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.
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.
| Zhou, Weizhuang; Altman, Russ B (2018) Data-driven human transcriptomic modules determined by independent component analysis. BMC Bioinformatics 19:327 |
| Lo, Yu-Chen; Rensi, Stefano E; Torng, Wen et al. (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538-1546 |
| Liu, Tianyun; Ish-Shalom, Shirbi; Torng, Wen et al. (2018) Biological and functional relevance of CASP predictions. Proteins 86 Suppl 1:374-386 |
| Han, Lichy; Maciejewski, Mateusz; Brockel, Christoph et al. (2018) A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease. Bioinformatics 34:985-993 |
| Lavertu, Adam; McInnes, Greg; Daneshjou, Roxana et al. (2018) Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum Mol Genet 27:R72-R78 |
| Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E et al. (2018) Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome. Pac Symp Biocomput 23:56-67 |
| Rensi, Stefano; Altman, Russ B (2017) Flexible Analog Search with Kernel PCA Embedded Molecule Vectors. Comput Struct Biotechnol J 15:320-327 |
| Rensi, Stefano E; Altman, Russ B (2017) Shallow Representation Learning via Kernel PCA Improves QSAR Modelability. J Chem Inf Model 57:1859-1867 |
| 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 |
Showing the most recent 10 out of 41 publications
