminal reservoir was sealed with an O2-impermeable barrier which resulted in the auto-generation of an anoxic environment ( < 2% O2) in the luminal reservoir within 8 hours by the O2 consumption of the epithelial cells. The basal chamber remained normoxic to supply the epithelial cells with O2 through the porous membrane and ECM support. The generation of a steep O2 gradient was measured and experimentally confirmed. The resulting O2 gradient allowed for anaerobes (lactobacillus rhamnosus GG) to be cultured in the luminal reservoir in contact with an oxygenated colonic epithelial layer. Colonic epithelium and anaerobic bacteria each maintained >90% viability when co-cultured for ≥3 days. Our co-culture platform is simple, robust, self-sustaining and easy-to-use. It does not require any fluidic and gas control systems. It is based on regular standard SBS microplate format that industry and academia use on a daily basis. Thus, it can be adopted in any microbiology laboratory without requiring new equipment.
Data Analysis and Informatics
SLAS2020 Innovation Award Finalist: Interpreting AI models trained on high-content microscopy data
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Open to view video. The increasing popularity of high-content screening (HCS) and phenotypic profiling in preclinical drug discovery is generating enormous amounts of complex imaging data. The flexibility of these imaging-based assays allows researchers to quantify many different biological processes using a single technology. Examples include examining nuclear translocation of proteins, internalization of receptors, and morphological changes in response to tens of thousands to hundreds of thousands of treatments. Despite the experimental throughput of HCS, analyzing and interpreting HCS imaging data remains a key bottleneck in utilizing these systems. Scientists often need to collaborate closely with computer vision experts and data scientists to extract informative measurements (i.e. features) from imaging data and design customized analysis pipelines for each new assay. Machine learning provides a unique opportunity to automate and accelerate many of the steps involved in analyzing HCS screens. Recent results have shown that deep learning, specifically deep convolutional networks (CNNs) trained directly on raw pixel data, outperform existing approaches at classifying and clustering cellular phenotypes. The tradeoff often associated with these methods is the lack of interpretability of predictions made by deep learning models. We’ve designed several novel machine learning process for HCS that prioritize interpretability, by highlighting regions in the image that are responsible for the model’s predictions. These models combine fully convolutional neural networks, typically used for image segmentation, with convolutional multiple instance learning (convMIL) to aggregate predictions spatially across fields of view. Additionally we’ve correlated predictions made by convMIL models with features extracted from individual cells using traditional feature extraction based analyses. Combining these two methods provides an additional layer of model interability by automatically indicating which features are changing most significantly between classes predicted by the CNN. Finally, we’ve developed a novel approach for exploring single cell phenotypes in HCS screens using weakly-supervised learning models combined with an interactive tool for exploring phenotypes. Weakly supervised models are CNNs trained to predict every unique condition in an HCS screen based on image crops of single cells. Once the model is trained, a feature vector is extracted for every cell in the screen based on outputs from intermediate layers in the CNN. These feature vectors are then converted to 2D using dimensionality reduction techniques like t-SNE and UMAP. The interactive scatterplot we’ve built allows scientists to explore this 2D space while being able to see what individual cell phenotypes look like and which treatment conditions are common in different clusters that appear. We’ve used this tool to discover antibodies and compounds that are active in multiple assays that can include multiple cell-types and 3D culture systems. Taken together, these approaches significantly accelerate and improve phenotypic discovery programs.
The Lab of the Future: Automation in the Digital Age
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Open to view video. "Lab of the Future" (LoF) has recently become a popular topic for modernizing laboratories. While performing upgrades to laboratory informatics systems such as ELN or LIMS and adopting "hyped" technologies such as IoT, AI/ML, AR/VR and blockchain may support modernization on the surface, many existing LoF strategies run the risks of only delivering incremental value. Your LoF strategies need to have a deeper impact to survive executive scrutiny and must put an augmented data analytics strategy at the center. A LoF strategy must also enable a digital twin for the lab at multiple levels- with impacts on lab assets, personnel, and systems. As businesses transform, all aspects of business, including the laboratory need to support digital optimization and transformation. In this session, we review the meaning of digitalization, the technologies important for achieving LoF, and outline strategic steps for ensuring your "LoF" strategy will be aligned to deliver true value in the Digital Era.
How advances in mobile, voice and AI technology are impacting scientists: The evolution of technology in the lab - University of California, San Francisco case study
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Open to view video. Scientists working today must navigate very large and complex datasets and work within regulatory boundaries that are tighter than ever. In order to meet the needs of modern scientists, lab documentation and management systems have had to evolve from simple pen and paper to flexible, integrated digital tools. We are at an era in which technology is at our fingertips, and having new lab automation tools like a voice powered AI digital lab assistant that allows integration of multiple functionalities within a laboratory annotation system greatly simplifies research workflows. Ernesto Diaz-Flores is an Assistant Adjunct Professor at UCSF who works with his team of scientists in the lab to study novel therapeutic targets for high-risk subtypes of childhood leukemia. New technologies such as voice powered AI digital assistants enable scientists at the UCSF lab to take voice notes, upload photos of experiments in real time, set up several reminders throughout the day and even dictate what reagents they need in their shopping list, and have it all immediately added to their e-lab notebooks. There’s also been less human error, scientists can capture and access more information at the point of experimentation hands free. When eyes and hands are occupied on the experiment, their voice can make the observations and capture the information in real time with digital lab assistants. As mobile, voice and AI technology evolves, there are now new options for scientists that seamlessly integrates with lab equipment and other data sources. The developments in mobile, voice and AI/machine learning technology are playing an important role in helping scientists bring their innovations and discoveries to market, improve efficiencies in the lab and make their work more reproducible.
The hyperloop for the lab: An integrated approach for sample delivery and treatment of culture dishes
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Open to view video. Digitalization, Automation and Miniaturization currently change the way we live and work. It also affects the daily work in laboratories creating what we perceive as the Lab 4.0 or the Lab of the Future. The disruptive development of new technologies such as open source automation technology, the Internet of Things (IoT) and 3D-printing offer endless possibilities for rapid engineering of new laboratory devices, which are compact, adaptable and smart. In conjunction with automated 3D-image analysis or deep learning algorithms, powerful instruments emerge to create and resolve research data.At the SmartLab systems department the PetriJet31X hyperloop technology was developed to automate all processes associated with culture dishes in environments such as routine laboratories or culture development for the next generation of antibiotics.The device technically is a x-y-robot consisting of two linear axles enabled to transport all kinds of culture dishes from A to B through a 3D-printed gripper-system which can also remove the lid of the culture dish. The platform has been extended with a rail system and a small cobot with the ability to transport piles of culture dishes or other laboratory material throughout the lab. Core part of the programming is a self-learning control software that does not need any teaching – the most time-consuming part of setting up a typical robot. With the presented solution an experiment conducted on samples is planned only once and executed for all culture dishes in the machine with the right processing station installed – e. g. 3D sample imaging and analysis. It is no longer necessary to specify locations for culture dish piles and treated dishes get allocated dynamically and drawn e.g. from the incubating chamber while user interactions are directed by LED-lighting. The system can process more than 1.200 culture dishes in an 8-hour shift and is equipped with a storage unit for these culture dishes.One example of the benefit of the PetriJet hyperloop can be found in routine labs for water and food inspection. Large numbers of samples get incubated on specific medium in culture dishes and are visually inspected regularily. Our system directly receives the tasks from the laboratory information and management system (LIMS), creates job lists and provides analytical data to the lab assistants through the LIMS. The data can then easily be turned into result sheets right from the desk. Unique feature of the system is that it can operate the night shift with no staff present.The PetriJet31X hyperloop platform now operates at the Chair of Microbiology at the TU Dresden for screening of new antimicrobial substances and the next generation of antibiotics. The system enables biologists to screen agent combinations faster and use the gained image data to feed new deep-learning algorithms.
Lab of the Future Panel Discussion
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Open to view video. Panelists: Michael Shanler, VP Research, Gartner Inc. Guru Singh, Head of Growth, Lab Twin Ernesto Diaz Flores,PhD, Assistant Professor, UCSF Christoph Otto, Dipl.-Ing. PhD Student, TU Dresden Moderator: Umesh Katpally, PhD, Director - Data Advisory, BC Platforms
Enabling "Bench-to-Bedside" with FAIR data
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Open to view video. Translational Medicine (“bench-to-bedside”) is a multidisciplinary field focused on developing new therapies and procedures that extend and enhance the human life. Collaboration with internal and external labs is at the very core of this effort which poses numerous challenges in fulfilling its promise. Biomarker data from thousands of patients and multiple indications needs to be collected, collated with clinical data, analyzed and visualized. Data needed to gain insights is often hard to find, incomplete, opaque, lacks conformance and governance. Bristol-Myers Squibb has embarked on a set of bold strategic initiatives dubbed “Digital Health - Sage” to tackle these challenges head on by using FAIR (Findable, Accessible, Interoperable, Reusable) data principles as a guide. Five key capabilities were delivered as part of the Digital Health – Sage effort: data lake, data catalog, analytics environment, search and visualization, data access and governance. The presentation will detail business challenges, technology solutions, and lessons learned.
Machine Learning is the Easy Part
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Open to view video. Data silos are prevalent across healthcare organizations, with challenges from people, process and technical capabilities. Once that data is shared and in a usable format, there's many new machine learning capabilities that can unlock improved data-driven decision making and shared context. This presentation will cover some of the typical challenges to data sharing, strategies for overcoming, and opportunities for advanced analytics once available.
Drug Target Strategies
Leveraging intrinsic target degradation
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Open to view video. Protein degradation is a very effective way to inhibit target activity and prevent its scaffolding function. At Cedilla, we are focusing on small molecules that directly or indirectly regulate the homeostasis of a protein of interest. This presentation will cover some of our strategic approaches to direct and indirect degradation and the contribution of biomolecular sciences (biophysics, biochemical, structural biology, molecular dynamics…) to this novel path in drug discovery.
A Homogeneous Cell-Based Membrane Potential Assay to Identify Compounds That Promote Readthrough of Premature Termination Codons in the Cystic Fibrosis Transmembrane Conductance Regulator Ion Channel
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Open to view video. Cystic fibrosis (CF), an inherited genetic disease, is caused by mutation of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene, which encodes an ion channel involved in hydration maintenance via anion homeostasis. Nearly 5% of CF patients possess one or more copies of the G542X, which results in a stop codon at residue 542, preventing full-length CFTR protein synthesis. Identifying small molecule modulators of mutant CFTR biosynthesis that affect “readthrough” of this stop codon, or premature termination codon (PTC) to synthesize a fully functional CFTR protein represents a novel target area of drug discovery. We describe the implementation and integration for large scale screening of a homogeneous, miniaturized 1536-well functional G542X-CFTR readthrough assay. The assay utilizes HEK293 cells engineered to over-express the G542X-CFTR mutant, whose functional activity is monitored with a membrane potential dye. Cells are co-incubated with a CFTR amplifier and CFTR corrector to maximize mRNA levels and trafficking of CFTR, such that compounds that allow translational readthrough and synthesis of functional CFTR chloride channels will be reflected by changes in membrane potential in response to cAMP stimulation with forskolin, and CFTR channel potentiation with genistein. Assay statistics were excellent with Z’ values of 0.69±0.06 despite a S:B of 1.19±0.04. As further evidence of HTS suitability, we completed automated screening of 666,120 compounds, identifying 7,761 initial hits. Following secondary and tertiary assays, we have identified 188 confirmed hit compounds with low and sub-micromolar potencies. Thus, the assay has integrated the advantages of a phenotypic screen with high throughput scalability to identify new small molecule G542X-CFTR readthrough modulators.
Monovalent Versus Bivalent Degraders
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Open to view video. The emerging drug design strategy based on inducing target protein degradation offers the potential of drugging classes of proteins not previously thought to be druggable. Furthermore, the magnitude of effect for these agents is not limited by receptor occupancy and the duration of effects can persist beyond drug exposure. The current design of protein degraders is more commonly based on bivalent molecules, which consist of a ligand for the target protein linked to a ligand for a ubiquitin ligase (such as VHL, CRBN or XIAP). Due to their bivalent design, such drugs typically have a higher molecular weight than classic small molecule drugs and may present some non-ideal properties as drugs. An alternative monovalent degrader strategy is exemplified by the group of drugs termed SERDs (selective estrogen receptor degraders), for example fulvestrant. The molecular structures of SERDs are typically designed around a receptor ligand and a “degradation tail”, whose presence results in the degradation of the estrogen receptor. We have recently reported that this monovalent strategy can also be applied to the bromodomain and extra-terminal (BET) family, with the example of the monovalent BRD4 degrader GNE-0011. We will use examples of monovalent and bivalent degraders of BRD4 to compare these two complimentary degrader strategies.
CETSA®-HT – enabling a new paradigm in Hit Discovery
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Open to view video. High throughput screening (HTS) cascades have evolved to ensure that high quality hits can be identified from large screening collections. Traditionally, most primary screens focus on the identification of modulators of catalytically active sites, while target engagement assays are placed further down the cascade. Well established technologies like competition-based assays, affinity selection technologies or differential scanning fluorimetry (DSF) depend on availability of protein which is tested in a non-native biochemical setting. Therefore, one of the main concerns when initiating an HTS cascade remains the demonstration of target interaction within a relevant cellular environment. The use of cellular assays during primary screening and the HTS cascade presents an alternative. However, cell-based screens can easily become very complex, risk off-target effects and thus often require time consuming target deconvolution of pathway hitters. To date there has been no single technology that can demonstrate cellular target engagement in a suitable format for HTS primary screening. The cellular thermal shift assay (CETSA®) can act as an interface between this classic biochemical-cellular screening dichotomy. CETSA® facilitates label free screening in disease relevant cells while approaching the ease of biochemical assays. In an isothermal setup, full assay plates are heated to a setpoint within the target protein melting curve. While most proteins unfold and precipitate upon this heat-shock, a characteristic of protein-ligand interaction is induced thermal stability. The remaining stabilized protein can subsequently be detected with a pair of anti-species antibodies in an AlphaScreen® system. This high throughput (HT) CETSA® format allows large numbers of compounds to be tested in an HTS setting. Here, we report the development of two CETSA®-HT assays along with the application of this technology in HTS for the first time. This has been enabled following the recent agreement between Pelago Bioscience and PerkinElmer to streamline CETSA®-HT into validated kits and to offer support in the assay development. In the Global High Throughput Screening Centre of AstraZeneca we are exploring the potential and the feasibility of CETSA®-HT for large scale HTS campaigns ( >0.5M compounds). These datasets provide an indication of the future impact CETSA®-HT will have in hit identification. This is particularly timely given the expanding interest across drug discovery groups in new target protein classes. With new modalities like PROTACS (proteolysis targeting chimera) non-catalytically active proteins can now therapeutically be targeted. Utilising CETSA®-HT to identify target engagement in cellular environments early during primary screening could shift the paradigm of hit finding.
Targeting engagement by utilising CETSA in drug:target studies
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Open to view video. Target engagement is a fundamental paradigm in drug discovery. A significant portion of projects fail to reach the clinic due to lack of efficacy or failure to show the lead candidate is interacting with the intended target in a more complex environment. Using the proven CEllular Thermal Shift Assay (CETSA) technology to measure target engagement in various matrixes, such as tissues, intact cells or lysates is increasingly common for SAR studies and lead optimisation. It is also possible to inform early stage programs with CETSA technologies and investigate in-situ ligandability. This study looks to investigate a well-established oncology target using CETSA high throughput platform (HT). CETSA HT measures direct target engagement through interaction of the protein and molecule after a heat challenge, with versatile readouts applicable to HTS and miniaturisation for robotic platforms. Assay development for CETSA HT includes development of HTS off-the-shelf kit formats and in this project, investigations into target engagement of fragments within a cellular environment. These target engagement studies are not reliant on in-vitro and often abstract functional screens or methods, often limiting to certain protein classes.
Micro- and Nanotechnologies
SLAS2020 Innovation Award Finalist: Enabling precision medicine: pipetting at single cell resolution
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Open to view video. Single cell isolation is essential in stem cell biology, cancer research and biotechnology among others. For example, to ensure quality, safety and efficacy of the biotherapeutic product, companies shall demonstrate that each new recombinant cell line has been cloned from a single progenitor cell (WHO, 2014). Because available methods for cell cloning do not provide fully traceable cells yet, companies may waste up to 50 weeks in clonal validation. To solve this issue, we have developed an automated impedance-based pipetting robot for single cell dispensing, allowing for traceable cloning of single cells. This technology permits the efficient and gentle isolation of industrial cell lines as well as rare and fragile stem cells and cancer cells, at a single cell resolution, so that cells can be individually expanded in culture, transplanted downstream or analyzed by omics assays. We will present the technology and illustrate its key features through various case studies.
SLAS2020 Innovation Award Finalist: An Adaptable Microfluidic Platform for Single Cell Pathogen Identification and Antimicrobial Susceptibility Testing
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Open to view video. Bacterial infections, such as bloodstream infections (BSI), ventilator-associated infection (VAI), and urinary tract infections (UTI), are a common cause of patient morbidity and mortality. Rapid identification of the causative pathogens and their antimicrobial susceptibility profiles will improve the clinical workflow for clinical management, accelerate clinical decision-making, and improve patient outcomes. However, definitive clinical microbiological analysis of samples obtained from patients requires several days, hindering proper management of infection and driving the overuse and misuse of broad-spectrum antibiotics. Novel precision technologies for rapidly identifying the pathogens and their antibiotic resistance are highly sought-after. To address this clinical unmet need, we develop a nanotube assisted microwave electroporation (NAME) technique for intracellular detection of species-specific bacterial 16s rRNA in 30 minutes. NAME allows amplification-free pathogen identification at the single cell level. Unlike typical sensing techniques that lyse the bacteria and dilute the intracellular content, NAME directly detects species-specific regions of the 16S rRNA inside the cells. Due to the small volume of a bacterium, the target molecule in the cell has a high effective concentration, which creates a strong signal for single cell detection without amplification. Intracellular detection of bacterial 16S rRNA in viable cells also facilitates subsequent antimicrobial susceptibility testing (AST). By incorporating an adaptable microfluidic design, we demonstrate a phenotypic AST system that rapidly determines the existence of bacteria, classifies major classes of bacteria, detects polymicrobial samples, and identifies antimicrobial susceptibility directly from clinical samples at the single-cell level. The adaptable microfluidic system can dramatically accelerate the workflow of the microbiological analysis. Pathogen classification, which is based on microfluidic separation and microscopic inspection, eliminate the slow culture step. This approach rules out negative samples, classifies bacteria according to size and shape in as few as 5 minutes, and identifies samples with multiple pathogens for polymicrobial infection diagnosis. By monitoring the bacterial growth directly, AST results can be reported in as few as 30 minutes or in a time scale similar to the doubling times of the bacteria. In this study, we report the integrated microfluidic system for rapid pathogen classification and AST. We demonstrate the NAME technique for identifying bacteria that commonly cause BSI, VAI, and UTI. In collaboration with our clinical and industrial partners, we are developing an integrated ID-AST platform for rapid diagnosis of bacterial infections. We pilot a study of 25 clinical urine samples to demonstrate the clinical applicability of the microfluidic system. The platform demonstrated a sensitivity of 100% and specificity of 83.33% for pathogen classification and achieved 100% concordance for AST. Our results demonstrate the analytical and clinical feasibilities of the integrated ID-AST platform for rapid microbiological analysis.
SLAS2020 Innovation Award Finalist: A Benchtop Biochemical Analyzer: Microchip Capillary Electrophoresis Coupled to High Pressure Mass Spectrometry (HPMS)
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Open to view video. Biochemical analysis needs are frequently addressed using liquid chromatography coupled to mass spectrometry (LCMS) in a centralized laboratory setting. While these systems can be quite versatile to address a broad range of biochemical measurement problems, they are correspondingly complex and require a trained operator to produce results. LCMS instrumentation also typically occupies a large footprint and requires utilities beyond a simple power outlet. Our laboratory has been pursuing miniaturized versions of liquid phase separation systems and mass spectrometers for over two decades. We are combining these two technologies to demonstrate a compact benchtop analyzer that can address measurement needs in areas such as cellular biology, clinical diagnostics, and biopharmaceutical research and development that would normally be accomplished using LCMS. We have developed microfabricated capillary electrophoresis (microchip CE) devices with monolithically integrated nano-electrospray ionization (ESI) emitters that exceed the performance of conventional CE-ESI implementations. CE separations require ionic analytes, whereas LC can potentially separate either charged or neutral compounds. Biochemical species of interest are predominately ionic and CE systems outperform LC systems for separative performance, while the former can also be implemented more compactly with simpler components, e.g., voltage sources versus high pressure pumps. Microchip CE has been used to separate ions as small as elemental species to intact monoclonal antibodies. One million theoretical plates of separation can be generated in one to a few minutes. Moreover, the microchip CE cartridge is easy to use and does not require any plumbing to connect the ESI emitter. We have also been involved in the development of a new form of mass spectrometry, HPMS, that can be implemented in a compact form as it operates at pressures several orders of magnitude higher than conventional MS, i.e. approximately 1 Torr. Operating at such pressures allows significant simplification of the vacuum system and the use of a vacuum pump that can rest in the palm of your hand. The mass analyzer in HPMS is a form of ion trap with sub-mm scale critical dimensions. We have theoretically and experimentally demonstrated that HPMS resolution can be increased by decreasing critical dimensions and correspondingly increasing the RF drive frequency. In this presentation, we will describe microchip CE and HPMS and the coupling of the two technologies to create a compact and useful biochemical analysis tool. The instrument implemented with a 96 well plate autosampler is approximately the size of a tower computer. Example applications such as monitoring bioreactor broth constituents will be presented.
Microscale Linear Ion Trap for Portable Mass Spectrometry
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Open to view video. We present initial results from a high-aspect-ratio linear ion trap employing 20-micrometer-wide electrodes patterned onto ceramic substrates, with a characteristic trapping dimension of 800 micrometers. In previous efforts we showed that a variety of ion trap geometries can be made using assemblies of two ceramic plates, the facing surfaces of which are patterned with appropriately shaped electrodes. The present report shows significant miniaturization of this approach. Mass spectra of organic compounds with this device have resolution of 2-3 amu. These highly miniaturized analyzers are now being developed for portable GC-MS instrumentation. Aluminum electrodes were deposited onto one side of each ceramic substrate. Electrodes are wire-bonded to a printed circuit board, which connects with a capacitive voltage divider. Two plate-PCB assemblies are mounted in a sandwich configuration, with the trapping fields being established in the space between the plates. Prior to patterning, a tapered ejection slit, 166 micrometers wide, was laser-cut into each substrate for ion ejection. The taper is critical to prevent ions from striking the inner wall of the slit and building up space-charge, while allowing the thickness of the substrate to remain sufficiently thick for strength. Dipole resonant ejection of ions, in which the applied ejection waveform is phase-locked with the drive RF, was demonstrated by the use of special phase-tracking circuit. Alignment of the substrates was demonstrated using a set of 4 micropositioners (three linear and three angular). Low-power performance—essential for portable and hand-held mass spectrometers—was also demonstrated, with a maximum RF amplitude of 400 V at the highest point in the scan. Typical mass resolution of small organic compounds (toluene, xylenes) is 1.5 Da. Experiments using high molecular weight compounds (octofluorotoluene and perfluorotributylamine) showed typical mass resolution of 2-3 Da. The effects of higher operating pressure on mass spectra were also examined. Resolution decreased at pressures above 5 mTorr, but suitable spectra could still be obtained at pressures of up to 42 mTorr. Resolving power is decreased compared with the larger scale version of this device, possibly due to increased space charge. However, the signal to noise ratio is large due to the high aspect ratio of these traps—the ratio of the length to the characteristic trapping dimension is greater than 40, providing a large trapping volume.
A Small Footprint Ambient Ionization enabled High Throughput Chemical Detection System
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Open to view video. Rapid analysis of the products of chemical reactions produced in high throughput experiments (HTE) are completed by thermal desorption of sub-microliter volume samples into an ionizing gas. The heated ionizing gas completes vaporization of sample typically present in dimethyl-sulfoxide in 1-3 seconds per sample with rapid mass detection.The utility for Direct Analysis in Real Time (DART) for ionization of chemicals in the presence of aprotic solvents such as DMSO, and DMF has been employed to enable detection of those chemicals from sub-microliter volumes of sample thus eliminating the need for sample dilution prior to analysis by LC/MS. The sub-microliter samples have been prepared by using several sample disposition method including low volume automated pipettor station and a high capacity disposable pin-tool. Using these devices we have chemicals present in concentrations appropriate for high throughput experiments have been deposited onto a wire mesh surface which is then positioned between the DART source exit and the mass detector entrance for analysis. Use of small volume samples reduces the potential for matrix effiects by limiting the abundance of chemicals present in the ionizing gas. Rapid sampling of the small size droplets present on the sample supporting wire mesh enables continuous screening of the samples. We document the performance of each sample method at analysis 1 per second to demonstrate the potential for full 386-well sample plate analysis in under 10 minutes. Automated data analysis of the continuous collection of spectra in the data file is demonstrated using a file parsing software in order to permit archival of the results. An outline of the overall workflow and it utility for simplifying the analytical effort in support of nanochemistry will be discussed.
A Portable Quality Control Lab in The Era of Food and Beverage Craftsmanship
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Open to view video. In the past decade, we have observed a tremendous growth of interest in food and beverage craftsmanship due to new trends for sustainability and eco-friendliness. This has resulted in numerous exciting initiatives in food and beverage production. However, these initiatives have limited resources and thus struggle to maintain consistently high-quality processing. Preventing losses, financial and otherwise, caused by unpredictable events such as microbiological contamination is often challenging. Traditionally, these problems are tackled by quality-control (QC) protocols embedded in the production process. However, the implementation of QC often requires trained personnel and professional analytical equipment, with associated costs well beyond the budgets of small entrepreneurs. Therefore, it is a great opportunity for the rapidly growing field of portable (bio)chemical analysis to step in and offer a viable solution for this market. However, tests are often developed for trained chemists who are capable of proper sample acquisition and data interpretation. We propose an alternative technology that can accommodate common colorimetric tests, in a portable format that can be used by non-chemists. Moreover, the technology provides shorter sample-to-answer times for samples collected and analyzed directly at the production site by the craftsman. To achieve this, we utilize a patented sample concentration technology that enables quantitative analytical tests with enhanced sensitivity, combined with a sample acquisition system and data interpretation software. The sample concentrator used in this work was developed by rapid prototyping with a stereolithographic 3D-printer. The concentrator cartridge consists of a sample acquisition module for volumetric sampling with subsequent liquid transfer to a porous particulate column packed into a 3D-printed cartridge. A series of branched air ducts embedded in the sample concentrator guide pressurized air from a simple gas supply to a selected region of porous membrane fixed into the bottom of the cartridge. Liquid samples are reacted with reagents in the 3D cartridge and then concentrated on the membrane through evaporation of solvent by the gas. Analysis is subsequently carried out by colorimetry. Integrated software analyzes the test results and compares it to results stored in our database, so that every craftsman can use our system without the need for extensive chemical training. Additionally, our cartridges provide these tests with better protection against contamination, with improved user-friendliness, and the possibility of combining multiple materials for one test. The described technology has been applied for QC testing in different branches of the food and beverage industry. Our technology contributes to the market of portable analysis because it provides a tool that can be applied outside an analytical laboratory, but with comparable results. This means shorter times between sampling and result, and, importantly, provides substantially better options for the small food-and-beverage entrepreneur to realize improved QC.
Molecular Libraries
Library Of Compounds with Really Annoying Pharmacology (LOCRAP)
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Open to view video. False positive results have long been the bane of High Throughput Screening (HTS) campaigns. Depending on the compound library tested, the target itself and the assay system employed these have been estimated to account for enrichments of up to 95 % in hit outputs. Many sources of undesirable hits exist whether they be technology artefacts, redox cycling compounds, inhibitors of coupled enzyme systems or any other from a myriad of mechanisms. One strategy to cope has been the development of so called ‘nuisance compound’ sets. Over the past decade numerous pharmaceutical companies acting in isolation, including AstraZeneca, have generated their own versions. Whilst the rationale, composition, source and application of these decks has varied between organisations the end goal has remained constant. Each group has aimed to either minimise the prevalence of undesirable actives by optimising assay design or build bespoke triage cascades capable of identifying them. This presentation details the process by which the AstraZeneca set was established and shows the impact it’s had on drug discovery projects. We explore how it differs from those created by some of our peers and finally introduce a new cross industry and academia initiative called LOCRAP. This Library Of Compounds with Really Annoying Pharmacology (LOCRAP) is a joint collaboration between AstraZeneca, Eli Lilly, Novartis, Pfizer, Broad Institute, NCATS and lead academics in the area Jonathan Baell (Monash University) and Mike Walters (University of Minnesota). By pooling our collective knowledge, the intention is to design and deliver an industry standard collection that will be available to all through a third-party commercial partner. This new set will cover multiple classes of problematic compounds and contain thoroughly validated and well annotated examples. We’ll discuss how this has been achieved and what classes are currently included. We are actively seeking contributors with ideas on compounds to include or to act as beta-testing supporters once the collection is available. Our hope is to make the tools and resources available to large pharmaceutical companies an option for any organisation interested in drug discovery no matter how large or small. From this arises the aspiration to globally improve the quality of assays employed for hit identification and subsequently success rates for discovery campaigns.
Doubling Down: Betting on the success of HTS & DEL libraries in parallel
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Open to view video. High-throughput screening (HTS) libraries and DNA-encoded libraries (DELs) are two key repositories from which active hit compounds can be identified. FORMA has implemented a screening paradigm that enables HTS and DEL in parallel to enhance the likelihood of discovering hits for a particular target. Utilizing both methodologies in parallel allows us to thoroughly sample an expanded chemical space compared to either individually as well as evaluate potentially unexpected mechanisms of action (MOA) for each target. We will present our current success using the combined approach as well as a retrospective analysis of how expanded chemical space and novel MOAs could have further enhanced our past success delivering lead compounds.
Automated chemistry – from an idea to reality
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Open to view video. The discovery of bioactive small molecules is generally driven via iterative design-make-purify-test cycles. Automation is nowadays routinely used at the purify and test stage of these cycles but still very rarely at the design and make stage. However, recent advances in areas such as microfluidics-assisted and batch chemical synthesis as well as AI systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into all aspects of this process. Here, we describe recent progress we have made with the build of a fully automated synthesis platform comprising all aspects of the make-purify workflow. A variety of chemistry transformations have been established on the platform allowing not only the rapid synthesis of compound libraries but also complex molecules via multistep sequences. The value and impact of our platform is illustrated in the context of specific case studies from the early phase drug discovery. We also consider the longer-term goal of realizing the fully autonomous discovery of bioactive small molecules through the integration of our automated synthesis platform with automated design and testing.
Exploring Reliable and Cost-effective DNA-encoded Library Approaches for Developing Active Compounds
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Open to view video. Tagging combinatorial libraries with DNA barcodes allows using a simple affinity selection protocol to rapidly identify protein binders. A primary challenge with such DNA-encoded libraries (DELs) is how to design them to provide the effective screening productivity needed for the routine discovery of developable hits. The predominantly pursued approach is to make platforms of very large libraries of chemically complex compounds. While many success stories have proven the validity of this paradigm, it is unclear whether such DELs compare favorably to competing technologies with regard to return-on-investment. Moreover, the often heterogeneous synthesis yields of very large libraries and undersampling of DNA-barcodes impedes effective hit triaging. The cost of producing large DEL platforms and identifying hits from screening data is completely prohibitive for laboratories with limited resources. We therefore explore alternative library designs with the goal of finding active compounds at lower DEL synthesis and lead-development costs. We custom-design DELs for specific target classes, and early studies have demonstrated that such libraries provide hits rapidly and economically. For example, a small and chemically simple DEL targeting NAD+-binding sites provided potent and target-selective hits for ADP-ribosyltransferases. Possible strategies for advancing early screening hits from such chemically simple libraries will be discussed.
Applying Artificial Intelligence and Machine Learning Techniques and Cross Platform Communication to Enable Informatics Driven Experimentation
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Open to view video. Over the past decade there has been a shift away from the traditional static method of performing high throughput screening (HTS) against large chemical libraries where experiments are designed in advance, executed and then the generated data is processed. Traditionally, this results in additional rounds of biological validation, testing and lead compound follow up for medicinal chemistry. More recently there has been a movement to focus instead on an increased number of targeted chemical libraries and smaller initial HTS experiments which can be run in a more dynamic fashion with the resultant data processed automatically and in near real time to initiate new biological experimentation and even automated chemical synthesis on the fly. To make this possible it is necessary to have an underlying software and messaging infrastructure that can connect informatics platforms that utilize Artificial Intelligence and Machine Learning techniques to design experiments that can then be transferred to physical systems to initiate new experiments or small molecule synthesis. NCATS has developed such a platform with the initial validation being used to perform dynamic assay optimization but which is extensible to far more complex experimentation types to move beyond automation and instead towards autonomy.
Case studies in AI-driven drug design
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Open to view video. Breakthroughs in machine learning theory and practice, coupled with ready access to cloud based supercomputing resources and ever-increasing amounts of experimental data, are enabling truly AI centric processes for small molecule drug design wherein predictive models successfully substitute for laboratory assays throughout the Discovery critical path. This presentation will describe how diverse applications of machine learning techniques, ranging from multidimensional and multitask boosting to deep neural networks, can extract accurate, scaffold independent, ligand based predictive models for important phenomena: target binding, functional activity, selectivity, PK/ADME properties, and toxicity. Applications of these models will be illustrated with examples from therapeutic programs and discussed in terms of their potential to enhance success/reduce attrition in drug discovery.
Precision Medicine Technologies
Toward Precision Medicines for Rare Diseases
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Open to view video. Intracellular accumulation of misfolded proteins causes toxic proteinopathies, diseases without targeted therapies. Mucin 1 kidney disease (MKD) results from a frameshift mutation in the MUC1 gene (MUC1-fs). Here, we show that MKD is a toxic proteinopathy. Intracellular MUC1-fs accumulation activated the ATF6 unfolded protein response (UPR) branch. We identified BRD4780, a small molecule that clears MUC1-fs from patient cells, from kidneys of knockin mice and from patient kidney organoids. MUC1-fs is trapped in TMED9 cargo receptor-containing vesicles of the early secretory pathway. BRD4780 binds TMED9, releases MUC1-fs, and reroutes it for lysosomal degradation, an effect phenocopied by TMED9 deletion. Our findings reveal BRD4780 as a promising lead for the treatment of MKD and other toxic proteinopathies. Generally, we elucidate a novel mechanism for the entrapment of misfolded proteins by cargo receptors and a strategy for their release and anterograde trafficking to the lysosome.
Liquid biopsy as a tool in anticancer drug screening for guiding personalized therapy decision and drug discovery
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Open to view video. The use of circulating tumor cells (CTCs) isolated from liquid biopsy are already used to predict disease progression and survival in metastatic patients. However, the lack of robust drug screening assays has hampered their application in monitoring patient drug response/resistance and personalized therapy decision. We have developed a workflow to isolate tumor cells from pleural effusion and malignant ascites samples from metastatic lung and breast cancer patients and subjected them to medium scale drug screens against approved anticancer drug libraries. This approach allows realization of personalized treatment decisions within less than a week by evaluating drug responses directly in patient-derived tumor cells obtained from liquid biopsy. In patients with no pleural effusion or malignant ascites, we have established another workflow to isolate viable CTCs from peripheral blood of metastatic patients, from which we have generated 2D and 3D in vitro (spheroids and organoids) and in vivo (CTC-derived xenografts) models. Particularly, drug screens on CTC-derived organoids was feasible within therapeutic timeframes which can potentially influence personalized treatment strategy. Drug responses from the screen mirrored patients’ drug resistance and revealed promising candidates for treatment of individual patients. Beyond that, high-throughput drug screens in CTC-derived preclinical models closely mimicking patients' setting enable discovery, repurposing and development of more efficient cancer therapeutics. Integration of drug screening of liquid biopsy-derived tumor cells constitutes a powerful tool to better improve personalized treatment strategies and discovery for metastastic patients.
Villages in a Dish: Scaling the use of human cell models to detect drug-genotype interactions
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Open to view video. A maturing application of reprogramming and stem cell technologies is their application to understanding how genetic variation that underlies disease risk impinge the function of affected cell types. However, a major limitation of this approach has been the number of patients and genetic variants that can be reasonably analyzed. I will describe a new strategy we have developed that allows us to simultaneously measure phenotypes in cell types derived from as many as 100 individuals in a single tissue culture well. These approaches, we call “Dropulation Genetics” and “Census sequencing” not only have allowed us to probe how genotype underlies phenotype at previously impractical scales, they have also provided a remarkable improvement in sensitivity and assay reproducibility. I will describe practical application of these approaches in psychiatry, neuromuscular disease and susceptibility to infectious agents.
Developing Epileptic Encephalopathy Models Using iPSC-Based Technologies
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Open to view video. Mutations in KCNQ2, which encodes a pore-forming K+channel subunit responsible for neuronal M-current, cause neonatal epileptic encephalopathy, a complex disorder presenting with severe early-onset seizures and impaired neurodevelopment. The condition is exceptionally difficult to treat, partially because the effects of KCNQ2mutations on the development and function of human neurons are unknown. Here, we used induced pluripotent stem cells and gene editing to establish a disease model, and measured the functional properties of patient-derived neurons using electrophysiological and optical approaches at single-cell resolution. We find that while patient-derived excitatory neurons exhibit reduced M-current early, they develop intrinsic and network hyperexcitability progressively. This hyperexcitability is associated with faster action potential repolarization, larger afterhyperpolarization, and a functional enhancement of Ca2+-activated K+(BK and SK) channels. These properties facilitate a burst-suppression firing pattern that is reminiscent of the interictal electroencephalography pattern in patients. Importantly, we were able to phenocopy these excitability features in control neurons only by chronic but not acute pharmacological inhibition of M-current. Our findings suggest that dyshomeostatic mechanisms compound KCNQ2 loss-of-function and lead to alterations in the neurodevelopmental trajectory of patient-derived neurons. Our work has therapeutic implications in explaining why KCNQ2 agonists are not beneficial unless started at an early disease stage.
Microfluidic platform for screening of antibiotic susceptibility at the single-cell level
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Open to view video. The inoculum effect describes a dependency between the minimum inhibitory concentration (MIC) of an antibiotic and the concentration of bacteria in the sample: the less the bacteria, the less concentrated antibiotic is needed to stop their growth. MIC for populations consisting of a single cell is known as single-cell MIC (scMIC). scMIC is important for public health, as the presence of antibiotic at concentration of scMIC in a large population of bacteria drives the evolutionary pressure towards resistant strains1, and the inoculum effect is a source of errors in MIC assessment in the clinic. However, efficient assessment of scMIC values for large numbers of cells has not been shown until now. Here, we demonstrate a method of determining scMIC values in hundreds of replications per experimental run, and we achieve this without optical labeling of the reaction conditions. We generate a series of emulsions of different concentration of antibiotic at a step emulsifier2. We encapsulate single cells in each emulsion droplet due to stochastic confinement. Each emulsion is separated from the others by being encapsulated in a third immiscible phase, and transferred to a piece of tubing, where all the separated emulsions can be incubated to provide for growth of bacteria. We measured the scMIC value of cefotaxime in E. coli for hundreds of cells, recording the inoculum effect when we used higher initial cell densities, and observing distribution of resistance level in a population of bacteria. Currently, we use our platform to generate up to 20 separate emulsions with different and known reaction conditions of ca. 2000 droplets each with immediate plans to upscale. In the near future we plan to screen for interactions of antibiotics in relation to inoculum effect, including the measurements at the single-cell level. The described method might be useful in the field of antibiotic resistance at a single-cell level, which is unbiased by the inoculum density. A microfluidic method of screening multiple chemical conditions in emulsions without labeling can be also deployed in other fields of research, wherever several reaction conditions should be replicated hundreds or thousands of times. For now3, to establish whether the bacteria grow or not, we detect fluorescence from fluorescent proteins produced by bacteria, but we are currently working on an add-on module to detect growth without labelling. We are also integrating our system with optical detection of moving droplets to automate the liquid handling protocol. 1 T. Artemova, Y. Gerardin, C. Dudley, N. M. Vega and J. Gore, Mol. Syst. Biol., 2015, 11, 822. 2 W. Postek, T. S. Kaminski and P. Garstecki, Lab Chip, 2017, 17, 1323–1331. 3 W. Postek, P. Gargulinski, O. Scheler, T. S. Kaminski and P. Garstecki, Lab Chip, 2018, 18
Overlap of fetal-specific cardiac regulatory variants and GWAS lead variants supports fetal origins of cardiovascular disease
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Open to view video. It has been hypothesized that many disease-causing variants exert their effects during development, rather than in adult cells. However, it is difficult to identify these variants and their effects as they could act in multiple different cell types, and there was a recent moratorium on research using fetal tissue. We recently established that iPSC-derived cardiovascular progenitor cells (CVPCs) are fetal-like, and can be utilized to identify cardiac regulatory variants. Here, we leveraged this system to identify fetal cell-type specific eQTLs that underlie GWAS signals for adult cardiac diseases. We started by characterizing the differentiation of iPSCs into iPSC-CVPCs via scRNA-seq on eight samples, and found they were comprised of two cardiac cell types: cardiomyocytes (CMs) and epicardium derived cells (EPDCs). Next, we derived 180 iPSC-CVPCs, performed bulk RNA-seq, and used the scRNA-seq expression signatures to deconvolute and determine the relative proportions of CMs and EPDCs in each sample. We integrated these data with WGS and identified cell type-specific eQTLs (associated with only CMs or EPDCs). We next identified fetal-specific eQTLs by colocalizing our iPSC-CVPC eQTLs with all GTEx adult cardiac tissue eQTLs. To identify variants underlying the fetal origin of complex adult cardiac traits, we colocalized these fetal-specific eQTLs with cardiac traits GWAS summary statistics (pulse rate and myocardial infarction), and found 10 fetal-specific eGenes, including CLPTM1 which has previously been associated with congenital malformations (as expected for a fetal-acting gene). Our findings provide genetic evidence supporting the fetal origin of cardiovascular disease and show that iPSC-derived tissues can be leveraged to study the fetal origins of diseases in relevant cell-types.
Keynote Presentations
Keynote: How AI is Disrupting Pharmaceutical R&D
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Open to view video. BenevolentAI is a British held company that is using AI to augment the research capabilities of drug scientists, radically changing the way R&D is done. The company spans all areas of R&D, from target identification and validation, through medicinal chemistry and lead optimization to clinical PoC. Through its proprietary platform, the company ingests vast quantities of biomedical data to augment the ability of scientists at all stages of the R&D process. This has led to significantly shorter lead optimization timelines and the generation of valuable new treatment hypotheses for serious diseases with high unmet medical need.
Keynote: Digitizing Chemical Synthesis and Discovery using Chemputers
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Open to view video. With one of the largest multidisciplinary chemistry-based research teams in the world, Dr. Cronin’s group at the University of Glasgow is developing the concept of ‘Chemputing’ which enables the universal digitization of chemistry, in order to create artificial life forms, find alien life, further explore the digitization of chemistry, understand how information can be encoded into chemicals and construct chemical computers. Unlike digital electronics and the Internet, which relies on open standards and formats allowing universal implementation, chemistry is in the dark ages. This is because chemistry relies on expert users to design, carry out and analyse chemical processes making universal reproducibility and automated discovery impossible. In our work we have developed the concept of ‘Chemputing’ which enables and exploits, the universal digitization of chemistry. The key to achieving that is finding a practically implemented approach to the translation of a chemical synthesis of known molecules, as well as enabling discovery of new molecules, making important molecules into a universally executable set of instructions or chemical programs.
Ignite Panel Discussions
Ignite Panel: Success Stories - Commercial Products from Ideas
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Open to view video. How do great ideas become new companies? How to avoid pitfalls on the perilous road of entrepreneurship? Join experts from business and tech as they answer questions and share their prowess.
Ignite Panel: How Diversity Drives Entrepreneurial Success
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Open to view video. Are organizations missing out on great ideas from within because of the monocultural group that initially reviews them? An active professional and social debate about “manels” (all-male panels), unbalanced review boards and advisory teams is underway. Join us to discuss the importance of diverse, inclusive groups to drive scientific ventures.
TRISH, Where Space and Health Intersect
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Open to view video. The Translational Research Institute for Space Health (TRISH) is a biomedical research institute advancing the frontier of where space and health intersect by bring emerging health care technology to the human deep space travel. Hear about adapting Earth-based technologies like astronaut self-diagnostics, radiation treatment, enhanced foods and non-pharmacological performance enhancement are being developed to support future Mars missions. This discussion is aimed at space technology enthusiasts and entrepreneurs interested in potential projects with TRISH.
Ignite Academic Collaboration Presentations
Ignite Academic Collaboration Presentation: Intelligent Microscopes Using Open-Source Hardware for High-Throughput Laboratory Automation
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Open to view video. Traditional microscopes used for automated imaging and analysis sets one aback with tens of thousands, if not hundreds of thousands, of dollars. This limits the number of microscopes a lab can afford, hence limiting the number of parallel experiments that can be performed. We present a novel approach by combining low-cost, low-resolution microscopes with advanced computational imaging methods that can extract high-resolution image information in post processing. In addition, we implement novel machine learning methods to jointly optimize the automation task, e.g. cell segmentation, and the data acquisition process, e.g. illumination pattern, to capture less data without losing the performance of the automated task. Our initial prototype costing ~$150 employed a Raspberry Pi as the computer and a modified Raspberry Pi V2 camera as the low-resolution microscope. A low-cost 16x16 LED array developed for display is used to illuminate the sample and 3D printed parts are used for assembly. LEDs in the array are sequentially illuminated to capture 256 low-resolution images, where the high-resolution information is encoded within these low-resolution images using the aperture synthesis concepts. The captured 256 low-resolution images were combined to achieve 0.8µm resolution, for the first time in a low-cost setting, across 4 mm The 3D-printed design of our microscope can be easily modified to the specific requirements of a lab, e.g. imaging stress, fibre reorientation in cells under mechanical stimuli require a different setup compared to imaging cell confluency in a petri-dish. Our optics and algorithms still stay valid for all these different configurations and the required modifications in the 3D printed designs are usually minor. This is not possible with commercial systems which are designed for a limited number of imaging applications. Combining latest developments in machine learning makes our approach a powerful tool for laboratory automation and diagnostics in low-resource settings.
Ignite Academic Collaboration Presentation: A Microfluidic-Free Method to Perform Ultra-Sensitive ELISA on Cytometer Compatible Microgels
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Open to view video. Digital enzyme-linked immunosorbent assays (dELISA) allow measurement of biomarkers down to individual molecules. Such technologies have demonstrated great utility in rare biomarker detection and early-stage diagnostics. However, the need for specialized and relatively costly equipment (e.g. the Quanterix Simoa system) has impeded the adoption of digital ELISA technologies for either R&D or clinical diagnostics. We have developed a transformative approach to perform ultra-sensitive ELISA assays on microgel particles and acquire quantified analyses with standard flow cytometers. Our approach addresses several challenges to build accessible digital ELISA assays by (1) leveraging the solid support of cytometer compatible highly uniform hydrogel particles, with a high-throughput platform to fabricate these particles with high uniformity, (2) immobilizing amplified signals on individual particles, thus allowing for simple and effective washing, (3) generating a homogenous water-in-oil emulsion by simple pipetting to prevent crosstalk, using the hydrophilic microgel particles to template the formation of emulsion drops. We demonstrate the production of monodisperse spherical polyethylene glycol (PEG) hydrogel particles at a rate of 1000 particles per second over more than 10 hours, consistently creating microgel particles < 40 μm in diameter with a < 5% CV. These microgel particles can be stored for several months. Tyramide signal amplification (TSA) was used to generate amplified signals on the particles. Alexa Fluor 488 conjugated tyramide is converted by HRP into short-lived radical intermediates which then covalently link to tyrosine residues on nearby proteins. HRP labeled particles were immediately emulsified in fluorinated oil once suspended in tyramide solutions to minimize crosstalk. By simple pipetting and agitation, the hydrophilic particles template the formation of a highly monodisperse emulsion. At least 500k droplets can be formed within 45 seconds. The emulsion was disrupted after incubation, and the accumulated tyramide signals, which remained bound on the gel particles throughout the rigorous washing, were analyzed by a flow cytometer. In our proof-of-concept work based on biotinylated microgel particles and streptavidin labeled HRP, we were able to achieve a sensitivity of ~700 HRP molecules per particle, an improvement of more than 100 folds from unamplified fluorescent labeling, and a dynamic range of >3 orders of magnitudes without sample dilution. We aim for this assay to display single-molecule sensitivity with a multiplex capacity. The batch approaches at each step of the assay grants this method high scalability to accommodate a large dynamic range without having to design or redesign any specialized component. The entire workflow, upon acquiring the pre-made hydrogel particles, is performed using basic mixing operations and standard benchtop laboratory equipment without microfluidic chips or pumps. We envision the microgel particles can be fabricated at a central site and widely distributed to speed up the adoption of digital ELISA and other highly sensitive immunoassays."
Ignite Academic Collaboration Presentation: Cell Painting Consortium for Image-Based Profiling
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Open to view video. Microscopy images contain tremendous information about the state of cells, tissues and organisms. This morphological information can be quantified and used to compare samples in order to identify, at a single-cell level, how diseases, drugs and genes affect cells. This can uncover small molecules’ mechanism of action, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles and identify novel therapeutics. Two pre-competitive consortia have been established to share best practices and create the world's largest, shared image-based Cell Painting dataset
Ignite Academic Collaboration Presentation: Liquid Biopsy as a Tool in Anticancer Functional Drug Screening for Personalized Therapy Decision and Drug Discovery
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Open to view video. Molecular information obtained from liquid biopsy is already used to predict disease progression, survival and therapy selection in metastatic patients. However, owing to the low abundance of tumor cells in liquid biopsy samples, their use in functional drug screening assays has been hampered for monitoring patient drug response/resistance, personalized therapy decision and drug discovery. We have developed a workflow to isolate tumor cells from pleural effusion and malignant ascites samples from metastatic lung and breast cancer patients and subjected them to medium scale drug screens against approved anticancer drug libraries. This approach allows realization of personalized treatment decisions within less than a week by evaluating drug responses directly in patient-derived tumor cells obtained from liquid biopsy. In patients with no pleural effusion or malignant ascites, we have established another workflow to isolate viable circulating tumor cells (CTCs) from peripheral blood of metastatic patients, from which we have generated 2D and 3D in vitro (spheroids and organoids) and in vivo (CTC-derived xenografts) models. Drug responses in the screens mirror patients’ drug resistance and reveal promising treatment options on an individual patient level. Moreover, high-throughput drug screens in CTC-derived preclinical models closely mimicking patients' setting enable discovery, repurposing and development of more efficient cancer therapeutics. Integration of drug screening with liquid biopsy-derived tumor cells constitutes a powerful tool to improve personalized treatment strategies and for drug discovery in metastatic patients.
Ignite Award Finalist Presentations
Ignite Award Winner: Correlia Biosystems
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Ignite Award Candidate Presentation: Synthace Ltd.
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Ignite Award Candidate Presentation: Lucerna, Inc.
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Ignite Award Candidate Presentation: COMBiNATi Inc.
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Ignite Award Candidate Presentation: 490 BioTech, Inc.
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Ignite Award Candidate Presentation: Ramona Optics, Inc.
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Ignite Award Candidate Presentation: ScreenIn3D
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Ignite Award Candidate Presentation: DeepDiveBio, Inc.
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Open to view video.
Ignite Award Candidate Presentation: Anatomi Corp.
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Open to view video.
Ignite Award Candidate Presentation: AxoSim Inc.
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Open to view video.
Ignite Award Candidate Presentation: DropGenie
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Open to view video.
Ignite Award Candidate Presentation: Byonoy GmbH
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Open to view video.
Ignite Award Candidate Presentation: Iscaffpharma
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Open to view video.
Ignite Award Candidate Presentation: Bioelectronica
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Open to view video.