Jennifer Wilson's Research at UCLA: Pioneering Computational Network Models for Drug Discovery

Introduction

Jennifer Wilson is a distinguished faculty member at the UCLA Samueli School of Engineering, specializing in the development of computational network models to enhance drug design. Her research focuses on identifying potential drug targets by analyzing their effects within protein networks, with recent emphasis on immune, oncology, and schizophrenia indications. Wilson's innovative approach combines systems biology, network biology, and clinical informatics to address complex problems in therapeutic development and disease understanding.

Background and Education

Wilson's expertise is rooted in a strong academic foundation. She earned her Ph.D. in biological engineering from the Massachusetts Institute of Technology (MIT). Following her doctoral studies, she held a postdoctoral appointment with SPARK, a program within the Chemical and Systems Biology Department at Stanford University. Prior to that, she was a postdoctoral fellow at the UC San Francisco-Stanford Center for Excellence in Regulatory Science and Innovation. Her diverse postdoctoral experiences have equipped her with a multidisciplinary perspective on drug discovery and regulatory science.

Research Focus: Computational Network Models for Drug Design

Wilson's primary research interest lies in refining and developing computational network models. These models are designed to improve the efficiency of drug design by enhancing the identification of potential drug targets. Her approach leverages the analysis of protein network effects to prioritize novel targets, particularly for immune, oncology, and schizophrenia indications. By understanding how potential drug targets interact within complex biological networks, Wilson aims to provide mechanistic insights into drug efficacy and safety.

Network Biology and Systems Pharmacology

Wilson's work incorporates principles of network biology and systems pharmacology. Network biology involves studying biological systems as interconnected networks of molecules, genes, and proteins. Systems pharmacology applies systems biology approaches to understand drug action, efficacy, and toxicity. By integrating these disciplines, Wilson develops models that capture the complexity of drug-target interactions and predict therapeutic outcomes.

Application to Immune, Oncology, and Schizophrenia Indications

Wilson's research has significant implications for the treatment of immune disorders, cancer, and schizophrenia. These conditions are characterized by complex underlying mechanisms and a need for more effective and targeted therapies. By identifying key nodes within relevant biological networks, Wilson's models can help prioritize drug targets that are most likely to yield therapeutic benefits.

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Key Publications and Contributions

Wilson's contributions to the field are reflected in her extensive list of publications. Her research spans a range of topics, including drug safety, drug efficacy, and the identification of novel therapeutic targets.

PathFX: Mechanistic Insights into Drug Efficacy and Safety

Wilson's work on PathFX, a computational tool for identifying drug safety and efficacy phenotypes, is particularly noteworthy. PathFX provides mechanistic insights into drug action by analyzing the effects of drugs on biological pathways. This tool has implications for regulatory review and therapeutic development, offering a means to assess drug safety and efficacy more comprehensively.

Wilson JL, Racz R, Liu T, Adeniyi O, Sun J, Ramamoorthy A, Pacanowski M, Altman RB. “PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development”. PLoS Comput Biol.
Wilson JL, Wong M, Chalke A, Stepanov N, Petkovic D, Altman RB. “PathFXweb: a web application for identifying drug safety and efficacy phenotypes.” Bioinformatics. 2019 May 22.

PhenClust: Identifying Trends in Biological Phenotypes

Wilson's work also includes the development of PhenClust, a standalone tool for identifying trends within sets of biological phenotypes. PhenClust uses semantic similarity and the Unified Medical Language System (UMLS) Metathesaurus to analyze phenotypic data. This tool can be used to uncover relationships between phenotypes and identify potential drug targets.

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Wilson, JL, Gravina A, Grimes K. Wilson, JL, Stepanov N, Wong M, Petkovic D, Altman RB. “PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System Metathesaurus”.

Quantitative Systems Pharmacology Approach for Drug-Induced Cytopenias

Wilson's research also addresses the mechanisms of drug-induced cytopenias, a condition characterized by a reduction in blood cell counts. She developed an in vitro quantitative systems pharmacology approach to deconvolve the mechanisms of drug-induced, multilineage cytopenias. This approach can help identify the specific pathways and targets involved in drug-induced toxicity, leading to safer drug development.

Wilson, JL, Lu D, Corr N, Fullerton A, Lu J. “An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias”. PLoS Computational Biology.

Network Paradigm for Predicting Drug Synergistic Effects

Wilson has also explored the prediction of drug synergistic effects using network analysis. Her research demonstrates that a network paradigm, based on downstream protein-protein interactions, can effectively predict drug synergies. This approach has implications for the development of combination therapies that are more effective than single-drug treatments.

Wilson JL, Steinberg E, Racz R, Altman RB, Shah N, Grimes K. A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.. CPT: pharmacometrics & systems pharmacology, 2022.

Scientific Considerations for Global Drug Development

Wilson has contributed to the understanding of scientific considerations for global drug development. Her work in this area addresses the challenges and opportunities associated with developing drugs for a global market. This includes considerations related to regulatory requirements, patient populations, and clinical trial design.

Wilson JL, Cheung KWK, Lin L, Green EAE, Porrás AI, Zou L, Mukanga D, Akpa PA, Darko DM, Yuan R, Ding S, Johnson WCN, Lee HA, Cooke E, Peck CC, Kern SE, Hartman D, Hayashi Y, Marks PW, Altman RB, Lumpkin MM, Giacomini KM, Blaschke TF. “Scientific Considerations for Global Drug Development”. Sci Trans Med.

Identifying Novel Signaling Regulators of TGFα Ectodomain Shedding

Wilson's research has also focused on identifying novel signaling regulators of TGFα ectodomain shedding, a process involved in cancer development. By using a functional genomics approach, she has identified new targets for cancer therapy.

Wilson JL, Kefaloyianni E, Stopfer L, Harrison C, Sabbisetti VS, Fraenkel E, et al. “Functional Genomics Approach Identifies Novel Signaling Regulators of TGFα Ectodomain Shedding”. Mol Cancer Res.

Pathway-Based Network Modeling for Acute Lymphoblastic Leukemia

Wilson has also developed pathway-based network models to identify hidden genes in shRNA screens for regulators of acute lymphoblastic leukemia (ALL). This work demonstrates the power of network modeling to uncover novel therapeutic targets in cancer.

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Wilson JL, Dalin S, Gosline S, Hemann M, Fraenkel E, Lauffenburger DA. “Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia”. Integr Biol (Camb).

Biomarkers: Delivering on the Expectation of Molecularly Driven, Quantitative Health

Wilson has contributed to the discussion on the role of biomarkers in delivering molecularly driven, quantitative health. Her work in this area emphasizes the importance of biomarkers in personalized medicine and drug development.

Wilson JL, Altman RB. “Biomarkers: Delivering on the expectation of molecularly driven, quantitative health”. Exp Biol Med.

Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real-World Data

Wilson has used real-world data to identify new therapeutic targets for amyotrophic lateral sclerosis (ALS) and other neurological diseases. This work highlights the potential of using real-world data to accelerate drug discovery for these conditions.

Alidoost M, Huang JY, Dermentzaki G, Blazier AS, Gaglia G, Hammond TR, Frau F, McCorry MC, Ofengeim D, Wilson JL. Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real-World Data.. Clinical pharmacology and therapeutics.

Preclinical Side Effect Prediction Through Pathway Engineering of Protein Interaction Network Models

Wilson has developed methods for predicting preclinical side effects through pathway engineering of protein interaction network models. This work can help identify potential safety issues early in the drug development process.

Alidoost M, Wilson JL. Preclinical side effect prediction through pathway engineering of protein interaction network models.. CPT: pharmacometrics & systems pharmacology, 2024.

Awards and Recognition

Wilson's contributions to the field have been recognized with numerous awards and honors, including:

Sanofi iDEA Award
National Science Foundation Graduate Research Fellowship
Koch Cancer Graduate Fellowship

Impact and Future Directions

Jennifer Wilson's research at UCLA is at the forefront of computational drug discovery. Her work on computational network models, systems pharmacology, and network biology has the potential to transform the way drugs are designed and developed. By providing mechanistic insights into drug action and identifying novel therapeutic targets, Wilson's research can lead to more effective and safer therapies for a range of diseases, including immune disorders, cancer, and schizophrenia. Her ongoing work promises to further refine these models and expand their application to other disease areas, ultimately contributing to the advancement of personalized medicine and improved patient outcomes.

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