This project aims to develop and implement multiple but integrated computational tools for drug repurposing by exploiting complimentary big data streams, i.e., unstructured texts (social media networks, published biomedical literature, and electronic health records), electronic databases of chemical-biological interactions and pathways, and laboratory data (biological screening of chemical libraries). We expect that tools to be developed in this project will be useful for repurposing observational textual data for research projects (addressing the second challenge of the underlying RFA). In addition, the envisioned translation of this data into a format amenable to quantitative modeling of drug effects will also enable integration of textual and laboratory data to create minable metadata (cf. the third challenge).

Public Health Relevance

This project aims to develop and implement computational tools for drug repurposing by exploiting two complimentary big data streams, i.e., unstructured texts (social media networks, published biomedical literature, and electronic health records) and laboratory data (biological screening of chemical libraries). The proposed tools and underlying computational framework will employ observational human data together with experimental bioactivity data to discover and validate novel therapeutic applications of existing drugs, i.e., following the knowledge discovery path from man to molecules to man.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01CA207160-01
Application #
9161354
Study Section
Special Emphasis Panel (ZRG1-BST-U (50)R)
Program Officer
Miller, David J
Project Start
2016-09-01
Project End
2019-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
1
Fiscal Year
2016
Total Cost
$437,810
Indirect Cost
$137,810
Name
University of North Carolina Chapel Hill
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Capuzzi, Stephen J; Thornton, Thomas E; Liu, Kammy et al. (2018) Chemotext: A Publicly Available Web Server for Mining Drug-Target-Disease Relationships in PubMed. J Chem Inf Model 58:212-218
Baker, Nancy C; Ekins, Sean; Williams, Antony J et al. (2018) A bibliometric review of drug repurposing. Drug Discov Today 23:661-672
Alves, Vinicius M; Muratov, Eugene N; Zakharov, Alexey et al. (2018) Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? Food Chem Toxicol 112:526-534
O'Banion, Colin P; Priestman, Melanie A; Hughes, Robert M et al. (2018) Design and Profiling of a Subcellular Targeted Optogenetic cAMP-Dependent Protein Kinase. Cell Chem Biol 25:100-109.e8
Lima, Marilia N N; Melo-Filho, Cleber C; Cassiano, Gustavo C et al. (2018) QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities. Front Pharmacol 9:146
Shen, Min; Asawa, Rosita; Zhang, Ya-Qin et al. (2018) Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing. Oncotarget 9:4758-4772
La, Mary K; Sedykh, Alexander; Fourches, Denis et al. (2018) Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions. Drug Saf 41:1059-1072
Gomes, Marcelo N; Muratov, Eugene N; Pereira, Maristela et al. (2017) Chalcone Derivatives: Promising Starting Points for Drug Design. Molecules 22:
Capuzzi, Stephen J; Muratov, Eugene N; Tropsha, Alexander (2017) Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS. J Chem Inf Model 57:417-427
Gomes, Marcelo N; Braga, Rodolpho C; Grzelak, Edyta M et al. (2017) QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity. Eur J Med Chem 137:126-138

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