SLAS Europe 2021 Digital

The SLAS Europe 2019 Conference and Exhibition course package contains 29 presentations including:

26 presentations from three scientific tracks
Discovery
Emerging Biology
Technology
Three Keynote Speakers

Based on presenter permission, 26 of the 30 total SLAS Europe 2021 Digital Conference & Exhibition 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.

Johannes Grillari, Ph.D.

Director

Ludwig Boltzmann Institute for Experimental and Clinical Traumatology

Johannes Grillari’s research focus is on improving our understanding of the molecular and physiological mechanisms of regeneration, as well as on the impact of cellular senescence on regeneration, specifically in skin and bone. This interest has led to pioneering the field of circulating miRNAs and extracellular vesicles in aging.

Johannes Grillari has performed his studies and PhD (1999) in Biotechnology at BOKU University Vienna, Austria in the fields of cell aging and RNA biology. In 2010 he was appointed Associate Professor at BOKU, in 2019 director of the Ludwig Boltzmann Institute of Experimental and Clinical Traumatology, Vienna. He also acted as co-founder in 2011 for Evercyte, a company generating and providing immortalized cells for biopharmaceutical research and development. In 2013, he co-founded TAmiRNA, a company interested in identifying circulating miRNAs as biomarkers for aging and age-associated diseases, specifically in osteoporosis.

Giulio Superti-Furga, Ph.D.

Scientific Director

CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences

Giulio Superti-Furga has >25 years of experience in the use of chemical biology and omics techniques to understand drug action. He performed his studies in molecular biology at the University of Zurich, Genentech/San Francisco and the IMP in Vienna (Meinrad Busslinger).

He was a postdoctoral fellow with Giulio Draetta and Sara Courtneidge at the EMBL/Heidelberg becoming team leader in 1995. He co-founded the biotech companies Cellzome, Haplogen, Allcyte, Proxygen and Solgate. He was responsible for several large-scale projects such as genome-wide characterization of the yeast protein complexes, mapping of the entire NF-κB pathway, the viral interactome, organization of the human lipidome and global genetic interaction map of SLCs. His work focuses on molecular networks and the mechanism of action of drugs. After characterizing SLC transporters required for drugs to enter cells and other required to couple nutrient availability to mTOR activity, he became an advocate of more research on SLC transporters. He has been awarded 2 ERC Advanced grants and 2 ERC Proof of Concept grants. He is the academic coordinator of the Innovative Medicines Initiative (IMI) consortium “RESOLUTE” focusing on SLCs.

He is a steering board member (initiator) of “Genom Austria” project the Austrian Personal Genome Project initiative within the Global Network of Personal Genome Projects. His personal genome sequence PGA1 is publicly available and probably was the first full genome which was given open access to in continental Europe.

Georg Winter, Ph.D.

Principal Investigator

CeMM

Georg Winter, PhD, obtained his degree from the Medical University of Vienna, working on elucidating the mechanism of action of anti-neoplastic drugs under the supervision of Prof. Giulio Superti-Furga. He specialized on proteomics- as well as chemical genetics approaches to identify drug resistance mechanisms and synergistic drug combinations. He continued his training in chemical biology, working as a postdoctoral fellow with Dr. James Bradner the Dana Farber Cancer Institute/Harvard Medical School. Supported by an EMBO fellowship, he innovated the first generalizable pharmacologic solution to in vivo target protein degradation (Winter et al., Science 2015). He was recruited as a CeMM Principal Investigator in June 2016 where his research is now focused on using the unique molecular pharmacology of targeted protein degradation to understand and disrupt fundamental principles of transcription and gene control aberrantly regulated in human cancers. Georg Winter (co-) authored 35 manuscripts including publications in Science, Nature, Nature Chemical Biology, Nature Genetics, Elife and Molecular Cell. His interdisciplinary research lab consists of 6 Postdocs, 4 graduate students and 3 technical assistants trained in molecular biology, organic chemistry and computational biology, and is supported by several national and international grants and fellowships including an ERC Starting Grant. Dr. Winter’s contribution to the field of targeted protein degradation was acknowledged via multiple prices and awards, including the prestigious Eppendorf Award 2019 and the Elisabeth Lutz Award of the Austrian Academy of Sciences.

Gregory I. Vladimer, Ph.D.

Chief Scientific Officer

Allcyte

Gregory Vladimer is the CSO of Allcyte, a biotech startup in Vienna, Austria. He received his PhD from Department of Medicine at the University of Massachusetts Medical School where he studied inflammation, and was a Senior Postdoctoral Fellow at CeMM, the Center for Molecular Medicine, in Vienna. Together with the Department of Hematology of the Medical University of Vienna, 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.

Tijmen H. Booij, Ph.D.

Lab Automation- and Screening Specialist ETH Zurich

NEXUS Personalized Health Technologies

Tijmen studied Bio-Pharmaceutical Sciences at Leiden University, during which he worked on the establishment of 3D cell culture models for drug screening. His studies were eventually awarded with the Suzanne Hovinga Award for best internship project. After his graduation, Tijmen did his PhD studies at the Leiden Academic Centre for Drug Research (LACDR) where he developed 3D cell culture-based high-throughput screening platforms for polycystic kidney disease (PKD), as well as several neoplastic disorders. His work was presented at several scientific conferences as well as several publications. Tijmen obtained his PhD degree in 2017 and is currently working as screening specialist at NEXUS personalized health technologies, a technology platform of ETH Zurich, to develop organoid-based screening methodology. Tijmen's main interests are in high-content screening with 3D tissue cultures in the context of neoplastic disorders, ultimately aiming to help discover new therapeutics.

Kathrin Weidele, Ph.D.

Senior Scientist

Fraunhofer ITEM-R

Guillaume Médard, Ph.D.

Group Leader Chemical Proteomics

Technical University of Munich

Guillaume Médard studied organic chemistry at the Ecole Nationale Supérieure de Chimie de Montpellier and the University of Strasbourg. He obtained his PhD (2009) from University College London for his synthetic work towards a natural marine secosteroid. He worked 4 years as a medicinal chemist in a drug discovery CRO in England (Argenta Discovery, now Charles River), where he specialized in kinase inhibitor discovery.

Guillaume has now been leading the chemical proteomics efforts of the Kuster lab for 9 years, at the Technical University of Munich. The key aspect of his research is the coupling of medicinal chemistry and proteomics to develop proteomics-aided drug discovery.

Markus Ralser

Einstein Professor of Biochemistry Head, Institute of Biochemistry

Charité Universitätsmedizin Berlin

Iwan Zimmermann, Ph.D.

CSO

Linkster Therapeutics AG

Ali Jazayeri, Ph.D.

CSO

OMass Therapeutics

Ali Jazayeri joined OMass Therapeutics in October 2018. He was previously CTO of Heptares Therapeutics, a company he joined in 2007 as one of the first scientists and was involved in the transfer of the GPCR stabilisation technology from LMB to Heptares. Following successful implementation and industrialisation of the technology, he led the development of novel methodologies that significantly increased the efficiency and reach of the technology. He was part of the team that lead the acquisition of G7 therapeutics, a spin out company from Zurich University. Prior to Heptares, Ali worked as a post-doctoral scientist in Clare Hall Laboratories and Kudos Pharmaceuticals where he carried out research on the role of cell cycle checkpoint kinases in DNA damage response pathways. He has a BSc in Genetics from University of Manchester and obtained his PhD in molecular biology with Prof Steve Jackson at the Gurdon Institute from University of Cambridge.

Christel Menet, Ph.D.

Chief Scientific Officer

Confo Therapeutics

Diego L. Medina, Ph.D.

Associate Professor of Biology, Telethon Institute of Genetics and Medicine

University of Naples Federico II

  • Diego Medina was born in Seville, Spain. After his graduation in Biology at the University of Seville-Spain, he moved to do his Ph.D. in Biochemistry and Molecular Biology at the Instituto de Investigaciones Biomedicas/CSIC, Madrid-Spain. Then, he was awarded with a postdoctoral Marie Curie fellowship at the EMBL-Monterotondo, Rome-Italy working neuroscience using mouse genetics. Then, he joined Eli Lilly & Co as Researcher in the Lead Optimization Biology Department (Alcobendas-Madrid-Spain). In 2007 he came-back to basic research at TIGEM working in the biological mechanisms underlying lysosomal storage disorders (LSDs). Currently, Diego Medina is Assistant Investigator, Head of the High Content Screening Facility at TIGEM, and since 2019 Associate Professor of Biology at the University of Naples "Federico II". His current research interest is focused on the discovery of new ‘druggable’ targets and the development of pharmacological strategies to treat rare genetic diseases by using cell biology, high-content imaging, and OMICS.

Lorna Suckling, Ph.D.

Team Leader

GlaxoSmithKline

With 13 years experience in drug discovery, Lorna has worked in both the academic andindustrial fields to gain expertise in high-throughput screening and automation. Shecompleted her PhD in Cancer Therapeutics at the Institute of Cancer Research, London followed by a Postdoctoral research position at Imperial College London where she used automated robotics platforms to develop high-throughput assays for engineering biology. Her current role at GSK includes leading a team to develop cell-based assays, in particular using High-throughput Flow Cytometry, and implementing automation capabilities for complex cell-based screening.

Christoph Schroder

Founder & CEO

Sciomics GmbH

Pranav Tripathi

Postdoctoral Fellow

IIT Bombay

Pranav Tripathi is pursuing PhD in the field of aptamer based diagnostics from MNNIT Allahabad, Prayagraj, India. Under the supervision of Dr. Seema Nara, applicant has worked on novel substitutes of antibodies for diagnostic uses. Applicant is a gold medalist during his M.Tech program in Biotechnology discipline from Uttarakhand Technical University. Applicant has also published 10 research papers and 5 book chapter along with 2 patents. His area of interest lies in diagnostics especially; nucleic acid based lateral flow assays.

PhD (Biotechnology)- 2014 - till date- Motilal Nehru National Institute of Technology Allahabad

M.Tech (Biotechnology)- 2012-2014- Uttarakhand Technical University

B.Tech (Biotechnology)- 2008-2012- Amity University

CSIR-Senior Research Fellow- PhD scholar (Biotechnology)- 2014 - till date- Motilal Nehru National Institute of Technology Allahabad.

Elisa Onesto, Ph.D.

Principal Scientist, Cell Biology

Axxam

I obtained the Master Degree (Laurea Magistralis) in Pharmaceutical Biotechnologies and the PhD degree in Endocrinology and Metabolic Science at the University of Milan. I worked as Post-Doctoral Researcher at Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano In all these years the fosuc of my reserch was the identification of molecular mechanisms potentially causative of amyotrophic lateral sclerosis (ALS). I’m currently working at AXXAM as assay development scientist at research laboratories, in the field of set-up and generation of cell-based assays for High Throughput Screening

Zachary A. Gurard-Levin, Ph.D.

Chief Scientific Office

SAMDI Tech, Inc

Dr. Zachary Gurard-Levin has served as chief scientific officer at SAMDI Tech, Inc. since 2016. He brings 15 years of multidisciplinary research experience with expertise in chemistry, biochemistry, cellular biology and drug discovery research. Dr. Gurard-Levin was a pioneer user of SAMDI technology and co-developed SAMDI as a high-throughput, label-free solution for drug discovery research.

Prior to SAMDI Tech, Dr. Gurard-Levin was a research scientist at the Institut Curie in Paris, France, leading epigenetics drug discovery and diagnostics projects in oncology. Dr. Gurard-Levin has authored numerous peer-reviewed articles and has been awarded multiple research grants. He has a doctorate in chemistry from the University of Chicago. In addition, he completed a postdoctoral fellowship at Institut Curie with Dr. Genevieve Almouzni.

Johanna M. Kastl, Ph.D.

Senior Scientist

Origenis

I received my PhD from the University Konstanz in 2014 focusing on the identification of small molecule inhibitors of the mitotic checkpoint component Mad2. I then moved on to the high throughput screening group at Amgen. In 2016 I joined the global HTS department at AstraZeneca as a Senior Research Scientist leading HTS campaigns for several therapeutic areas. My work interests include specialist cellular assays with FLIPR or imaging readouts and their application in hit finding strategies. More recently I became interested in the implementation of protein degradation assays in screening cascades to include profiling for this MoA. I am committed to sharing my expertise mentoring and developing research scientists and students through project supervision and training.

Ben Davis, Ph.D.

Vernalis

Dr Ben Davis is a Research Fellow at Vernalis Research, a biotech company based in Cambridge UK which has been at the forefront of fragment-based approaches since 1998. An NMR spectroscopist and biophysicist by training, his research focus is developing and applying biophysics and FBLD methods to enable drug discovery for challenging therapeutic targets and systems.

Dr Davis studied for his PhD in protein folding with Professor Alan Fersht at Cambridge University, and then worked on a wide range of molecular interactions in both academia and biotech companies. He has over 20 years’ experience in the drug discovery industry, has contributed to seven books over the last decade and is an author on more than thirty scientific publications. He is a frequent speaker at scientific conferences and has been running FBLD training workshops since 2007.

Martin Bachman, Ph.D.

Medicines Discovery Catapult

Martin obtained his MSci degree in Medicinal Chemistry from University College London, followed by a CRUK-funded PhD in Chemical Biology and Molecular Medicine in the groups of Sir Shankar Balasubramanian and Dr Adele Murrell at the University of Cambridge. During this interdisciplinary project, Martin combined chemical synthesis of stable isotope labelled probes with cell culture, in vivo experiments and mass spectrometry to address unanswered questions in the field of epigenetic modifications of nucleic acids. He then moved on to an industrial postdoc position at AstraZeneca where he worked on the development of Acoustic Mist Ionisation high-throughput mass spectrometry platform in collaboration with Waters and Labcyte. In 2018, Martin joined the Medicines Discovery Catapult as a Lead Scientist to apply innovative mass spectrometry-based solutions to drug and biomarker research.

Adrien Pasquier, Ph.D.

Post Doc

AstraZeneca

Ola Engkvist, Ph.D.

Head Molecular AI

AstraZeneca R&D

Dr Ola Engkvist is head of Molecular AI in Discovery Sciences, AstraZeneca R&D. He did his PhD in computational chemistry at Lund University followed by a postdoc at Cambridge University.  After working for two biotech companies he joined AstraZeneca in 2004. He currently lead the Molecular AI department, where the focus is to develop novel methods for ML/AI in drug design , productionalize the methods and apply the methods to AstraZeneca’s small molecules drug discovery portfolio. His main research interests are deep learning based molecular de novo design, synthetic route prediction and large scale molecular property predictions. He has published over 100 peer-reviewed scientific publications. He is adjunct professor in machine learning and AI for drug design at Chalmers University of Technology and a trustee of Cambridge Crystallographic Data Center. 

Christopher Southan, Ph.D.

Competitive Intelligence Analyst

Medicines Discovery Catapult

Daniel A. Thomas, Ph.D.

LCGI Head of Discovery Biology

Arctoris Ltd

Daniel Thomas is Head of Discovery Biology at Arctoris where he is jointly responsible for the implementation of a comprehensive scientific strategy designed to deliver high quality, reproducible and richly annotated data sets supporting early research. Blending the latest in biochemical, cellular and molecular techniques together with cutting edge automation and data analytics, the goal is to deliver automated pipelines to eliminate unnecessary experimental variability. He received his BSc and Ph.D. from the University of Leeds before joining GlaxoSmithKline, where he spent 20 years working in small molecule drug discovery. He has a passion for enzyme kinetics, extensive theoretical and practical knowledge of assay development and is an accomplished leader. He has been responsible for the quality oversight of substantial production screening portfolios, overseen the delivery of mechanistic data across multiple therapeutic areas and was principal industrial contributor in the collaborative development and implementation of a number of transformative technologies.

Tobias Brode

Head of Business Unit, Medical Engineering and Biotechnology

Fraunhofer IPA

Tobias works in various roles at the Fraunhofer IPA on the subject of laboratory automation. As a business unit manager, he is passionate about building bridges between research and industry and applying new technologies.

Sebastian Bierbaum, M.S., Ph.D.

Student Enabling Technologies

Bayer AG

Thomas Huser, M.S., Ph.D.

Professor of Physics

University of Bielefeld

Thomas Huser is a Professor of Physics at the University of Bielefeld, Germany. He holds several patents on single molecule and single cell analytics, as well as super-resolution microscopy, and has over 120 peer-reviewed publications. From 2005 - 2011 he was an Associate Professor in the Department of Internal Medicine at the University of California, Davis, and also served as Chief Scientist for the NSF Center for Biophotonics Science and Technology. From 2000 - 2005, Dr. Huser was a Group leader for Biophotonics and Nanospectroscopy at Lawrence Livermore National Laboratory in Livermore, CA, where he developed and applied novel nano-biophotonics tools to the analysis and characterization of individual cells. Dr. Huser obtained his Ph.D. in Physics from the University of Basel, Switzerland, on near-field optical microscopy. At the University of Bielefeld he applies optical nanoscopy and spectroscopy techniques to biological and medical problems at the single cell level.

Pilar Ayuda-Durán, Ph.D.

Postdoctoral Researcher

Oslo University Hospital

I’ve always been interested in cancer research. My PhD work was about the effects of an altered cell cycle misregulation in the occurrence of genomic instability, a common feature in the origin of tumour development. For that purpose I worked with the useful model system Saccharomyces cerevisiae. After obtaining my PhD I decided to move to a disease-related research. Jorrit Enserink provided me the opportunity to combine basic research using a leukaemia model (mice Ba/F3 cells expressing the BCR-ABL oncogene, common in CML and ALL) with a more clinical-related challenge, the establishment of a personalized medicine platform. Altogether these research paths fulfil my desire of contributing to unravel cancer biology and helping to design new treatments against this disease.


Christian Brüser, Ph.D.

Post-Doc

Max-Planck-Institute for Biophysical Chemistry

I studied Biologie at the Heinrich-Heine University in Düsseldorf. During my Master‘s studies I worked intensively with superresolution microscopy and analyzed the degradation of transmembrane receptors through the endosomal pathway. In 2014, I joined the laboratory of Prof. Stefan Jakobs at the Max-Planck-Institute for Biophysical Chemistry in Göttingen. I received my PhD in 2018 and since then I have been working as a Postdoc, investigating the behavior of mitochondrial DNA and the organization of the inner mitochondrial membrane, using a variety of superresolution microscopy techniques, especially STED-microscopy.

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Keynote Presentations
Keynote Presentation with Prof. Johannes Grillari
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Open to view video. From Cellular Senescence to Cell Based Assays and RNA Diagnostics. A Journey.
Keynote Presentation with Prof. Giulio Superti-Furga
Open to view video.
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Keynote Presentation with Georg Winter
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Open to view video. Identification and Characterization of Novel Molecular Glue Degraders Targeted protein degradation is a now therapeutic paradigm that promises to overcome limitations of traditional pharmacology. Of particular interest are molecular glue degraders as they can lead to the drug-induced degradation of proteins that are unligandable and thus otherwise considered “undruggable”. Until recently, the identification of glue degraders was largely driven by serendipity. In this presentation, I will present our recent efforts to develop rational strategies for the identification and characterization of molecular glues that prompt the degradation of oncoproteins.
Discovery
Accelerating translational medicine with high content imaging and deep learning based analysis of primary cancer samples
Open to view video.
Open to view video. High-throughput confocal microscopy has changed the way ex vivo drug screening can be performed, by enabling the detection of single-cell phenotypes after small molecule or biological perturbation. Combined with advances in machine-learning driven image analysis, which have allowed for the quantification of these single cell events, it opens new doors for large phenotype-focused studies. By amending technologies that have historically been used with adherent cell lines, to create pipelines amenable for mixes of primary adherent and non-adherent cells, we can perform image-based drug screening directly in fresh primary material taken from patients with a wide range of hematological and solid tumor cancers. This enables more clinically translatable drug screening, and ultimately, functional precision medicine. In a prospective clinical trial, combining multiparametric immunofluorescence with high-throughput automated microscopy and single cell image analysis, we quantified tumor-cell specific biological parameters of millions of adherent and non-adherent individual cells from primary samples to prioritize treatment options for patients with late-stage hematological malignancies. An interim analysis revealed that patients receiving treatment prioritized by this program had a longer progression-free survival on this therapy than the prior round of therapy; and where most treatments provided were off-label and repurposed. Image-based ex vivo functional drug testing can be further combined with epigenetic screening to rationally uncover combination treatment options for specific indications, supporting functional genetics. This talk will focus on applications of deep learning in the analysis of multi-lineage tissues, as well as precision medicine and patient-centric drug discovery efforts.
High-throughput drug screening with pancreatic cancer organoids
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Open to view video. Lab automation has enabled miniaturization and high-throughput screening approaches in drug research, allowing increased numbers of potential drugs to be tested in in vitro assays. Due to their increased physiological relevance, 3D patient-derived xenografts or organoids have gained in popularity to improve the translation of in vitro assays to in vivo studies. The transition, however from screening in 2D cell culture assays to 3D PDX or patient-derived organoid (PDO) assays has long been held back by technological limitations and high costs. Pancreatic ductal adenocarcinoma (PDAC) is a malignancy with a mortality rate above 98% and poor responsiveness to chemotherapy. The improved physiological relevance of PDX and PDO models could be the key to unlock the identification and development of more effective drugs to treat this aggressive disease. We established a robust organoid screening platform for PDAC that integrates fully-automated liquid handling, plate transfer and readouts. This screening platform was applied to screen a library of 1172 drugs that allow for rapid drug repurposing on two PDX-derived organoid models. We identified 22 hits and validated these on biobanked PDO- and PDX samples, as well as matched non-cancerous tissue. We confirmed the activity of several classical chemotherapeutic agents as well as drugs approved for other indications than cancer. We found that several of the drugs act in PDAC organoids by repressing HIF1 signaling, a pathway that is associated with poor prognosis.
Liquid biopsy-based platform for anticancer drug screening in precision oncology and drug repositioning
Open to view video.
Open to view video. Liquid biopsy is a valuable tool in precision oncology and its molecular analysis provides crucial information for the prediction of prognosis and therapy selection in metastatic cancer patients. However, the rarity of cancer cells in liquid biopsy samples as well as the difficulties associated with culturing primary cells limit their application in functional drug screens. Therefore, we developed and optimized workflows for isolating tumor cells from blood (circulating tumor cells, CTC), pleural effusions or malignant ascites and established representative patient-derived in vitro models for subsequent drug screening. Drug response data from screening anticancer drug libraries with tumor cells from breast, lung and parotid cancer patients mirrored patients’ drug resistance and revealed promising candidates for treatment of individual patients. Using such liquid biopsy-based approach, drug sensitivities and treatment decisions can be performed within a therapeutically feasible time frames by evaluating drug responses directly in patient-derived tumor cells and thereby potentially influence personalized treatment strategy. Moreover, high-throughput drug screens in patient-derived in vitro models enable discovery, repositioning and development of more efficient cancer therapeutics as they closely mimic the patients' setting.
Chemoproteomic profiling
Open to view video.
Open to view video. Mass-spectrometry based proteomics allows to massively multiplex the identification and quantification of proteins present in biological samples. Chemoproteomic profiling makes use of this powerful readout technology to assess the targets that a molecule engages within a lysate of organelles/cells/tissues or even whole organisms. Here, the molecules are profiled as competitors of an affinity matrix, whose enrichment scope defines the “panel” of the assay. The binding affinities of the molecules for their targets in their native states can hence be obtained. Finding the target of a hit after phenotypic screening (target deconvolution) is a prime application of such technology, where the bioactive molecule is set to compete against its own immobilised avatar (tailored matrix). We have extended this concept to a focused library centered on a versatile pharmacophore. Here, 40 linkable molecules revolving around the same chemical scaffold have been synthesised to serve as both affinity probes and competitors for the dose-dependent competition pulldown experiments. By analysing the target deconvolution of all the analogues, we have obtained for the first time a proteome-wide SAR, in contrast to classical medicinal chemistry which usually establishes structure-activity relationships for a single enzyme. To systematically profile a class of inhibitors, the synthesis of tailored matrices becomes prohibitive. For this purpose, the preparation of family-specific matrices able to enrich defined subproteomes becomes an empowering method, where the “panel” of native proteins is defined by 1) the abundance of the proteins in the biological sample and 2) the target space of the affinity matrix among those proteins. For the kinome, we have contributed to the development of Kinobeads. This affinity matrix is prepared as a mixture of wide-scope complementary immobilised kinase inhibitors, and has allowed to notably establish the target landscape of clinical kinase drugs. To profile lysine deacylase (KDAC) inhibitors we have synthesised and evaluated a range of analogues of known inhibitors in conjunction with a range of human cell lines. We could then create a chemoproteomics assay, featuring 3 hydroxamate affinity probes and 2 cell lines, to measure the affinity of drugs for many native metal-binding proteins, including the (complex-engulfed) human HDAC1, HDAC2, HDAC3, HDAC4, HDAC5, HDAC6, HDAC7, HDAC8 and HDAC10. The combined profiles of 50 molecules illuminate the target landscape of KDAC inhibitors, and notably reveal MBLAC2 as a frequent off-target of hydroxamate drugs.
The proteome in high throughput
Open to view video.
Open to view video. Life runs on many thousands of different chemical reactions, known collectively as cell metabolism. Metabolic reactions are vital for keeping cells and organisms growing and alive, and problems with cellular metabolism are implicated in ageing and diseases such as cancer, diabetes and brain disorders. In order to understand metabolism at its scale, novel technologies are required. These need to measure metabolites and proteins at precision, at high throughput, and at costs that facilitate systematic perturbation experiments. In this lecture, I’ll summarize our efforts in using mass spectrometry, yeast as a simple system, as well as human plasma analytics, for conducting hundreds to thousands of analytical measurements, allowing us to study how these complex metabolic processes are controlled, and how they are reconfigured in response to environmental changes. By taking detailed precision measurements of the genes and molecules involved in metabolic processes and putting the data into computer analysis programmes, we can see how the cell’s metabolism adapts and changes in response to various conditions.
Flycode® Technology to Screen Hundreds of Bispecifics Simultaneously in one Animal
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Open to view video. Pharmacokinetic experiments are key to predict the behaviour of biologics in humans. Unfortunately, PK studies exhibit severe limitations, such as high experimental variability between individuals and very low throughput. In this talk, the Flycode® technology will be introduced, which enables high-throughput in vivo PK determinations of antibodies with minimal animal abuse. It will be demonstrated that more than one hundred low-dosed bispecifics can be monitored simultaneously in the same animal, allowing for accurate determination of key PK parameters without variability from different individuals. The Flycode® technology screens hit molecules in vivo prior to upscaling early in development so that the most promising leads can be chosen based on large datasets obtained by a minimal number of animal experiments.
Native Mass spectrometry as a novel drug discovery method for membrane proteins
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Open to view video. Native mass spectrometry (MS) can be used to characterize proteins without disrupting the non-covalent interactions that maintain their structural integrity as well as their interactions with ligands. Whilst this methodology has been applied to soluble proteins widely, its application to membrane proteins has been challenging due to the presence of detergent micelles. Recent technical advances in membrane protein biochemistry as well as instruments has led to the development of membrane protein specific protocols. Utilizing native MS on membrane proteins such as GPCRs represents a novel and highly differentiated technique to interrogate the biology of these important class of proteins. Specifically, for GPCRs that we have demonstrated that native MS allows identification of novel endogenous ligands that co-purify with the receptor as well as hits from a complex library of synthetic compounds. Moreover, through analysis of the interaction of the receptor with the downstream signalling molecules (e.g. G-proteins), the pharmacology of ligands can be observed directly. Given the high resolution and high content nature of the native MS approach, these data demonstrate that application of native MS to GPCRs represents an exciting platform that can be used to screen complex libraries more importantly characterize the functional consequences of compound binding in a biophysical technique.
The Confo Therapeutics Technology Platform Enabling GPCR Fragment-based Drug Discovery
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Open to view video. Confo Therapeutics is a drug discovery company building internal drug discovery programs on GPCRs addressing unmet medical need. Confo Therapeutics employs its proprietary Confo® technology to lock inherently unstable functional conformations of GPCRs as a superior starting point for drug discovery. ConfoBody-stabilized active state conformations of these receptors expose previously inaccessible structural features empowering the discovery of novel agonists for better therapeutic intervention. Confo aims to broadly exploit its disruptive GPCR technology by developing a pipeline of proprietary programs and by forging strategic collaborations with select pharma partners.
Emerging Biology
TRPML1 links lysosomal calcium to autophagy initiation
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Open to view video. TRPML1 (or Mucolipin 1) is a cation-permeable channel localized on the membranes of late endosomes and lysosomes (LELs) of mammalian cells. TRPML1 has been involved in vesicular trafficking, vesicular fusion, phagocytosis of large particles, and lysosomal exocytosis (Medina et al, 2011; Xu et al, 2015; Venkatachalam et al, 2015; Li et al, 2016). We have recently shown that lysosomal Ca2+ release, through TRPML1, plays a major role in lysosomal adaptation to starvation by inducing calcineurin that de-phosphorylates and activates TFEB, a master gene of lysosomal function (Medina et al, 2015). While studying the role of TRPML1 in autophagy, we also observed that the inhibition of TRPML1 significantly reduced the recruitment of the WD-repeat PtdIns(3)Peffector protein WIPI2 to vesicles (Medina et al, 2015). Since WIPI2 is an essential effector at the nascent autophagosome (Proikas-Cezanne et al, 2015), we reasoned that the activity of the lysosomal calcium channel TRPML1 may play a role in autophagosome biogenesis. Indeed, we found that genetically and pharmacologically inhibition of TRPML1 during starvation significantly reduced the number of vesicles containing both WIPI2 and the phagophore marker DFCP1. Conversely, TRPML1 overexpression increased phagophore formation inducing autophagic flux. Interestingly, WIPI2-puncta formation during starvation was also reduced in fibroblasts derived from human patients affected of Mucolipidosis type IV, a lysosomal storage disease caused by loss-of-function mutations in TRPML1 gene. We also found that TRPML1-mediated effects require both calcium and PtdIns(3)P, since the addition of BAPTA chelator or the inhibition of PIK3C3 during starvation completely abolish WIPI2 puncta formation. Together, our results suggest that TRPML1 and lysosomal Ca2+ modulate autophagy by the recruitment of PtdIns(3)P to the phagophore membrane during autophagy initiation.
The Identification of Novel Drug Target Candidates in T cells
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Open to view video. Advances in functional genomics and chemogenomics methods are enabling the identification of novel drug target candidates. The use of CRISPR based screening systems as a functional genomics tool, can be used for both target identification and validation. Such screens provide platforms for the rapid identification of multiple therapeutic target candidates which when targeted result in a desired phenotypic response. When combined with multiparametric readouts, these screens can provide a wealth of data for the drug discovery field. We have developed CRISPR screening workflows in primary CD4+ T cells with flow cytometry based endpoint assays. These screening workflows will enable the identification of gene targets which, when silenced, cause T cell subsets to elicit immunomodulatory effects. These data will provide information for multiple disease areas when selecting targets for further development.
Prediction of severe Covid-19 disease – Affinity-Proteomics based biomarker discovery
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Open to view video. Protein profiling plays an essential part in today’s biomedical research, striving to improve patients’ quality of life by using molecular signatures for diagnosis and treatment guidance. Here, we present data from an early disease severity marker screening in COVID-19 patients. The identified candidates differentiate patients with a mild or moderate disease from a severe or critical disease in the first days after onset of symptoms or diagnosis. Such markers can be especially useful to select high-risk individuals for early treatment with novel drugs to reduce disease aggravation, mortality and long-term consequences. Fast and robust biomarker discovery at Sciomics is done using our fully immune-based platform for protein biomarker discovery as well as pathway activity profiling. With this platform, the abundance of 1,300 proteins as well as the respective phosphorylation or ubiquitination status is profiled in a single assay.
Aptamer based assay for detecting ovarian cancer biomarker CA125
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Open to view video. From decades, point-of-care technology (POCT) has proven its potential regarding swift and cost-efficient detection of analytes. In the context of POCT, lateral flow assays are crucially competent formats. Since its inception, lateral flow system has witnessed numerous improvisations in terms of label, capture reagent, quantification method and signal enhancement method. We at this moment report an aptamer-based quantitative lateral flow assay for detection of CA125 (an FDA approved biomarker for ovarian cancer). The aptamer was eloquently screened and characterised before use in this technology. This assay incorporates aptamer as a capture reagent and ImageJ software for quantification of CA125 by image analysis in a competitive format. The test was specific and sufficed for a limit of detection of 3.71 U/mL when the KD of aptamer was 168nM.
Cell-based assays for studying intracellular protein-protein interaction in live cells
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Open to view video. Protein-protein interactions (PPIs) are central mechanisms for biological processes and as such are investigated by scientific researchers and for drug discovery applications. The most widely used techniques, for example co-immunoprecipitation and pull-down assays, allow only low throughput. The development of ‘easy to run’ cell-based assays would be beneficial for investigating PPIs, not only for basic research, but especially for drug discovery and screening. In recent years, the modulation of PPIs has become of interest in the field of cancer research. Different phenotypic screenings have identified small molecules that displayed a strong selectivity between different cancer cells. Subsequent studies have identified the compound-dependent interaction between proteins as a feature associated with drug response. The availability of cell-based assays for the study of proteins of interest could provide a great tool for both the characterization of molecular processes and also for the discovery of novel candidates for drug development. We describe some case studies in which PPIs were investigated in intact and viable cells. We explored both the NanoBiT (NanoLuc Binary Technology) and the NanoBRET technology (NanoLuc Bioluminescence Resonance Energy Transfer), in which the proteins of interest are fused with the specific reporter tags. The PPI between the two target proteins facilitates either subunit complementation and the reconstitution of a functional NanoLuc luciferase able to produce luminescence (for NanoBiT) or the energy transfer from NanoLuc to the acceptor probe bound to HaloTag (for NanoBRET). These technologies were succesfully applied for the study of PPI of different targets: in a first step, we performed transient transfections of different combinations of N- or C-terminal fusion proteins and identified the best pairing constructs for PPI assays, that were subsequently used for the stable expression in cells. We overcame issues related to cell toxicity if one of the two partners was over-expressed using an inducible stable cell line, in which both target proteins were stably expressed. We developed a NanoBiT-based, high-throughput screening (HTS) grade cell-based assay that allowed the identification of small molecules that mediate intracellular PPI between two cytoplasmic proteins in live cells; this assay is highly suitable for screening purposes. This cell-based assay not only allowed for the identification of small molecules that mediate PPI, but also allowed for real-time monitoring of the interaction since the assay is performed on viable and intact cells. Cell-based PPI assays have the great advantage of allowing the analysis of proteins in their physiological environment, over time, in live cells and in a high-throughput fashion. Thanks to these advantages, they provide an important tool for drug discovery.
Innovative approaches for label-free and high-throughput SARS-CoV-2 drug discovery using self-assembled monolayers and mass spectrometry
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Open to view video. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic. Several proteins have emerged as promising antiviral targets including the coronavirus 3-chymotrypsin-like cysteine protease (3CLpro) and non-structural protein 14 (NSP14), which features exonuclease (ExoN) activity implicated in replication fidelity. Initiating drug discovery efforts focused on small molecules for SARS-CoV-2 has been challenged by to a lack of suitable high-throughput assay methodologies. This study describes the combination of self-assembled monolayers and matrix-assisted laser desorption ionization mass spectrometry, a technique termed SAMDI-MS, to enable the first label-free and high-throughput assay for 3CLpro and NSP14 activity. For 3CLpro, the assay was optimized and miniaturized with a Z-factor > 0.8. To validate the assay, we evaluated reported inhibitors of coronavirus 3CLpro such as GC376, calpain inhibitors II and XII, and the FDA-approved drugs shikonin, disulfiram, and ebselen. The data demonstrate that the label-free MS assay offers greater sensitivity and eliminates false positive results compared to a traditional FRET assay. We next describe multiplexing the assay to additionally measure the activity of the human rhinovirus 3C protease (HRV3C). The duplex assay was then used to screen 300,000 small molecules to identify potent and selective 3CLpro inhibitors. For NSP14, the SAMDI-MS assay was used to shed light on substrate specificity and the reaction mechanism. Next, the assay was optimized for kinetically balanced conditions and miniaturized, while achieving a robust assay (Z-factor > 0.8) and a significant assay window (signal to background > 200). Screening 10,000 small molecules from a diverse library revealed candidate inhibitors, which were counter screened for NSP14 selectivity and RNA intercalation. Taken together, the study highlights the benefits of SAMDI-MS to accelerate antiviral drug discovery. Importantly, the flexibility of the assay enables its application against other targets, opening avenues to rapidly identify antiviral agents directed against SARS-CoV-2, other viruses, as well as other pathologies.
Rapid identification of small molecule degraders from HTS screening outputs – an unexpected MoA
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Open to view video. Traditional approaches in drug development based on modulating the function of a target protein are often limited to certain target classes like enzymes and receptors. Thus, a large part of the proteome is still deemed “undruggable” and alternative routes like disrupting functional protein-protein interactions have proven to be challenging. Targeted protein degradation presents a novel, universal strategy which hijacks the cells proteostasis machinery to deplete specific proteins removing them completely and may lead to a paradigm shift in how early drug discovery is approached. Recent efforts have focused on the development of PROTACs (Proteolysis Targeting Chimaeras). These bifunctional molecules combine a target specific binding moiety with an E3 ligase binding function to trigger selective ubiquitination of the target protein marking it for subsequent proteasomal degradation. Whilst these molecules can be highly efficacious, they require the formation of a complex ternary protein structure to operate and have generally unfavourable physicochemical properties due to their large size. In contrast, a small molecule that induced degradation via direct target binding could represent a simpler and more attractive option overcoming the limitations of both traditional inhibitors and PROTACs. Such an effect could be driven by destabilisation of local or distal regions of the structure that trigger an unfolded protein response (UPR). To identify these specific small molecule degraders, we developed a high throughput amenable 1536 well degradation assay utilising Promega’s Nano-Glo® HiBiT technology and a CRISPR knock-in cell line which facilitates detection of endogenous HiBiT-tagged protein. Our model target for this study had just completed an HTS screening of 1.7 Mio compounds in a fluorescence-based competition displacement assay. The putative binders identified from this output were profiled in the HiBiT degradation assay along with a 100K sample diversity set. “Hits” which significantly decreased HiBiT signal were further profiled in toxicity and selectivity assays as well as biophysical methods to further elucidate their mechanism of action. Identified key chemical components may finally provide the basis of rational design for these exciting modalities.
Technology
Fragment-Based Discovery of Novel Non-Hydroxamate LpxC Inhibitors with Antibacterial Activity
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Open to view video. LpxC catalyzes the first committed step in the biosynthesis of Lipid A, an essential component of the cell envelope of Gram-negative bacteria. In collaboration with colleagues at Taisho Pharmaceuticals, we have identified two series of non-hydroxamate compounds derived from fragments with differing modes of zinc chelation. Structure-guided design led to a compound exhibiting low nanomolar inhibition of LpxC and a minimum inhibitory concentration (MIC) of 4 µg/mL against Pseudomonas aeruginosa.
The rise of high-throughput mass spectrometry for cellular profiling of early drug candidates
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Open to view video. Cell based assays are an important part of early drug discovery. Compared to biochemical enzyme activity assays, they present a great rise in sample complexity and come with high attrition in the numbers of candidate compounds. An ideal assay would directly reveal whether a compound has made its way to the cell, show inhibition or activation of the target enzyme, point at any downstream and off-target effects and highlight unexpected compound metabolism. High-throughput is vital due to relatively high numbers of conditions to be tested (multiple compounds, concentration points and time points). Using real-life examples, I will discuss how recent advances in high-throughput mass spectrometry get us closer to the ideal type of cellular assays, making it possible to focus on real hits very early on in the drug discovery pipeline.
Multi-modality secretomics, functionalised fragment and small molecule approach identifies pathways involved in dedifferentiation and redifferentiation in Type 2 diabetes
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Open to view video. Recent evidence suggests β-cell dedifferentiation as a key driver for the loss of pancreatic β-cell function. Exploratory data in human models of disease remain scarce and much is still to be understood regarding the signalling mechanisms. We adapted a previously established model of dedifferentiation using the human β-cell line, EndoC-βH1 to (i) enable a multi-modality secretomics, small molecule and small molecule fragment approaches for high-throughput investigation of the pathways involved in dedifferentiation in Type 2 diabetes (T2D) and (ii) identify targets and small molecules capable of reversing the dedifferentiated phenotype. We performed a multi-modality phenotypic screen using the EndoC-βH1 model of dedifferentiation to alter the differentiation and redifferentiation status of the human pancreatic β-cell line using (1) 1400 proteins of the AstraZeneca secretome library, (2) a small molecule fully functionalised fragment (FFFs) library recently deployed at AstraZeneca which is embedded with a chemical proteomics platform and enables global mapping of reversible ligand-protein interactions in cells, (3) small molecule profiling to assess the potential to inhibit and reverse the dedifferentiation effect. Dedifferentiation was monitored by measuring changes in the subcellular location and expression of the β-cell marker MAFA (key regulator of glucose stimulated-insulin secretion) and the pancreatic progenitor marker SOX9. The secretomics screen identified FGF9, FGF20, FGF18 and FGF4 as novel biological modulators of the dedifferentiation phenotype, along with previously established FGF1 and FGF2. FGF9 induced the strongest phenotypic effect with a 2 fold increase in SOX9 nuclear intensity and a 1.5 fold reduction in MAFA nuclear/cytoplasmic localisation compared to FGF2 control. The unique ability of the FGF family members from 1400 secreted proteins to alter the dedifferentiation phenotype suggests that FGF signalling may be an important player in dedifferentiation. Indeed, further studies indicate that a FGFR antibody is capable of partially inhibiting FGF2 induced changes in MAFA and SOX9 in both EndoC-βH1 and human primary islets. Furthermore, the FGF2-driven dedifferentiation of the EndoC-βH1 cell line was found to be reversible through treatment with small molecule inhibitors of MAPK/ERK and TGFβ signalling. A complementary and novel approach using an FFF library of 500 fragments embedded with a chemical proteomics platform identified a fragment molecule which redifferentiates FGF2 modified β-cells by re-establishing expression of MAFA. Chemoproteomics competitive and enrichment profiling identified putative β-cell targets which will help to further elucidate the mechanisms and pathways required to reverse dedifferentiation.
Applying AI and Automation for drug design
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Open to view video. Artificial intelligence is underway to transform the society through technologies like self-driving cars. Also, in drug discovery machine learning and artificial intelligence methods has received increased attention. [1] The increased attention is not only due to methodological progress in machine learning and artificial intelligence, but also progress in automation for screening, chemistry, imaging and -omics technologies, which have generated very large datasets suitable for machine learning. While machine learning has been used for a long time in drug design, there has been two exiting developments during the last years. One is the progress in synthesis prediction, where deep learning together with fast search methods like Monte Carlo Tree Search has been shown to improve synthetic route prediction as exemplified by a recent Nature article. [2] In this talk I will focus on the second development, which is applying deep learning based methods for de novo molecular design. It has always been the dream of the medicinal and computational chemist to be able to search the whole chemical space of estimated 1060 molecules. This would be a step change compared to search enumerable chemical libraries of perhaps 1010 compounds. Methods to search the whole chemical space through generative deep learning architectures has been developed during the last 3-years. In the presentation there will be a focus de novo generation of molecules with the Recurrent Neural Network (RNN) architecture. The basis will be described and exemplified of how molecules are generated. After the concept has been introduced it will be described how the method is used within drug design projects at AstraZeneca. Finally, we will discuss how AI based de novo design can be combined with chemistry automation to increase the productivity in drug design [1] The rise of deep learning in drug discovery, Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke, Drug discovery today, 23, 6, 1241 [2] Planning chemical syntheses with deep neural networks and symbolic AI, Marwin HS Segler, Mike Preuss, Mark P Waller, Nature, 555, 7698, 604 [3] Molecular de-novo design through deep reinforcement learning, Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen, Journal of cheminformatics, 9, 1, 48
AssayNet: A directed assay graph for drug discovery
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Open to view video. The drug discovery process can be divided into a series of steps according to the type of assay performed; 1) in vitro biochemical assays using purified protein targets for activity screens, 2) cell-based assays for mechanistic insights, 3) the use of tissues and organs to increase physiological relevance, 4) in vivo testing in animal models of disease and 5) clinical trials in human patients. Based on technical similarities and chemical connectivity paths between them, AssayNet has integrated these five categories into a neural net, based on textual descriptions extracted from papers and patents. The concepts underpinning this network have been developed from previous work on the large-scale clustering of ChEMBL assay descriptions using Word2vec [1]. MDC is now developing AssayNet for the following; a) populating the network for drug discovery projects with external collaborators b) to gain insights into paths through the assay landscape from “targets-to-trials” c) identifying what chemistry has traversed which routes, d) locating translational dead-ends to enable alternative through-route choices with a higher likelihood of success e) provide a resource for collaborators to find new assays f) open up new repurposing opportunities and g) to explore new AI/ML options for clustering targets, assays and chemistry [1] Zwierzyna M, and Overington JP. 2017, Classification and analysis of a large collection of in vivo bioassay descriptions. PLoS Comput Biol. Jul 5;13(7), PMID 28678787.
Combatting Irreproducibility: the journey from pipe dream to reality
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Open to view video. The challenge of experimental reproducibility is not new, and confidence in the accuracy of research findings is low. However, recent high-profile examples of published papers being retracted, corroborates reports from a number of pharma companies indicating that as many as 90% of published research findings cannot be reproduced by independent third parties. Reasons for this include the use of poorly validated reagents and protocols, the inappropriate implementation or absence of standards and inconsistencies in experimental performance. The Arctoris cloud laboratory aims to tackle each of these issues, ensuring fidelity in the capture of all data and metadata, together with the provision of comprehensive electronic audit trails, all of which are critical to the delivery of high quality data to support decision making. The current pressure on cycle time reduction, ultimately aiming to progress from hit to candidate in under 12 months has inevitably shifted the focus towards exploiting innovations that accelerate molecular evolution. This information-intensive process demands rich, high quality data sets about molecular activities upon which AI and ML platforms can learn, informing molecular design and ultimately delivering desirable molecular properties in fewer iterations. The latest examples of such data sets that can be generated using our automated platform will be showcased, together with automation logs demonstrating the veracity of the data generation process. An unerring focus on the elimination of unnecessary variability in experimental execution will not only ensure future data equivalence but will protect valuable resources, reduce cycle times from weeks to hours and ensure the pipe dream becomes a reality.
Kevin – autonomous laboratory robot / Alone in the Lab!
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Open to view video. Nowadays, handling laboratory consumables is a time consuming and wasteful operation that does not add to the profit of companies. Transportation processes consumes precious lab time of the employees, whilst at the same time limiting the degree of utilization of expensive lab equipment. As the working hours of readers and other by themselves, highly automated devices are directly linked to the working hours of lab personnel operating those devices. Therefore, a great deal of utilization time of devices is wasted as they remain idle during night shifts and on weekends. At the same time, personnel is wasted on tasks that would be predestinated for automation. At Fraunhofer IPA we have developed an autonomous lab assistant addressing these issues named Kevin. Kevin is able to navigate in the standard lab environment, avoiding obstacles both static and dynamic. He also features dynamic mapping taking into account that labs operated by human individuals are likely to change. To face the challenge of transporting lab consumables Kevin comes equipped with a SCARA robotic arm and a plate hotel for micro well plates in SBS-format. As modular design principles have been considered during the design phase, compositions with other arms are possible allowing designs matching the application. Human robot collaboration regulations are considered for both platform and the attached robotic arm as well as for the combination of both. This enables Kevin to work in unison with human lab personnel. Control of and access to Kevin is possible via industry default communication interfaces such as SiLA or OPC‑UA. Therefore, many existing laboratory IT services may benefit from integrating Kevin. Kevin enables retrofitting laboratories with automation. Thus, allowing laboratories with a strongly dependent human workflow to benefit from automation without having to suffer the high costs and inflexibility of fully automated solutions.
Automated STED microscopy for cell-biological high-throughput analyses
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Open to view video. In the past decade, super-resolution fluorescence microscopy techniques established a new era in fundamental biological research. While breaking the diffraction limit of light microscopy, by now various approaches enable the elucidation of intracellular details on the protein level with attainable resolutions of a few 10nm. However, whereas these methods are in the meantime routinely used in academia, transition to the industrial environment remains challenging. Many super-resolution light microscopy approaches require for elaborate sample preparation techniques, a rather complex instrumentation and overall, still manual operation that frequently result in long imaging times and low throughput. To enable super-resolved feature extraction while maintaining high-throughput capabilities, stimulated emission depletion (STED) microscopy tends to be the method of choice as it allows for direct image formation in a fast point-scanning fashion without the necessity for subsequent image reconstruction. In my talk, I will address currently ongoing developments towards a fully automated STED imaging platform. Imaging of centrosomal clusters in fixed cancer cells that routinely cannot be resolved by standard light microscopy serves as a model use case to tackle critical steps towards this goal: First, sample preparation protocols must be streamlined to allow for measuring large scale assays in microtiter plates. Besides proper fixation and labeling approaches, also the right choice of STED-capable fluorophores is critical for satisfactory image quality. Second, increased resolution requires smaller pixel sizes and thus increased acquisition times, demanding intelligent imaging modalities to obtain super-resolved feature recognition in acceptable recording times. Not least, intelligent algorithms are needed to automatically target features of interest inside the sample and assist during subsequent data analysis. Automated STED microscopy has thus the potential to circumvent current bottlenecks including limited statistics and long acquisition times and to become a standard technique for cell-biological high-throughput assays in future drug screening applications.
Smarter visualization of nanoscale processes in living cells by super-resolution optical microscopy with instant image reconstruction
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Open to view video. Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fastest and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high and the physical size of the instruments is still prohibitively large. Here, I will present our latest efforts towards realizing a new generation of compact, cost-efficient and high-speed SR-SIM instruments. Tight integration of the structured illumination microscope capable of video-rate image acquisition with instant image reconstruction based on parallel computing of image information enables us to realize a super-resolving fluorescence microscope with the look-and-feel of regular wide-field microscopy. I will demonstrate this by discussing the overall integration of optics, electronics, and software that allowed us to achieve this, and then present its capabilities by visualizing the dynamics of intracellular transport and movement in living cells, in particular the dynamics of liver cell fenestrations. These nano-sized pores in liver endothelial cells play a particularly crucial role in human physiology, which is reduced or lost during disease and/or aging. To best address these issues, we are continuously improving the instrument, e.g. by parallelizing three-dimensional image acquisition by utilizing multiplane image splitting prisms, or by investingating alternative, more efficent and even faster approaches to SR-SIM. This allows us to rapidly capture and cover large sample areas in multiple fluorescence color channels with approximately 100 nm spatial resolution to survey and identify sample areas of interest, which can subsequently by specifically targeted for long-term imaging or imaging with even high spatial resolution.
Predicting drug sensitivity and resistance: The journey from single drug responses to drug combinations
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Open to view video. Cancer is a very heterogeneous disease and is often considered that each tumor in each patient, and even different tumors within the same patient are unique. Taking acute myeloid leukemia (AML) as an example, latest classifications based on the mutational profile identify 14 different groups and many more subgroups, and this not considering other molecular characteristics, as epigenetic modifications. Tumor heterogeneity is a challenge to the implementation of personalized medicine strategies, providing each patient the better treatment for their disease and leading to improved quality of life and survival rates. Although the search of informative biomarkers in the tumor, as specific mutations, has being implemented in clinical decision making, is still difficult to capture the tumor heterogeneity and predict the response of the tumor to a specific treatment. Directly testing drugs on primary cells, known as drug sensitivity screenings, is a strategy to assign treatments to cancer patients. Drug-driven precision medicine approaches compare the sensitivity of primary cells from individual cancer patients and healthy donors to a panel of anticancer drugs and select the most effective drug for each patient. This approach considers any combination of mutations or epigenetic changes that might not be found in the standard sequencing panels, an advantage when dealing with such a heterogeneous disease. In Oslo University Hospital we have established a high-throughput screening platform where we test both primary cells and well-characterized cancer cell lines. On one hand, we analyze the ex vivo drug response of leukemia primary cells to a panel of 349 drugs covering cancer chemotherapeutics as well as many clinically available and emerging molecularly targeted compounds. To date we have successfully processed 6 healthy donors and over 100 leukemia samples. We have demonstrated that the in vitro drug response is reproduced in vivo for selected drugs and that we can predict patient outcome based on their in-vitro response to specific drugs. On the other hand, we have set up a system to screen large amounts of drug combinations in a high-throughput format. We are now exploring the response of 20 well characterized melanoma cell lines to a panel of 65 pair wise drug combinations, and we aim to identify synergistic drug combinations for each of the cell lines, which we will correlate with their molecular make-up. Our long-term goal is to identify synergistic drug combinations for each patient that comes to our clinic. The combination of these single-drug and synergistic approaches will help us determine whether specific subgroups of patients respond with a similar dynamic to certain drugs or pairs of drugs, identify both new biomarkers that predict treatment response, drugs that can potentially be repurposed and new drug combinations that can be implemented for future treatment of cancer patients.
Superresolution STED microscopy
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Open to view video. The attainable spatial resolution of light microscopy is fundamentally limited, due to the diffraction of light. However, in recent years, several superresolution microscopy techniques have been implemented to overcome this limitation. One of those techniques is STED microscopy. STED microscopy is a beam scanning approach that allows the imaging of sub-cellular structures in living and fixed cells at the nanoscale. We utilize STED microscopy to investigate the detailed structure of mitochondria, the “cellular power plants”. All human cells contain mitochondria and they are essential for eukaryotic life and their dysfunctions are associated with numerous devastating diseases. Mitochondria contain multiple copies of their own DNA, which are organized in DNA-protein-structures called nucleoids. With a size of down to 50 nm and their tendency to form clusters, they are notoriously difficult to be visualized by classical microscopy, but amenable for the analysis by superresolution microscopy. Numerous questions on the biology of nucleoids are still unsolved. To investigate the characteristics of mitochondrial nucleoids, I measured the activity level of more than 200.000 nucleoids in hundreds of cells. Using three-color STED microscopy, the activity levels of these nucleoids could be quantified, which provides novel insights in the functional heterogeneity of the nucleoid populations in cells. While the data-evaluation was computer-based and fully automated, the recording of this huge amount of images as well as designing the imaging schemes, defining imaging parameters and adjusting the microscope was done manually. I will discuss how STED microscopy can benefit from automation and point out crucial and error prone steps.