Our accelerating ability to measure biological systems at the molecular, cellular, organ and organism level promises a new generation of powerful and improved drug therapies. However, drug failures continue to occur at high rates due to lack of efficacy or unexpected toxicity?even when the drug binds its intended target with very high affinity. We do not sufficiently understand the biological systems in which we are intervening, suggesting that we are making fundamental assumptions that are wrong. Building on results from our previous grant period, this renewal proposal proposes new assumptions: (a) when drugs work it is because they interact not only with their target but with many other off-targets that produce synergistic effects, (b) the actions of drugs can be best understand as the interaction between protein networks that are dysfunctional in disease and drug response networks that are modulated by the complete set of relevant targets, and (c) that evidence of direct physical interaction is superior to complicated and integrative signals (such as gene expression) in creating and analyzing drug response networks that can usefully be linked to disease networks. Thus, we propose a plan to (1) develop and apply methods to predict drug interactions on a proteome scale, and uses these to improve methods for creating interaction networks relevant to drug response and disease biology, (2) devise methods to associate drug response with disease biology, using the features of the associated protein networks, and (3) collaborative apply these tools with collaborations from academia (U. Pennsylvania for NSAID response & the Structural Genomics Consortium for target selection and triage), industry (Genentech for cancer, Pfizer for autoimmune disease), and government (the U.S. FDA for seeking biomarkers to predict efficacy and toxicity. With success, we will have created a framework for drug discovery, repurposing, combination use and toxicity prediction that may contribute to a higher rate of success in delivering new therapies to benefit public health.

Public Health Relevance

Despite great advances in our understanding of biology, experimental drugs still routinely fail because they do not work or cause unexpected side effects. We simply do not understand biology well enough, and likely are making incorrect assumptions. We outline a plan to revisit assumptions about how drugs work and cause side effects. We will collaborate with industry, academia and government to assess the utility of the tools we develop.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM102365-07
Application #
9897603
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2012-09-01
Project End
2022-03-31
Budget Start
2020-04-01
Budget End
2021-03-31
Support Year
7
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Stanford University
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne et al. (2017) Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans. Genome Med 9:98
Torng, Wen; Altman, Russ B (2017) 3D deep convolutional neural networks for amino acid environment similarity analysis. BMC Bioinformatics 18:302

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