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Podium Abstract Compendium

Podium presentations are organized into 10 educational tracks. Podium abstracts and speaker information are organized first by track and then by session below.

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To view a complete schedule of podium presentations and schedule of events for SLAS2019 and to view speaker bios and photos, please visit the SLAS2019 Event Scheduler.

Advances in Bioanalytics and Biomarkers

Track Chairs: Shaun McLoughlin, Abbvie and Andreas Luippold, Boehringer Ingelheim (Germany)

Biomarker Discovery in Disease Relevant in vitro and Related in vivo Models

Session Chair: Martin Giera, Leiden University Medical Center

  • Application of acoustic mist ionisation mass spectrometry for metabolic profiling – case study in hepatic toxicology
    Delyan Ivanov, Discovery Sciences

    Acoustic Mist Ionization Mass Spectrometry (AMI-MS) is the hyphenation of a Waters Xevo G2XS time of flight (ToF) mass detector with an acoustic sampling interface. This new technology enables direct injection of samples from a standard 384 well plate into the mass spectrometer at very high-throughput. There are several advantages of using acoustics to load samples into the mass spectrometer, firstly it is non-contact so there is no carry over between sampling events. Secondly, the sample volume is very small, typically 15 Nano-Litres per second and thirdly, the acoustics can fire very quickly (1400Hz). This technology has been broadly applied within AstraZeneca to support biochemical high-throughput screening (HTS), we routinely process in excess of 100,000 samples per day.

    Having established this technology within HTS we have recently looked to expand the application of this technology into cell screening. Cellular applications offer significant challenges to AMI-MS, since this is a direct infusion MS system, there is no chromatography or separation technology between the acoustic sampling and the mass detector, suppression can be a significant issue. Preliminary experiments were carried out with adherent cells grown in standard 384 well plates. The culture medium is removed and the cells washed with ammonium formate before the cells are lysed in 50 micro-Litre of water. Using the time of flight MS scanning across ~2000Da range it is possible to generate a “finger print” spectra from the cells. This “finger print” contains examples of the most abundant metabolites present in the lysate including lipids, amino acids, sugars and nucleotides. Typically, less than 10,000 cells per well are required to generate a “finger print” and sampling times are typically in the range of 6-10 seconds per well (90-150nL). Since only very small sample volumes are taken from each well it is possible to generate significant numbers of technical replicates from each well. In addition, since we are working with adherent cell lines it is possible to have multiple biological replicate on the same plate.

    While it was interesting to demonstrate the ability to generate reproducible “finger prints” from cell lysates, the ability to demonstrate that compound treatment could perturb the “finger print” in a biologically relevant manor was our ultimate goal. We will share some “finger print” data from or our early work using hepatocyte cells treated with known DILI compounds. There are multiple examples where metabolic “finger prints” change on treatment and these changes are consistent with the known mechanism of action of the DILI compounds.

  • SLAS2019 Innovation Award Finalist: Live-cell Gene Imaging Nanotechnology for Cells, Tissue and Pre-clinical Abnormal Scarring Challenges
    David Yeo, Nanyang Technological University

    Live-cell imaging is critical to advancing biomedicine. For example, reporter constructs rely on (viral) integration to enable real-time gene monitoring. However, these result in: viral-induced mutations, laborious clonal selection processes and gene reporter re-design. In addition, fluorescence proteins have similar emission wavelengths to tissue auto-fluorescence, hence suffer poor signal-background ratio. On the other hand, contrast agent cell-labelling often lacks molecular specificity, resulting in highly misleading false positive signals. Our experience suggests that nanotechnology tools readily enable gene expression imaging and increase biomarker detection specificity. We have shown they are easy-to-use, have great selectivity and are highly versatile (simple biomarker re-configuration and near-infrared imaging).Abnormal scars are characterized by excessive fibrosis due to dysfunctional wound healing. Despite occurring in 1:12 of the developed world’s population, no satisfactory therapy exists. Furthermore, no reliable method prognosticates their emergence during early wound recovery. In response, we developed nanotechnology biosensors (nanosensors) to facilitate the following: 1) efficient drug screening; and 2) non-invasive, early scar detection and monitoring.1) To date, no drug screening study has identified suitable anti-scarring drugs. We developed a Fibroblast activation protein(FAP)-? Probe: FNP1, which is specifically and rapidly activated by gelatinases to trigger NIR fluorescence. We demonstrate screening utility with abnormal scar fibroblasts, TGF-?1, anti-fibrotic drugs, inhibitors and stimulants with undefined properties. Following validation against known anti-fibrotic treatment, compounds ‘R’ and ‘T’ were discovered to possess anti-scarring properties and further validated with gene expression and immunoassay analysis.2) Abnormal scar prognosis prior to full manifestation can only be achieved by skin biopsies in addition to further processing and analysis. However, biopsies are limited by: invasiveness, pain, inconvenience, and further scarring and infection complications. In response, we pioneered the concept of topically-applied nanoparticles to probe mRNA non-invasively. NanoFlares - highly-ordered nucleic acids surrounding a nanoparticle core, were chosen for their skin-penetrative properties. These comprise recognition and reporter elements that alter fluorescence emission properties upon target hybridization. NanoFlares targeting connective tissue growth factor (CTGF) demonstrated specificity in solution, cells, ex vivo (human) engineered tissue and animal models (mice, rabbits). Notably, NanoFlare performance was validated with non-coding, uptake NanoFlares, gene expression analysis against functional measures of abnormal scarring.I will elaborate on the critical role nanotechnology can play in abnormal scar therapy and diagnostic development. Specifically, FNP1 is an easy-to-use nanosensor that rapidly identifies novel anti-scarring drug or drug combinations. We also demonstrate the first-ever instance of biopsy-free skin diagnosis using topically-applied NanoFlares validated by several abnormal scarring models. Crucially, gene-based molecular imaging with nanotechnology may dramatically alter healthcare paradigms for skin diseases.

  • Screening a secretome library to discover novel biology and targets relevant to drug discovery
    Lovisa Holmberg Schiavone, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden

    Secreted proteins regulate human physiology by transducing signals from the extracellular environment into cells and regulating different cellular phenotypes. The human secretome represents a small (~2200 proteins) and biologically relevant screening library that can be used in phenotypic assays. Here, we have used a high-throughput mammalian cell factory approach to generate separately purified and quality assured human secreted proteins. A sample storage and handling process has been established to enable screening of the proteins, at known concentrations, in different cell-based assays. Screening 1000 proteins from the human secretome we show that the FGF9 subfamily, FGF9 and FGF16, are strong proliferators of cardiac progenitor cells. Using the library, we demonstrate that the effect of FGF16 is specific to the cardiac progenitor cells, with no observed effect on cardiac fibroblast proliferation. Additional biophysical binding experiments, using cardiac fibroblasts and cardiac progenitor cells immobilized on a biosensor surface, showed that the interaction of FGF16 and FGF9 with cells on the surface was additive. This suggests that the proteins are signaling through different receptors. Altogether, the data demonstrates how a secretome library can be used across a panel of assays to uncover novel functional information and to aid the discovery of novel signaling pathways and targets relevant to drug discovery.

  • Lipidomics and Metabolomics in drug research, a case study for dehydrocholesterol reductase 24
    Martin Giera, Leiden University Medical Center

    Over the last decades metabolomics and lipidomics technologies have mainly been applied to biomarker type of studies, aimed at the discovery of novel diagnostic means. However, just recently our view on metabolomics technologies and the metabolome itself, is changing. With advanced data sciences, network analysis and an increasing understanding of metabolism and physiology more and more studies are focusing on the application of omics-techniques in the context of activity metabolomics, target identification, drug effect evaluation as well as the metabolome as a possible drug target.

    In this lecture we will discuss the use of advanced analytical techniques in drug research aimed at altering bioactive endogenous metabolites. Using the example of dehydrocholesterol reductase 24, a membrane bound enzyme in distal cholesterol biosynthesis and its role in inflammation, we will discuss target identification and selectivity assessment using gas chromatography mass spectrometry in a whole cell screening assay. Subsequently we will discuss phenotypic observations obtained with an optimized chemical probe coming from this screening effort, showing promising results in a murine zymosan A induced peritonitis model. We will discuss the observed drug effects and show how advanced lipidomics and metabolomics techniques were used in order to decipher the mechanism of action of the chemical probe under investigation. Ultimately a comprehensive picture of the obtained phenotypic findings based on the combined use of lipidomics, genomics, protein array analysis and the use of a radioactively labeled probe will be sketched, allowing to illustrate how modern bioanalytical techniques go hand in hand and allow to obtain a detailed understanding of drug, gene, protein, metabolite and phenotype interactions.

Label Free Bioanalytical Techniques

SamdiSession Chair: Daniel Bischoff, Boehringer Ingelheim

  • Chemoproteomics and Thermal Protein Profiling in Target Discovery and Validation
    Friedrich Reinhard, Cellzome GmbH - a GSK Company

    Understanding the mode of action and the selectivity of small molecules in a complex cellular system comprise a major challenge in drug discovery. This knowledge is key to interpret their pharmacological effect and thus to identify and validate their molecular target(s). This presentation discusses state-of-the-art methods for the direct identification of small molecule targets and provides an overview of successful applications in our labs.The common theme of these methods is the use of quantitative mass spectrometry as unbiased way to investigate the interaction of small molecules with the proteome of either intact cells or cell extracts. We have established a two-pronged strategy using the orthogonal methods of thermal proteome profiling (TPP), based on the cellular thermal shift assay (CETSA), and affinity- or activity-based chemoproteomics with functionalized analogues of active compounds as capturing matrix. The functionalized analogues can subsequently help to validate the target(s) further by conjugating them to different functional tags e.g. to enable target localization (imaging-based) or target degradation in cells.We have used these chemoproteomics technologies to reveal unkown and unexpected binding partners of known drugs helping to understand the toxicity and side effects. Following phenotypic screens, these approaches have been applied successfully in our labs to identify therapeutically valuable targets. Therefore, chemoproteomic studies have opened up new avenues for drug discovery.

  • Exploiting the Potential of Ultra High Throughput Mass Spectrometry Approaches to Drug Discovery
    Melanie Leveridge, GlaxoSmithKline, Stevenage UK

    As a direct analysis, label free technology, mass spectrometry enables assays to be generated that monitor native analytes without the requirement for substrate/product modifications or indirect detection methods. It can have a dramatic impact on hit to lead and lead optimization stages of drug discovery by eliminating false positive/negative results typically associated with fluorescent screening technologies.

    Traditionally, however, MS-based techniques have been relatively slow and thus not suited for high throughput applications. Recent advances in mass spectrometry instrumentation, automation, software and low volume dispensing have enhanced its potential to be adapted to higher throughput approaches, under physiologically relevant conditions, and at sample volumes compatible with hit identification/lead generation screening.

    Here we describe the application of a variety of high throughput mass spectrometry approaches to lead discovery and our strategy for deploying them in a complementary way to create a suite of label free assay formats to address questions in discovery. This includes the application of Affinity Selection Mass Spectrometry to prioritise small molecule drug targets entering the discovery pipeline, the development of an automated Matrix-Assisted Laser Desorption / Ionization Time-Of-Flight (MALDI-TOF) MS platform for screening and compound profiling, and the evaluation of Acoustic-Mist MS to study kinetics.

  • High-Throughput ESI-MS Enabled by the Acoustic Droplet Ejection to the Open-Port Probe Sampling Interface
    Chang Liu, Sciex

    Label-free Liquid Chromatography/Mass Spectrometry (LC/MS) based screening technology is routinely used in early drug discovery, especially for the high throughput ADME screening. Although the current analysis speed of <30 seconds per sample is quite promising, it still cannot match the throughput provided by plate-reader based High Throughput Screening (HTS) platforms. Acoustic droplet ejection (ADE) is a droplet transfer technology capable of high speed, reproducibility, and absolute accuracy. In this work, we couple the ADE and the standard Electrospray Ionization (ESI) ion source of a mass spectrometer with the open-port probe (OPP) sampling interface. Screening speeds as fast as 0.4 seconds-per-sample are demonstrated with high sensitivity, high reproducibility, wide linear dynamic range, good quantitation capability, no ion suppression from various biological/reaction matrix, and broad compound coverage. The continuous-flow of carrier solvent for the OPP maintained the ionization stability and actively cleaned the entire flow system resulting in no observed carry-over. The advantages of this integrated system have been demonstrated with various drug discovery workflows.

  • Ultrahigh-Throughput Screening of Chemical Reactions Using MALDI-TOF MS and Nanomole Synthesis
    Sergei Dikler, Bruker Corporation

    There is an extremely large published body of work in synthetic organic chemistry describing reactions with high yield. However, negative results when chemical reactions do not generate the desired product or the product has low yield are rarely published or presented. The knowledge of types of starting materials and conditions that do not work for the selected reaction type is very important. This knowledge can be quickly generated by ultrahigh-throughput screening (uHTS) of many starting materials in multiple reaction conditions using MALDI-TOF mass spectrometry. In this work we focused on Buchwald-Hartwig reaction, which is a C-N coupling between cyclic secondary amines and N-heterocycle-containing aryl bromides using four different catalysts.

    Nanomole-scale reactions were run in glass 1536 well plates using Cu catalyst, Pd catalyst, Ir/Ni photoredox catalyst and Ru/Ni photoredox catalyst as four different conditions for each of the reactions. The reaction mixtures were spotted on HTS MALDI targets in 1536 format using a 16-channel positive displacement liquid handling robot. These targets were analyzed on the new generation MALDI-TOF instrument equipped with a 10 kHz scanning beam laser, significantly faster X, Y stage and faster target loading/unloading cycle. The readout speed for a MALDI target in 1536 format was in 8-11 min range depended on the number of laser shots.

    In the first screening approach we selected a reaction between the simplest cyclic secondary amine and the simplest N-heterocycle-containing aryl bromide and added 383 simple and complex fragment molecules to evaluate catalyst poisoning using four catalytic methods (1536 experiments). Deuterated form of the product was added for ratiometric quantitation of the MALDI product response. The fragment molecules were identified as catalyst poisons (>50% signal knockdown) and non-poisons (2 was 0.85.

    In the second screening approach the simplest cyclic secondary amine was reacted with 192 aryl bromides of increasing complexity and the simplest N-heterocycle-containing aryl bromide was reacted with 192 cyclic secondary amines of increasing complexity using the same 4 catalytic methods (1536 experiments). Direct correlation with UPLC-MS data was lower since MALDI signals of structurally diverse products were normalized against single internal standard. Nonetheless the normalized MALDI signal was successfully used to create binary reaction success/failure threshold of 20% and the detected trends were essentially identical to those from the UPLC-MS data. This novel uHTS workflow for synthetic reactions based on MALDI-TOF MS is the first step on a road to predicting chemical reactivity and reaction success, which has potential to decrease the number of unsuccessful experiments for organic chemists.

Target and Mechanism Identification After Phenotypic Screens

Session Chair: Jonathan Lee, PDD4Patients LLC

  • Mechanism of Action and Molecular Target follow-up following a Phenotypic Screen
    Jonathan Lee, PDD4Patients LLC

    Empirical or phenotypic drug discovery (PDD) is a target agnostic strategy which contributes a disproportionate number of first in class drugs. The approach also arguably provides a unique means to identify and develop tool compounds to investigate the function of the "unliganded" human proteome (estimated to be >90%). Although development of first in class medicines is of high commercial value, difficulties in translational target validation, has encouraged a "me too" drug development philosophy where pharma efforts tend to focus on a relatively small number of "highly validated" molecular targets. Significantly, broad adoption of PDD is limited by difficulties in obtaining information on the mechanism of action and/or molecular target of phenotypic actives.

    The presentation will provide a context and perspective of these issues by examining (1) the status of the unliganded proteome and its impact on drug discovery, (2) results from a phenotypic angiogenesis screen which identified non-kinase inhibitor leads, (3) identification of novel molecular targets and molecular mechanisms modulating angiogenesis through database mining and functional profiling of phenotypic actives, and (4) presentation/discussion of a literature study that successfully identified a molecular target of a phenotypic active but interestingly failed to reveal detailed mechanism of action information.

  • Hit Dissection and Target Identification from a Cell Viability Chemogenomic Screen
    Shaun McLoughlin, AbbVie

    Presentation information will be posted shortly.

  • SLAS2019 Innovation Award Finalist: Digging into molecular MOA’s with high-content imaging and deep-learning
    Sam Cooper, Phenomic AI

    Machine and deep learning models demonstrate incredible performance when it comes to extrapolating what we know already, in what are collectively called supervised approaches. For example, we’re now able to reduce raw imaging data from large high-content screens, where positive and negative control data exists, into accurate readouts of activity in mere minutes, as well as accurately predict a compounds MOA if it’s already been seen. However, when we’re presented with an unknown MOA, machine learning approaches will typically scan right over it, either missing it entirely or incorrectly assigning it to an existing MOA. By developing novel ‘unsupervised’ deep-learning models alongside high-content assays tailored for computational analysis, we’re able to group compounds by the similarity of their molecular MOA, with no prior knowledge, against a disease phenotype; thus, improving our ability to select the most exciting and novel hits for follow-on development.

  • Navigating the Small Molecule Target Deconvolution Challenge within Unprecedented Target Space
    Scott Warder, AbbVie

    Phenotypic Drug Discovery (PDD) is an attractive strategy for accessing novel target space, but successful implementation has been significantly hampered due to the long cycle times required to translate hits into targets. In the Pharma industry, hit progression in the absence of a defined target has been reserved for unique opportunities, and has not been widely adopted. With this challenge in hand, the two major themes we have focused on are PDD hit prioritization, and technology integration for identifying small molecule (SM) mechanisms and targets. In addition to assay panels (kinome, ion channels, etc.), we have committed to broad-endpoint biological assays, such as L1000 and BioMAP profiling. The breadth of perturbations captured in these companion reference databases not only enable ruling out common mechanisms of action, but provide data to support a measure of compound uniqueness. This also ensures that SMs with a stronger biological rationale are nominated for target deconvolution campaigns. For target identification, we advocate for label-free approaches, even when we have committed to synthesizing linked probes for target enrichment technologies such as Affinity Capture-MS, and SM-Phage display. Current label-free approaches that have delivered strong value include positive selection pooled whole genome CRISPR/CRISPRa screens to reveal nodes that assist functionalizing deep-dive time course phospho-proteomic profiling. Case studies to support the progression of hits into unprecedented target space will be presented.

    Disclosures: All authors are employees of AbbVie. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication.

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Assay Development and Screening

Track Chairs: Ralph Garripa, MSKCC and Deb Nguyen, Novartis - GNF

Advanced Imaging-based Assays and Phenotypic Profiling

Session Chair: Shannon Mumenthaler, University of Southern California

  • Single cell-based investigations of endocrine disrupting chemicals by high content analysis
    Fabio Stossi, Baylor College of Medicine

    Our lab has a longstanding interest in single cell analysis-based transcription studies.We have developed novel mechanistic and phenotypic approaches to study transcription within a cellular context, but with sensitive, high throughput approaches.Our main platform allows us to quantify transcription using high throughput microscopy and image analytics that are designed to link, at the single cell level, mRNA synthesis to DNA binding and promoter occupancy of nuclear receptors (NRs) and coregulators,histone modifications and large-scale chromatin modeling. A growing list of endocrine disrupting chemicals (EDC) from the environment have been shown to target NRs, including estrogen and androgen receptors (ER, AR), with large scale efforts to develop and test environmental samples for EDC activities.Our multiplex assays with engineered cells or tumor cell lines endogenously-expressing ER/AR are currently being used to assess individual or mixtures of known hormones and EDCs via machine learning approaches.These studies are currently being applied to environmental samples obtained Galveston Bay and the Houston Ship Channel via new funding from the NIEHS Superfund Research Program.

  • Mechanical Trap Surface-Enhanced Raman Spectroscopy for Live Three-Dimensional Molecular Imaging of Single Cells
    Santosh Paidi, Johns Hopkins University

    Label-free three-dimensional (3D) chemical imaging of live cells is critical to map molecular distributions and determine their dynamics in various physiological and pathological transformations of single cells. Yet, existing optical tools are limited by their inability to offer the desired combination of 3D structural information and endogenous molecular contrast. To facilitate such non-perturbative monitoring of single live cells (without loss of information due to averaging in population analyses), we report an approach for trapping arrays of single live cells and profiling intrinsic molecular signatures that collectively enable the monitoring of intracellular events without targeting specific epitopes. Our approach, named mechanical trap surface-enhanced Raman spectroscopy (MT-SERS), employs a combination of nanoparticle coated self-folding microgripper shaped devices and surface-enhanced Raman spectroscopy (SERS). These microgrippers, which roll-up due to tailored residual stresses induced during fabrication, offer a facile biocompatible platform to precisely trap single live cells for longitudinal analysis without the need for any batteries or external power sources. By leveraging functionalization of the inner surfaces of these traps with plasmonic nanostars, the MT-SERS method permits excellent SERS enhancement, which facilitates label-free molecular interrogation without any photodamage to the cells. We show that the developed platform reliably detects intrinsic chemical signatures over trapped microbeads as well as over a single trapped cell, and thus providing a multiplex volumetric distribution of analytes, such as lipids and nucleic acids. Taken together with the demonstrated ability to track compositional changes in dry, fluid and untethered environments, our findings underscore the potential of MT-SERS to furnish biologically interpretable and quantitative molecular maps, and therefore also opens the door for the elucidation of intercellular variability in normal and diseased cell populations.

  • Combining machine learning and microscopy in drug discovery: from high throughput screening to cell activity prediction
    Lina Nilsson, Recursion Pharmaceuticals

    This talk will share novel techniques for combining imaging assays and machine learning techniques to speed up, de-risk, and reduce costs across the pharmaceutical drug discovery pipeline. We will describe how at the start of the drug discovery process, high throughput screening (HTS) assays and computer vision tools can be developed hand-in-hand to design unbiased, autonomous assay platforms capable of detecting nuanced signals. From this combined data science - phenotypic profiling approach, other powerful novel tools can be developed. As one example, we will show how a small set of initial microscopy assays can be combined with reinforcement learning models for powerful in silico prediction of future drug compound activity. Finally, we will share recent progress on combining machine learning and phenotypic profiling to predict drug compound behavior along the rest of the drug discovery pipeline, such as warnings against likely toxicology.

    This talk will share novel techniques for combining imaging assays and machine learning techniques to speed up, de-risk, and reduce costs in the pharmaceutical drug discovery pipeline. We will describe how at the start of the drug discovery process, high throughput screening (HTS) assays and computer vision tools can be developed hand-in-hand to design unbiased, autonomous assay platforms capable of detecting nuanced signals. From this combined data science - phenotypic profiling approach, other powerful novel tools can be developed. As one example, we will show how a small set of initial microscopy assays can be combined with reinforcement learning models for powerful in silico prediction of future drug compound activity. Finally, we will share recent progress on combining machine learning and phenotypic profiling to predict drug compound behavior along the rest of the drug discovery pipeline, such as warnings against likely toxicology.

  • Screening patient-derived colorectal cancer models to interrogate tumor-stromal interactions and drug response

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