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DEC 03, 2020 4:30 PM PST

An Iterative Approach to Structure-based in silico to in vivo Development of Combinatorial Anti-SARS-CoV-2 Therapy

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Professor of Microbiology & Immunology, Professor of Otolarynology-Head and Neck Surgery, Drexel University College of Medicine
    Biography
      Dr. Ehrlich is Professor of Microbiology and Immunology, and Otolaryngology-Head and Neck Surgery at Drexel University College of Medicine (DUCoM) in Philadelphia, PA, USA. He also directs: the Center for Genomic Sciences (CGS); the Center for Advanced Microbial Processing (CAMP); and the Center for Surgical Infections and Biofilms within the Institute for Molecular Medicine and Infectious Disease at DUCoM. In addition, he directs the Core Genomics Facility for the Drexel University as a whole. CGS scientists utilize a broad array of comparative genomic techniques and bioinformatic tools, many developed in-house, to identify and characterize both virulence genes within pathogens, and susceptibility genes to pathogens within their hosts. Dr Ehrlich is also one of the founders of the field of Clinical Molecular Diagnostics (MDx), having been involved in the original application of PCR for the detection of human retroviruses in 19851. He founded the MDx Division at UPMC and used these experiences to author the first textbook/lab manual for infectious disease (ID) MDx2. Together with a team of like-minded pioneers he was one of the founders of the Association for Molecular Pathology and served as the first co-chair of the ID section. Dr Ehrlich counts among his major contributions to science the mapping and cloning of several major human disease genes3,4, and the re-writing of much of our understanding of chronic bacterial pathogenesis5,6. The latter began with his promulgation of the biofilm paradigm to explain many facets of chronic mucosal microbial infections7-9. Working with Chris Post, he started his explorations into chronic middle-ear disease in children in the early 90's which he has since repeatedly generalized such that it is now widely accepted that the vast majority of all chronic microbial infections are biofilm-associated10,11. He also advanced the Distributed Genome Hypothesis (DGH12,13) to explain the enormous clinical variability among strains of a bacterial species, which together with the biofilm paradigm form the bases for his rubric of Bacterial Plurality6. His work in human genetics combined with the laboratory resources necessary to test the DGH have resulted in his having played a role in the development of several waves of genomic technology over the last quarter century including microsatellite mapping, microarrays, and next-generation sequencing. More recently he has developed the concept of bacterial population-level virulence factors and has for the first time within the field of bacterial genomics used statistical genetics and machine learning algorithms approaches to identify unannotated distributed genes that are associated with virulence. These computational methodologies provides a non-biased, top-down approach to prioritize the annotation of hypothetical genes14. Coincident with the recent relocation of his research enterprise to DUCOM he founded CAMP which functions as a collaborative multi-discipline facility for exploitation of a suite of technological advances, many developed within the CGS, which permit the identification, cloning, heterologous expression, and biochemical verification of commercially important biosynthetic and biodegradative pathways from what he refers to as the "Genomic Dark Matter". This approach came out his successful collaborative studies with Dr. David Sherman at the University of Michigan wherein they used multiple omics technologies (and developed the term meta-omics) to isolate and characterize all of the genes for a novel biosynthetic pathway for an important anti-cancer drug from an unculturable endosymbiotic bacterium of a tunicate15. Over the past several years Dr Ehrlich has overseen the development of a novel ultra-high-fidelity microbiome assay that provides quantitative, species-specific analyses of microbial consortia using whole-gene 16S amplification and sequencing on the Pacific Biosciences third generation long-read sequencing platform16. When combined with a state-of-the art bioinformatics pipeline that takes advantage of novel pathway- algorithms and a custom database, developed in-house, this system provides unprecedented accuracy. In collaboration with Dr Curtis Harris at the NCI, Dr Ehrlich and his team applied this high-fidelity microbiome assay to identify bacterial species-specific changes to the lung microbiome associated with a specific TP-53 mutation - providing the first microbial biomarker for cancer17. Dr Ehrlich's lifelong interest in emergent MDx and "omic" technologies led to his recent appointment as Director of the Meta-Omics Core Facility at the Sidney Kimmel Cancer Center, a consortium NCI-designated Cancer Center involving Thomas Jefferson University and Drexel University. Dr Ehrlich's latest paradigm-changing hypothesis is that Alzheimer's disease results from a combination of chronic bacterial infections of the brain (primarily originating from the periodontium) and the brain's anti-microbial and inflammatory responses to these infections. Dr. Ehrlich was elected as fellow of the American Association for the Advancement of Science in 2014 and has won numerous awards for his research and teaching.

    Abstract

    This drug development program is designed to create a family of broad-spectrum, pan-coronaviral drugs that respectively inhibit multiple key enzymes required for viral replication. By targeting multiple gene products simultaneously we hope to eliminate/minimize the development of treatment-resistant viral strains as was successfully done when HAART (highly active anti-retroviral therapy) was introduced for HIV-1 disease.  We employ a pipeline-based drug development process that includes multiple feedback loops between and among both in silico and laboratory-based processes for the generation of highly effective and mutation-resistant pharmaceuticals.   As part of our process to minimize the risk of the evolution of escape mutants, we begin our drug design using a suite of artificial intelligence-based machine-learning algorithms that are proven to: 1) predict future evolution (mutations) of the target viral enzymes in silico –  these sequences are added to the extant set of all protein structure files from the -coronaviruses that correspond to each of our target enzymes;  2) perform protein structure overlays of all available structures for each target enzyme to determine the most highly conserved active site morphologies; 3) prepare a 3-D grid of the conserved (consensus) active site for each target enzyme;  4) using a 3-step in silico funnel process, with increased-stringency at each step, run each consensus target active-site against multiple large druggable compound libraries, including all FDA-approved drugs, to obtain ‘hit’ compounds.  These ‘hit’ compounds are then simultaneously moved into: a) our in silico molecular dynamics pipeline to identify and characterize the active-site interacting groups (molecular domains) which will be used as one of the inputs into our structure-activity relationship (SAR) modeling to rationally build derivative compounds with greater affinities; and b) our three-phase laboratory biophysical-biochemical pipeline consisting of (i) surface plasmon resonance for protein binding kinetics, (ii) functional enzyme inhibition studies, and (iii) isothermal titration calorimetry and microscale thermophoresis to obtain precise stoichiometry and binding affinities under multiple liquid-phase parameters.  Hit compounds meeting both preset biophysical and biochemical criteria will be moved as “leads” into the antiviral testing arm of the pipeline.  Those compounds showing promise, but without high enough binding scores or IC50 values will be moved in the SAR pipeline for the development of rationally-designed derivatives.  Results from the SAR studies will be screened for their ease/predicted yields of synthesis, and then process-specific software will be used to augment protocols for their organic syntheses.  Lead compounds will be evaluated individually, and as cocktails of two and three compounds targeting different enzymes, for: 1) human cell toxicity; 2) SARS-CoV-2 virological studies using Calu-3 cells and human airway epithelial cells; and 3) in a physiologically relevant lung-on-a-chip system. Emergence of viral escape mutants in response to lead compounds will be sequenced and inform the synthesis of escape-resistant antiviral drugs. Finally, the prophylactic/therapeutic in vivo antiviral efficacy of lead compounds will be determined in animal models of coronavirus infection and pathogenesis.

    Learning Objectives:

    1. Learn applications of deep-learning algorithms and machine-learning techniques to help with the design of escape-resistant drugs for infectious agents

    2. Learn targeting strategies to minimize the evolution of escape mutants


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