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Phenotypic Drug Discovery: Leveraging Computational Tools

WEBINAR

Only

Phenotypic Drug Discovery: Leveraging Computational Tools

Thursday, June 4 - Friday, June 5, 2020 EDT

The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York

Presented By

The Biochemical Pharmacology Discussion Group

The New York Academy of Sciences

 

Phenotypic screening has recently led to discoveries of first-in-class drugs with novel mechanisms of action. Due to this success, interest in this strategy has experienced a renaissance. In contrast to target-based strategies, phenotypic drug discovery does not rely on knowledge of a specific drug target or a hypothesis about its role in disease, but instead focuses on finding a gene or small molecule that corrects a cellular characteristic that is specific to disease. Along with this new strategy, major advances in high throughput technologies have resulted in immense phenotypic datasets. However, these recent advances in automated screening technology have resulted in a dilemma: experimental data can be generated at a much faster rate than researchers can possibly analyze and integrate them. This inefficiency makes it difficult to conduct certain types of experiments on a large scale, wastes investments, and delays the drug discovery process. This symposium will provide a snapshot of the current state of computational methods used in phenotypic screening and novel in silico approaches, and include discussions of deep learning, AI, functional genomics, chemical screening, systems biology, target deconvolution, biomarkers, and toxicity.

Registration

Member
$60
Nonmember Academia, Faculty, etc.
$130
Nonmember Corporate, Other
$170
Nonmember Not for Profit
$130
Nonmember Student, Undergrad, Grad, Fellow
$90
Member Student, Post-Doc, Fellow
$30
Member
$30
Nonmember Academia, Faculty, etc.
$65
Nonmember Corporate, Other
$85
Nonmember Not for Profit
$65
Nonmember Student, Undergrad, Grad, Fellow
$45
Member Student, Post-Doc, Fellow
$15
Member
$30
Nonmember Academia, Faculty, etc.
$65
Nonmember Corporate, Other
$85
Nonmember Not for Profit
$65
Nonmember Student, Undergrad, Grad, Fellow
$45
Member Student, Post-Doc, Fellow
$15
Deadline:
0
days
left

Keynote Speakers

Lawrence Lum, PhD
Lawrence Lum, PhD

Pfizer

Olga Troyanskaya, PhD
Olga Troyanskaya, PhD

Princeton University

Speakers

Andrea Califano, PhD
Andrea Califano, PhD

Columbia University

Paul A. Clemons, PhD
Paul A. Clemons, PhD

Broad Institute of MIT and Harvard

Olivier Elemento
Olivier Elemento, PhD

Weill Cornell Medical College

Jennifer Fuller
Jennifer Fuller, PhD

GeneCentrix Inc.

Traver Hart
Traver Hart, PhD

MD Anderson Cancer Center

Imran Haque, PhD
Imran Haque, PhD

Recursion Pharmaceuticals

Wengong Jin, SM
Wengong Jin, SM

Massachusetts Institute of Technology

Michael Keiser, PhD
Michael Keiser, PhD

University of California, San Francisco

Elizabeth McMillan, PhD
Elizabeth McMillan, PhD

Pfizer

Christian Meyer, PhD
Christian Meyer, PhD

Parthenon Therapeutics

Nicholas Tatonetti, PhD
Nicholas Tatonetti, PhD

Columbia University

Bridget Wagner
Bridget Wagner, PhD

Broad Institute of MIT and Harvard

Scientific Organizing Committee

Kira A. Armacost, PhD

Merck

Timothy J. Cardozo, MD, PhD

New York University

Ye Che, PhD

Pfizer

Paul A. Clemons, PhD

Broad Institute of MIT and Harvard

Guanglei Cui, PhD

GlaxoSmithKline

Olivier Elemento, PhD

Weill Cornell Medical College

Adam Gilbert, PhD

Pfizer

Susan Pieniazek, PhD

Bristol-Myers Squibb

Marie-Claire Peakman, PhD

Pfizer

Marco Prunotto, PhD

Roche

Alison Carley, PhD

The New York Academy of Sciences

Sonya Dougal, PhD

The New York Academy of Sciences





Thursday

June 04, 2020

11:00 AM

Welcome Remarks

Speaker

Alison Carley, PhD
The New York Academy of Sciences
11:10 AM

Introduction: Phenotypic Drug Discovery-Leveraging Computational Tools

Speaker

Marie-Claire Peakman, MRPharmS, PhD
Pfizer
11:20 AM

Keynote Address: Decoding Molecular Basis of Human Disease with Machine Learning

Speaker

Olga G. Troyanskaya, PhD
Princeton University

Session 1: Computational methods for functional genomics screening

12:05 PM

A Universal Reporter Assay to Assess Drug Activity and Mechanism of Action

Speaker

Andrea Califano, PhD
Columbia University
12:35 PM

CRISPR-mediated Target Finding – and Its Weaknesses

Speaker

Traver Hart, PhD
MD Anderson Cancer Center

Identifying essential genes in cancer cell lines is a straightforward way to classify targets and their associated biomarkers for repurposing existing therapies or developing novel ones. CRISPR/Cas9 screens in hundreds of cancer cell lines have offered hundreds of candidate targets. However, there is often a disconnect between a cell’s gene essentiality profile and its sensitivity to targeted small molecules. This brief talk will discuss one reason for this disconnect: functional buffering by paralogous genes. Small molecules often inhibit two or more members of a protein family, and only in their joint inhibition is the desired phenotype achieved. In contrast, now-standard CRISPR screens targeting one gene at a time likely have LOF phenotypes masked by paralog functional buffering.

1:05 PM

Lunch Break

Session 2: Computational methods for chemical screening

1:35 PM

Chemistry-first Approaches for Personalized Medicine

Speaker

Elizabeth McMillan, PhD
Pfizer
2:05 PM

Applications of Phenotypic Profiling to Small-molecule Collections

Speaker

Paul Clemons, PhD
Broad Institute
2:35 PM

Break

Session 3: Data intergration and exploration for phenotypic screening

3:05 PM

Phenotypic Discovery and Mechanism-of-action Studies in Diabetes

Speaker

Bridget Wagner, PhD
Broad Institute

A loss of beta-cell mass and biologically active insulin is a central feature of both type 1 and type 2 diabetes. A chemical means of promoting beta-cell viability or function could have an enormous impact clinically by providing a disease-modifying therapy. Phenotypic approaches have been enormously useful in identifying new intracellular targets for intervention; in essence, we are allowing the cells to reveal the most efficacious targets for desired phenotypes. A key step in this process is determining the mechanism of action to prioritize small molecules for follow up. Here, I will discuss how my group is identifying small molecules that promote beta-cell regeneration, viability, and function. We have developed a number of platforms to enable the phenotypic discovery of small molecules with human beta-cell activity, providing excellent starting points for validating therapeutic hypotheses in diabetes. We developed an islet cell culture system suitable for high-throughput screening that has enabled us to screen for compounds to induce human beta-cell proliferation, as well as to perform follow-up studies on small molecules that emerge from other efforts. This work identified DYRK1A inhibition as a relevant mechanism to promote beta-cell proliferation, and 5-iodotubercidin (5-IT) was shown to induce selective beta-cell proliferation in NSG mice.

3:35 PM

Reimagining Precision Oncology by Leveraging Machine Learning in Drug Combination

Speaker

Christian Meyer, PhD
Parthenon Therapeutics

The clinical relevance of in vitro assays is widely disputed, largely based on anecdotal evidence that phenotypic and target-based discovery efforts are commonly misled by the idiosyncratic nature of in vitro models or by the failure to account for target interactions. However, less attention is paid to the possible inadequacy of the quantitative models commonly used to analyze these preclinical data. Using two case studies, we transform the clinical utility of in vitrodata by analyzing them with systems-based models. Specifically, we designed algorithms for assessing: i) the diversification of cell subtypes in a tumor ecosystem (BooleaBayes); ii) the drug-induced rate of cell proliferation (DIP Rate); and, iii) the synergy of both potency and efficacy for drug combinations (MuSyC). These algorithms reduce error propagation at critical steps in a drug discovery pipeline,i.e., from model selection through assay design, execution, and data analysis opening the door for clinically impactful machine learning applications based on in vitro data. BooleaBayes, DIP Rate, and MuSyC are the centerpieces of our precision oncology platform designed to effectively leverage systems biology thinking and machine learning in drug discovery. Applied to Small Cell Lung Cancer and mutant-BRAF cancers, the Parthenon platform predicts clinically relevant therapeutics from preclinical studies, thereby reducing cycle times and increasing the probability of success.

4:05 PM

Historeceptomics Improves the Yield of Phenotypic Screens

Speaker

Jennifer Fuller, PhD
GeneCentrix Inc

Phenotypic drug discovery is emerging as an important present and future approach in pharmaceutical development. The bottleneck in phenotypic drug discovery is often target deconvolution after a promising hit compound, which modulates the phenotype, is found. Historeceptomics is a new, under-exploited technological approach to capturing simplified drug mechanism of action (MOA) information from big genomics and systems pharmacology data on specific drugs or drug candidates. The approach emphasizes investigation of the entire polypharmacologic ensemble of any drug candidate, specifically by taking into account the expression level of the drug’s targets in specific tissues and cells of interest. We applied historeceptomics to the problem of target deconvolution in phenotypic drug discovery. We found that re-ranking phenotypic screen hits according to the historeceptomics score representing the affinity of each hit for targets multiplied by the expression of those targets in the cells used in the screen resulted in unrecognized, significant drug targets being unveiled. In addition, the use of historeceptomics enables statistical significance of the top ranked hits to be calculated. Application of the approach to a published repurposing screen for drugs preventing Zika virus neuro-injury revealed CDK5 as a new, proprietary drug target for preventing this disease.

4:35 PM

Closing Remarks

4:45 PM

Adjourn

Friday

June 05, 2020

11:00 AM

Keynote Address: Epigenetic Consequences of INDEL-based Mutagenesis

Speaker

Lawrence Lum, PhD
Pfizer

The introduction of insertion-deletions (INDELs) by non-homologous end-joining (NHEJ) pathway underlies the mechanistic basis of CRISPR-Cas9-directed genome editing. Selective gene ablation using CRISPR-Cas9 is achieved by installation of a premature termination codon (PTC) from a frameshift-inducing INDEL that elicits nonsense-mediated decay (NMD) of the mutant mRNA. Here, by examining the mRNA and protein products of CRISPR targeted genes in a cell line panel with presumed gene knockouts, we detect the production of foreign mRNAs or proteins in ~50% of the cell lines. We demonstrate that these aberrant protein products stem from the introduction of INDELs that promote internal ribosomal entry, convert pseudo-mRNAs (alternatively spliced mRNAs with a PTC) into protein encoding molecules, or induce exon skipping by disruption of exon splicing enhancers (ESEs). Our results reveal challenges to manipulating gene expression outcomes using INDEL-based mutagenesis and computational strategies useful in mitigating their impact on intended genome-editing outcomes.

Session 4: Computational methods for systems biology and target deconvolution

11:45 AM

Metric Learning and Mechanism of Action Discovery for a Zebrafish Behavioral Screen

Speaker

Michael Keiser, PhD
University of California, San Francisco

Large-scale screening in larval zebrafish has revealed neuroactive compounds and protein targets for multiple behavioral phenotypes. Conventional efforts encode zebrafish behavioral videos by bulk motion over time and compare these “motion index” drug profiles by correlation distance metrics. Using a nearest-neighbor logic, a researcher may then use landmark drugs to infer the targets of phenotypically similar compounds. Here we explore two enhancements to this discovery pipeline. In the first, we develop a learned distance metric to relate drug-like molecules to each other by their behavioral profiles using Siamese Neural Networks. The learned distance metric substantially outperforms correlation distance and generalizes well to datasets collected months apart. In the second enhancement, we combine cheminformatic target prediction with statistical enrichment calculations to generate mechanism of action hypotheses directly from phenotypic screening datasets. This procedure reveals sensible but also unexpected protein targets, prospectively confirmed in vitro and in vivo. Together, these techniques demonstrate a means to extract otherwise hidden pharmacological signal from existing and archival phenotypic screens.

12:15 PM

Lunch Break

Session 5: Computational methods for clinical biomarkers and toxicity identification

1:15 PM

Predicting Mechanisms of Action, Human Toxicity and Optimizing Indication using Biology-driven AI

Speaker

Olivier Elemento, PhD
Weill Cornell Medical College
1:45 PM

Observational Data for Biomedical Discovery

Speaker

Nicholas Tatonetti, PhD
Columbia University
2:15 PM

Break

Session 6: Advances in predictive sciences in phenotypic screening

2:25 PM

Deep Learning for Drug Discovery

Speaker

Wengong Jin, SM
Massachusetts Institute of Technology

The current pandemic highlights an acute need to develop fast therapeutics against health threats. Traditional approaches to drug development are expensive and slow to react to pandemics. AI tools on the other hand have the potential to accelerate and transform this effort, enabling rapid, large scale search and identification of effective drugs. In this talk, I will showcase two of my recent efforts in addressing this challenge:

First, I will present application of deep learning to antibiotic discovery (Stokes et al., Cell 2020). We trained a deep graph convolutional network to predict antibacterial activity of compounds. We performed predictions on multiple chemical libraries and discovered eight new antibacterial compounds that are structurally distant from known antibiotics and display bactericidal activity against wide spectrum of pathogens.

Second, I will discuss our recent effort on developing effective property prediction methods to accelerate the search for COVID-19 antivirals. In particular, I will introduce a novel approach to learn property predictors that can generalize or extrapolate beyond the heterogeneous data (e.g., different COVID-19 screens). To test the method, we use a combination of three data sources: SARS-CoV-2 antiviral screening data, molecular fragments that bind to SARS-CoV-2 main protease and large screening data for SARS-CoV-1. Our predictor outperforms state-of-the-art transfer learning methods by significant margin.

2:55 PM

Applying AI to Accelerate Assay Development to Pandemic Speed

Speaker

Imran Haque, PhD
Recursion Pharmaceuticals

The COVID-19 pandemic has created unprecedented stresses for therapeutic development. With hundreds of thousands perishing in only a few months, there is great interest in discovering pharmacological treatments active against the SARS-CoV-2 virus; however, extant preclinical systems to study the virus and disease have a high risk of failure in human translation, with most in vitro work being done in an immortalized African green monkey cell line (Vero E6) and few-to-no in vivo models in the early phase of the pandemic.

In this talk I will describe the work done at Recursion to discover therapeutics for COVID-19. Recursion applied deep learning-based image AI to rapidly develop a human-cell-based assay for SARS-CoV-2 infection, applying phenotypic screening to deliver value even with the paucity of knowledge regarding virus-host interaction biology and targets. We subsequently deployed this assay to screen thousands of approved and clinical compounds for therapeutic benefit with minimal staff in only a few weeks. The utility of the platform will be demonstrated with recent results from our screening work.

3:25 PM

Closing Remarks

Speaker

Alison Carley, PhD
The New York Academy of Sciences
3:35 PM

Adjourn

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