The flow of safe and effective new drugs to the market has slowed in the last several years, and threatens our ability to continue to improve the nation's health. At the same time, our ability to measure biological systems and understand their function is exploding. Structural genomics has given us unprecedented access to the detailed three-dimensional structure of proteins, in many cases bound to small molecule drugs that modulate their function. Functional genomics measurements of cellular gene expression have given us a picture of how drugs impact the entire cell through their multiple physical interactions The opportunity now is to combine the detailed mechanistic understanding of drug binding and drug-target interaction with a systems view of drug effect, to create novel, high-confidence hypotheses about new therapeutic opportunities. In this proposal, we combine our informatics expertise in 3D structure analysis and gene expression analysis, with our medical and pharmacogenetic expertise to propose a research program to combine structural and functional data for drug repurposing-the use of previously approved and safe drugs for new indications, alone or in combination with other drugs. Our preliminary work has shown that we can detect distant binding site similarities to suggest new targets for existing drugs. It has also shown that we can associate novel sets of genes to diseases and drug-responses based on their patterns of expression. We propose to develop these together in the context of cancer and the treatment of rare "orphan" diseases. We present a focused plan to (1) develop novel methods for predicting the "druggability" of a protein, (2) create algorithms for detecting binding similarity between two pockets, (3) create filters that use expression data to find the most attractive pathways to target through single or multiple repurposed drugs, and (4) to apply our methods to propose new cancer therapies and new rare disease therapies. With success, our methods will demonstrate how the revolution in molecular biology and genomics can be harnessed to assist drug discovery-initially, in the context of repurposing, but with an eventual goal of designing entirely new small molecule therapies.

Public Health Relevance

Despite huge advances in our ability to measure biological systems, this has not translated into the anticipated increase in new and effective drugs for poorly treated diseases. We outline a plan to combine two types of data (structural and functional genomics) to generate hypotheses about new ways to use existing drugs to treat cancer and rare diseases. Our computational methods will provide proof that we can use the fruits of genomics effectively to create new treatments for disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM102365-03
Application #
8665449
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2012-09-01
Project End
2016-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
3
Fiscal Year
2014
Total Cost
$408,171
Indirect Cost
$147,117
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B (2016) OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records. J Am Med Inform Assoc 23:339-48
Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M et al. (2016) DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS. Pac Symp Biocomput 21:195-206
Bagley, Steven C; Sirota, Marina; Chen, Richard et al. (2016) Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants. PLoS Comput Biol 12:e1004885
Li, Yong Fuga; Xin, Fuxiao; Altman, Russ B (2016) SEPARATING THE CAUSES AND CONSEQUENCES IN DISEASE TRANSCRIPTOME. Pac Symp Biocomput 21:381-92
Liu, Tianyun; Altman, Russ B (2015) Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach. J Chem Inf Model 55:1483-94
Percha, Bethany; Altman, Russ B (2015) Learning the Structure of Biomedical Relationships from Unstructured Text. PLoS Comput Biol 11:e1004216
Chen, Jonathan H; Altman, Russ B (2015) Data-Mining Electronic Medical Records for Clinical Order Recommendations: Wisdom of the Crowd or Tyranny of the Mob? AMIA Jt Summits Transl Sci Proc 2015:435-9
Altman, Russ B (2015) Predicting cancer drug response: advancing the DREAM. Cancer Discov 5:237-8
Zhou, Weizhuang; Tang, Grace W; Altman, Russ B (2015) High Resolution Prediction of Calcium-Binding Sites in 3D Protein Structures Using FEATURE. J Chem Inf Model 55:1663-72
Altman, Russ B; Ashley, Euan A (2015) Using "big data" to dissect clinical heterogeneity. Circulation 131:232-3

Showing the most recent 10 out of 26 publications