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  • Label-free Raman spectroscopy for rapid identification of biologics

    Contains 1 Component(s) Recorded On: 02/06/2018

    We have developed partial least squares-discriminant analysis derived decision algorithms that provide near-perfect classification accuracy in predicting the identity of these drugs based on the subtle, but consistent, differences in their spectra, which are otherwise invisible to gross visual inspection. We have shown that the performance of the decision algorithm with plasmon-enhanced Raman spectroscopy, even at much lower biologic concentrations, is comparable with that of spontaneous Raman spectroscopy. Together, these results establish the feasibility of developing an automated non-perturbative spectroscopic pipeline for rapid identification and quality control during manufacturing and fill-finish testing of biologics – thus alleviating the principal limitations of conventional wet chemistry analyses.

    Monoclonal antibody based biologics are gaining immense popularity as therapeutic agents to treat a wide array of diseases such as cancer and inflammation. As a result, increasing number of biologics are currently undergoing clinical development and approval for clinical translation. Such a rise in demand for biologics necessitate the development of rapid, label-free and automated characterization tools for meeting stringent quality control requirements during their manufacturing. To meet regulatory requirements and reduce business risk associated with fill operations, accurate identification of the drug products is a critical and necessary analysis during multiple stages of manufacturing and distribution. However, due to high similarity in the chemical structures of these drugs, establishing product identification is challenging and the traditional wet lab techniques are destructive, labor intensive and expensive to perform multiple times during production and, even more so, for fill finish testing. Therefore, there is an urgent need for quick, inexpensive and reliable methods for biologics identification. Here, we report the first application of spontaneous and label-free plasmon-enhanced Raman spectroscopy coupled with multivariate data analysis for identification of a cohort of closely related human and murine antibody drugs. Building on finite difference time domain simulations, we synthesized nanoparticles of optimal morphology to compare the feasibility of performing SERS-based bulk sample detection with that of spontaneous Raman spectroscopy. We have developed partial least squares-discriminant analysis derived decision algorithms that provide near-perfect classification accuracy in predicting the identity of these drugs based on the subtle, but consistent, differences in their spectra, which are otherwise invisible to gross visual inspection. We have shown that the performance of the decision algorithm with plasmon-enhanced Raman spectroscopy, even at much lower biologic concentrations, is comparable with that of spontaneous Raman spectroscopy. Together, these results establish the feasibility of developing an automated non-perturbative spectroscopic pipeline for rapid identification and quality control during manufacturing and fill-finish testing of biologics – thus alleviating the principal limitations of conventional wet chemistry analyses.

    Santosh Paidi

    Department of Mechanical Engineering, Johns Hopkins University

    Santosh Paidi is a graduate student in the Department of Mechanical Engineering at Johns Hopkins University. His current research efforts in Dr. Ishan Barman’s lab are directed towards application of Raman spectroscopy and multivariate data analysis to develop novel quantitative approaches for addressing unmet needs in life sciences. A major focus of Santosh’s graduate study is the development of non-perturbative tool for rapid identification of closely related biologics in real time during their manufacturing, with the ultimate goal of translation to fill-finish sites. Prior to commencing graduate study at Johns Hopkins University, Santosh obtained a B.Tech in Mechanical Engineering and a minor in Aerospace Engineering from Indian Institute of Technology Bombay.

  • ExVive™ 3D Bioprinted Tissue Modeling of Liver Injury and Disease In Vitro

    Contains 1 Component(s) Recorded On: 02/06/2018

    Here we characterize progression of TGFβ-mediated induction of fibrosis and blockade by Galunisertib as evidenced by key biomarkers, cytokine production, regulation of fibrotic genes, and collagen deposition, collectively demonstrating the utility of the model for the evaluation of interventional strategies and providing evidence of clinically relevant pathway modulation in vitro.

    Successful prediction of candidate drugs can be hampered by the lack of in vitro tools to model complexities of human tissue biology.  This translational challenge can result in low safety and efficacy predictability and contribute to attrition in drug development.  To bridge this gap, the Organovo NovoGen® Bioprinting Platform was utilized to develop ExVive™ 3D Bioprinted Tissues, fully cellular 3D tissue models fabricated by automated spatially controlled cellular deposition.  The multicellular architecture of the 3D model can better recapitulate native tissue structure and function compared to standard in vitro models, allowing for complex, tissue-level phenotypes associated with chronic injury and disease.  Biochemical and histological characterization demonstrates that ExVive™ Human Liver Tissue enables detection of compound-induced progressive liver fibrogenesis.  Here we characterize progression of TGFβ-mediated induction of fibrosis and blockade by Galunisertib as evidenced by key biomarkers, cytokine production, regulation of fibrotic genes, and collagen deposition, collectively demonstrating the utility of the model for the evaluation of interventional strategies and providing evidence of clinically relevant pathway modulation in vitro.

    Kelsey Retting

    Organovo Inc.

    Kelsey Retting is an Associate Director at Organovo Inc. designing functional human tissues using proprietary three-dimensional bioprinting technology.  Prior to Organovo, Kelsey worked in drug discovery at Pfizer supporting the development of novel biotherapeutics for metabolic disease.  Kelsey has a PhD from the University of California, Los Angeles in Biological Chemistry.

  • A Quantitative Target Engagement Approach to Profile Compound Affinity and Residence Time Across Enzyme Classes In Live Cells

    Contains 1 Component(s) Recorded On: 02/06/2018

    Intracellular target selectivity is fundamental to pharmacological mechanism. Although there are currently a number of acellular techniques to quantitatively measure target binding or enzymatic inhibition, no biophysical approach exists that offer quantitative, equilibrium-based analysis of target engagement across enzyme classes in live cells. Here we report the application of an energy transfer technique (NanoBRET) that enables the first quantitative approach to broadly profile target occupancy, compound affinity, and residence time for a variety of target classes including kinases and chromatin modifying enzymes.

    Intracellular target selectivity is fundamental to pharmacological mechanism. Although there are currently a number of acellular techniques to quantitatively measure target binding or enzymatic inhibition, no biophysical approach exists that offer quantitative, equilibrium-based analysis of target engagement across enzyme classes in live cells. Here we report the application of an energy transfer technique (NanoBRET) that enables the first quantitative approach to broadly profile target occupancy, compound affinity, and residence time for a variety of target classes including kinases and chromatin modifying enzymes. The NanoBRET method allows for broad kinome profiling of inhibitor selectivity against nearly 200 kinases, and enables a mechanistic interrogation of the potency offsets observed between cellular and acellular analysis. Compared to published biochemical profiling results, we observed an improved intracellular selectivity profile for certain clinically-relevant multi-kinase inhibitors. Due to high levels of intracellular ATP, a number of putative drug targets are unexpectedly disengaged in live cells at a clinically-relevant drug dose. The energy transfer technique can also be performed in real time, allowing for measurements of drug residence time. Broad kinase profiling of compound residence time reveals surprising kinetic selectivity mechanisms.

    Matthew Robers

    Promega Corporation

    Matthew Robers is a Senior Research Scientist and Group Leader at Promega Corporation. Matthew received his B.A. Degree in the Dept of Genetics and his M.S. Degree in the Dept of Bacteriology at the University of Wisconsin - Madison. Matthew has authored over 20 peer-reviewed publications and published patents on the application of novel assay chemistries to measure intracellular protein dynamics. Matthew's team currently focuses on the development of new technologies to assess target engagement, and has developed a biophysical technique for quantifying compound affinity and residence time at selected targets within intact cells.

  • UPT and SCLS, two unique workflow for Drug Target Identification

    Contains 1 Component(s) Recorded On: 02/06/2018

    Ever-existing need of identifying the targets of bioactive molecules is recently fuelled by resurgence of Phenotypic Screenings in drug discovery. We will be presenting advantages and case studies of two of our proprietary target identification technologies that can be utilized, in tandem, at different stages of drug development.

    Ever-existing need of identifying the targets of bioactive molecules is recently fuelled by resurgence of Phenotypic Screenings in drug discovery. We will be presenting advantages and case studies of two of our proprietary target identification technologies that can be utilized, in tandem, at different stages of drug development. At ‘Hit’ stage, where compound SAR information is limited, Universal Unique Polymer Technology (UPT), that allows enrichment of targets of underivatized molecule, can be applied in narrowing / identifying the targets of the bioactive molecules. UPT relies on immobilizing the compound utilizing non-covalent, weak-interaction forces of the molecule on a polymer surface, that provides complementary weak-interaction forces for immobilization. Compound immobilized on the polymer are quantified and thus prepared compound specific matrices are incubated with the biological lysate for affinity capture of the target. Captured target proteins are eventually identified using Mass-Spectrometry and the specificity of capture is assigned by comparing the proteins identified from multiple compound loaded and control polymer matrix surface. Key advantage of UPT is that it allows affinity enrichment of target without compound derivatization. For the compounds that have travelled to ‘lead’ stage of development and SAR of the compound is well defined, a SubCellular Location Specific (SCLS) Target Capture Technology is utilized in confirming the identity and the subcellular location of the target. In SCLS, compound of interest is tagged to different subcellular location specific peptide probes. In multiple experiments, probes localize the compound at different cellular location and functional activity of the compound is recorded. The subcellular location, that shows maximum functional response, is then chosen as the target enriched compartment and utilized for target capture experiments. Antibody against the peptide allows the recovery of the probe and the affinity captured protein targets. Eventually captured target proteins are identified using Mass-Spectrometry. Key advantage of SCLS is that it allows investigating the target and mechanism of action in subcellular location manner. For critical evaluation of these new methods, along with the success examples, limitations of these methods will also be presented. 

    Chaitanya Saxena

    Shantani Proteome Analytics Pvt. Ltd.

    With 12+ years of experience in utilizing biophysical techniques to solve the relevant problems in life sciences, Chaitanya is a seasoned drug discovery professional. Chaitanya completed his Ph.D. in Biophysics from Dept. of Physics, The Ohio State University, Columbus, Ohio, USA and later worked with Eli Lilly and Company at Indianapolis. Presently he is serving as Chief Executive Officer at Shantani Proteome Analytics Pvt. Ltd. (Shantani), a wet-lab technology based company founded by him and Dr. Yanping Yan at Venture Center, National Chemical Laboratory Innovation Park, Pune, India. Shantani provides drug target deconvolution technologies to drug discovery organizations and proteomics and protein chemistry based path-forward solutions to bio-pharma and other life-sciences based companies. Chaitanya has published several scientific articles in divers’ scientific areas ranging from the Femto-second electron transfer processes in biology to the applications of Mass Spectrometry in drug-discovery.

  • Towards a comprehensive strategy to target identification and mode of action elucidation for bioactive small molecules

    Contains 1 Component(s) Recorded On: 02/06/2018

    ​Target identification and the elucidation of mechanism of action (MoA) for bioactive small molecules are key steps in phenotypic and pathway-centric approaches to drug discovery. In recent years, various strategies have been introduced and refined that address these questions from different angles and provide glimpses of different aspects of the often complex physical and functional interactions of a compound when exerting its biological effects in vivo.

    Target identification and the elucidation of mechanism of action (MoA) for bioactive small molecules are key steps in phenotypic and
    pathway-centric approaches to drug discovery. In recent years, various strategies have been introduced and refined that address these
    questions from different angles and provide glimpses of different aspects of the often complex physical and functional interactions of
    a compound when exerting its biological effects in vivo: Affinity-based approaches aim to describe the (protein) interactome of drug
    candidates which constitutes the full spectrum of potential efficacy and off-targets. These include quantitative chemoproteomics such
    the combinations of small molecule affinity chromatography or photo-affinity labeling with mass spectrometry-based protein
    identification and quantitation, as well as large scale implementations of biophysical approaches in vitro such as size-exclusion
    chromatography. On the other hand, a variety of functional genetic and genomic strategies have been introduced that include
    (unbiased or targeted) generation of compound-resistant cells followed by identification of the resistance-conferring genetic changes
    by next generation sequencing as well as the utilization of genome-wide knock-down and deletion approaches including RNAi and
    CRISPR. In these cases, the generation of target/MoA hypotheses is based on the elucidation of functional relationships between a
    gene and the compound-induced phenotype. In contrast to the individual protein or gene resolution provided by these former
    strategies, cellular profiling approaches interrogate the overall cellular response to compound treatment at the level of signaling, gene
    expression, viability or metabolism. Finally, knowledge-based approaches rely on empirical and computational approaches and a
    reference collection of compounds with known targets and MoA to make inferences. Since the various approaches provide orthogonal
    information and have unique strengths, multipronged strategies are best suited to provide a comprehensive picture of the target/MoA
    space of a bioactive compound and ultimately enable successful elucidation of the efficacy target and its functional link to the
    phenotype under investigation. The various classes of target ID platforms will be presented and discussed in the context of real-life
    applications.

    Markus Schirle

    Novartis Institutes for Biomedical Research

    Markus Schirle received his master’s degree in biochemistry at University of Tubingen, Germany and his Ph.D. in 2001 for his work with H.G. Rammensee/Tubingen on the identification of disease-associated MHC peptides by mass spectrometry-based approaches. After postdoctoral studies in proteomics with Alfred Nordheim/Tubingen, he joined Cellzome (Heidelberg, Germany, now part of GSK) from 2001 – 2007 where his work focused on protein and small molecule affinity proteomics for target identification (ID) and mechanism of action (MoA) studies. Since joining Novartis in 2007, he established a platform for chemical and affinity proteomics for target ID and MoA studies; since 2013 he is group leader for the ChemGx hub which also covers biophysical and biochemical in vitro methods for studying protein–compound interactions. The hub is responsible for affinity-based approaches to target ID/MoA and hit finding for Novartis drug discovery projects globally.

  • A Low-Cost Open-Source Cloud-based Liquid Handling Robotic Platform for Performing Remote Real-Time Collaborative Experiments

    Contains 1 Component(s) Recorded On: 02/06/2018

    We have developed a robotic-system capable of performing routine liquid-handling experiments, as well as artificial chemical life experiments. Our platform consists of an actuation-layer on top, an experimental-layer in the middle, and a sensing-layer at the bottom. The actuation-layer comprises the robot-head and modules mounted on it.

    We have developed a robotic-system capable of performing routine liquid-handling experiments, as well as artificial chemical life experiments. Our platform consists of an actuation-layer on top, an experimental-layer in the middle, and a sensing-layer at the bottom. The actuation-layer comprises the robot-head and modules mounted on it. The modules, e.g. pipet-modules, OCT-scanner, extruder, PH-probe, are designed to perform actions on experiments. The head holds modules and moves in the horizontal plane. The experimental-layer holds the reaction vessels. The sensing-layer consists of a camera below the experimental-layer to monitor the experiment. It collects data from experiment, and provides feedback for robot to interact with experiment. 

    To develop an open-source multi-platform user-interface for remote real-time control of our robotic-system, we decouple user software for programing experiments from robot control-software. Therefore, we use an integrated controller-hardware, namely a Raspberry-Pi3 single-board computer, instead of a dedicated computer. The resulting platform eases software management as installing, and managing software libraries required for feedback-based experiments on different hardware, and operating-systems was difficult. Furthermore, it is affordable owing to the low cost of Raspberry-Pi. This approach also enables us to implement a cloud-based software architecture for our platform. 

    The cloud-based software architecture for our robotic-system provides resource sharing and reusability of experiment protocols, the ability to work on the robotic-system collaboratively, and parallelizing experiments on different robotic-systems. Sharing resources allows users to benefit from experiment protocol-templates provided for common experiments, and also protocol-examples developed by other users. This is specifically helpful as our liquid-handling robot can be used for numerous applications by different users, therefore sample experiment-protocols can save a significant amount of time for the user-community. Collaboration on robotic-platforms, i.e. multiple users can work on the same experiment simultaneously, provides novel opportunities for researchers. On the user-interface, they see changes other users are making to the experiment protocol real-time. They can modify the same experiment as a team, or receive notifications regarding experiment progress. Moreover, users can continue to work on the same experiment on another machine. Finally, parallelizing experiments improves efficiency, specifically for artificial-chemical-life experiments, as several long-lasting experiments are performed on multiple-platforms.  

    A cloud-based implementation of the user-interface of our robotic-platform is a paradigm shift from single-user single-platform concept to single-user multi-platform, multi-user single-platform, and multi-user multi-platform approaches. A single-user multi-platform paradigm, i.e. a user being able to control several robotic-systems at the same time, and run the same code on multiple-robots, allows for a high degree of parallelism. A multi-user single-platform, i.e. several users can work on the same robot simultaneously, provides a great potential for collaboration on the robotic-platform. A multi-user multi-platform approach, i.e. several users, e.g. a team, being able to work on multiple-robots, enhances resource sharing, and reusability of experiment-protocols.

    Kasper Stoy

    IT University of Copenhagen

    More information coming!

  • Hit Triage and Mechanism Validation for Phenotypic Screening: Considerations and Strategies

    Contains 1 Component(s) Recorded On: 02/06/2018

    Phenotypic drug discovery approaches can positively affect the translation of preclinical findings to patients. However, significant differences exist between target-based and phenotypic screening, prompting a need to re-assess our strategies and processes to most effectively prosecute phenotypic projects.

    Phenotypic drug discovery approaches can positively affect the translation of preclinical findings to patients. However, significant differences exist between target-based and phenotypic screening, prompting a need to re-assess our strategies and processes to most effectively prosecute phenotypic projects. First, phenotypic screens have dual goals of delivering both efficacious compound series as well as novel molecular targets for diseases of interest whereas only desirable chemical matter is sought for target screens. Second, while confirming binding and functional impact is sufficient for target screening hits, the situation is noticeably more complex for phenotypic screening hits. Here, hits acting through a number of (largely unknown) mechanisms in a large and often poorly understood biological space need to be triaged to differentiate desirable mechanisms from undesirable ones. Given these fundamental differences, the hit triage and validation process was critically re-evaluated in light of the unique characteristics of phenotypic screening. Key considerations and specific strategies will be shared and exemplified by in house and literature case studies. 

    Fabien Vincent

    Pfizer

    Fabien Vincent, Ph.D., is Associate Research Fellow in the department of Hit Discovery and Lead Profiling at Pfizer.  He received a Diplome d’Ingenieur in organic chemistry from CPE Lyon (France) before conducting graduate research in the fields of chemical biology and enzymology in the laboratory of Pr. Harold Kohn at the University of Houston. He later became a post-doctoral fellow in chemical biology at the Genomics Institute of the Novartis Research Foundation in San Diego. Fabien Vincent then entered the field of drug discovery as both a research project leader and molecular pharmacology group leader. His main research interests are centered on improving the translation of pre-clinical research to patients and specifically include physiologically relevant assays, phenotypic screening and atypical molecular mechanisms of action. 

  • High-throughput imaging and selection of viable clones in line with single cell sorting improves viability of clones for downstream analysis

    Contains 1 Component(s) Recorded On: 02/06/2018

    An FDA requirement for biologics production is to provide evidence that the host cell line being employed is derived from a single, parental cell (i.e. monoclonal). Here we present the optimization of a microfluidics-based method—the single cell printing method—for imaging and screening of clones prior to single cell sorting, which significantly improved cell viability compared to other cell sorting techniques.

    An FDA requirement for biologics production is to provide evidence that the host cell line being employed is derived from a single, parental cell (i.e. monoclonal). Conventional techniques for isolating single cells such as limiting dilution and flow cytometry-based methods are significantly limited by process inefficiencies, including low plating densities and low viabilities. These inefficiencies, in turn, reduce the capability to screen for high-producing clones, thereby increasing timelines and costs at early stages of antibody discovery and cell line development processes.  Microfluidics-based methods hold promise for improving upon such inefficiencies due to their ability to sort single cells in a low stress environment. Here we present the optimization of a microfluidics-based method—the single cell printing method—for imaging and screening of clones prior to single cell sorting, which significantly improved cell viability compared to other cell sorting techniques.

    Steven Wiltgen

    Molecular Devices

    My PhD and post-doc focused on the development of a novel super-resolution imaging technique based on TIRF imaging of calcium permeable ion channels. I then served as an Adjunct Professor at several community colleges in the Southern California region before beginning my career at Molecular Devices 4 years ago.  I first served as a Field Application Scientist for our high-content imaging products, then switched gears to marketing where I served as an application scientist for our bioproduction development line of products.  I now serve as the product manager for these same instruments and have been in this role for 1 year.

  • Development of an Automated High Throughput CHO Stable Pool Platform for Generating Large Protein Collections

    Contains 1 Component(s) Recorded On: 02/05/2018

    Innovative solutions ranging from a new software Dashboard to manage projects and execute processes, a recently developed non-invasive Flask Density Reader and an upgraded harvest and purification system compatible with magnetic beads will be presented.

    Recombinant protein expression and purification is a central process in biomedical research and Chinese hamster ovary (CHO) cells are a primary workhorse for protein production from mammalian cells.  GNF has developed a robust suite of software and automated systems to support high throughput CHO (HT-CHO) stable pool establishment, archive of cell banks and protein purification.  Pools are established in 96-well plates, maintained until they are ready for scale up, and then expanded into an AutoFlask™.  Once cells reach the desired density, cell bank archives are created and one or more batch production AutoFlasks™ are inoculated depending on the amount of protein requested.  As an example, a single 50mL culture expressing a human IgG1 antibody typically yields 10 milligrams of protein.  Innovative solutions ranging from a new software Dashboard to manage projects and execute processes, a recently developed non-invasive Flask Density Reader and an upgraded harvest and purification system compatible with magnetic beads will be presented.  This platform enables cost-effective, facile production of proteins at quantities and quality useful for early stage drug discovery tasks such as screening, protein engineering and even in vivo studies. 

    Paul Anderson

    Gnf Systems

    BS Chemical Engineering, UCSB, 2003; MS Biomedical Engineering, Case Western Reserve University, 2005; Senior Automation Engineer, Genomics Institute of the Novartis Research Foundation, 2005-Present

  • Modeling the contribution of common variants to schizophrenia risk.

    Contains 1 Component(s) Recorded On: 02/05/2018

    Schizophrenia (SZ) is a debilitating psychiatric disorder for which the complex genetic mechanisms underlying the disease state remain unclear. Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain.

    Schizophrenia (SZ) is a debilitating psychiatric disorder for which the complex genetic mechanisms underlying the disease state remain unclear. Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. We identified microRNA-9 as having significantly downregulated levels and activity in a subset of SZ hiPSC-derived neural progenitor cells NPCs, a finding that was corroborated by a larger replication cohort and further validated by an independent gene-set enrichment analysis of the largest SZ genome-wide association study (GWAS) to date. Overall, this demonstrated a remarkable convergence of independent hiPSC- and genetics-based discovery approaches.  In developing this larger case/control SZ hiPSC cohort of hiPSC-derived NPCs and neurons, we identified a variety of sources of variation, but by reducing the stochastic effects of the differentiation process, we observed a significant concordance with two large post mortem datasets. We predict a growing convergence between hiPSC and post mortem studies as both approaches expand to larger cohort sizes. Meanwhile, we have been integrating CRISPR-mediated gene editing, activation and repression technologies with our hiPSC-based neural platform, in order to develop a scalable system for testing the effect of a manipulating the growing number of SZ-associated variants and genes in NPCs, neurons and astrocytes. Altogether, our objective is to understand the cell-type specific contributions of SZ risk variants to disease predisposition.

    Kristen Brennand

    ISMMS

    Kristen Brennand, PhD is an Associate Professor of Genetics and Genomics, Neuroscience and Psychiatry at the Icahn School of Medicine at Mount Sinai, in New York, New York. She trained in developmental and stem cell biology at Harvard University and in neurobiology during postdoctoral at the Salk Institute for Biological Studies. By combining expertise in stem cell biology and neurobiology, she has pioneered a new approach by which to study psychiatric disease. Dr. Brennand’s work is funded by the National Institutes of Health, the New York Stem Cell Foundation, the Brain Research Foundation and the Brain and Behavior Research Foundation.