Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along the one- drug-one-gene paradigm. Consequently, the cost of bringing a drug to market is staggering, and the failure rate is daunting. Our long-term goal is to revive the lagging pharmaceutical pipeline by identifying robust methods for achieving precision medicine. We will achieve this goal by developing a novel structural systems pharmacology approach to drug discovery, which integrates structure-based drug design with heterogeneous omics data integration and analysis in the context of the whole human and pathogen genome and interactome. An increasing body of evidence from both our group and others suggests that most drugs commonly interact with multiple receptors (targets). Both strong and weak multiple drug-target interactions can collectively mediate drug efficacy, toxicity, and resistance through the conformational dynamics of biomolecules. In order to rationally design potent, safe, and precision medicine, we face one of the major unsolved challenges in structure-based drug design: what are all the possible proteins and their conformational states interacting with a drug in an organism? This proposal attempts to address this challenge by developing, disseminating, and experimentally testing novel computational tools. Based on our successful preliminary results, we will develop an integrated computational pipeline to identify three-dimensional (3D) protein-chemical interaction models in the cellular context and on a structural proteome scale. Specifically, we will develop a quaternary structure- centric multi-layered network model by integrating heterogeneous data from genomics, proteomics, and phenomics. We will develop a novel collaborative one-class collaborative filtering algorithm to infer missing relations in the multi-layered network. We will combine tools derived from structural bioinformatics, biophysics, and machine learning to gain biological insights into the drug action. To facilitate the usability and reproducibility of the proposed algorithms, we will develop community-based web resources established by our previous experiences in developing the Protein Data Bank (PDB). More importantly, we will work closely with experimental laboratories to test the proposed computational tools using targeted kinase polypharmacology as a real-world example, and iteratively improve the performance and usability of algorithm, software, and web services. The successful completion of this project will provide the scientific community with: (1) new methods to enhance the scope and capability of high-throughput screening for structure-based multi-target drug design; (2) a user-friendly web service to support community-based drug discovery; and (3) potential novel anti-cancer targeted therapeutics. Together, these tools will advance drug discovery and precision medicine by providing a structural systems pharmacology toolkit.

Public Health Relevance

Statement The pharmaceutical industry is in a dire state. Both the cost to launch a new drug and the attrition rate are increasing. Methods that can correlate drug-target interactions with clinical outcomes will reduce both the cost of drug development and mortality rates during clinical trials and treatments.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Ravichandran, Veerasamy
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Hunter College
Biostatistics & Other Math Sci
Schools of Arts and Sciences
New York
United States
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Wang, Annie; Lim, Hansaim; Cheng, Shu-Yuan et al. (2018) ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy. IEEE/ACM Trans Comput Biol Bioinform 15:1960-1967
Lim, Hansaim; Poleksic, Aleksandar; Xie, Lei (2018) Exploring Landscape of Drug-Target-Pathway-Side Effect Associations. AMIA Jt Summits Transl Sci Proc 2017:132-141
Poleksic, Aleksandar; Xie, Lei (2018) Predicting serious rare adverse reactions of novel chemicals. Bioinformatics 34:2835-2842
Poleksic, Aleksandar; Turner, Carson; Dalal, Rishabh et al. (2017) Mining FDA resources to compute population-specific frequencies of adverse drug reactions. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2017:1809-1814