Identifying novel drug indications through automated reasoning

Luis Tari, Nguyen Vo, Shanshan Liang, Jagruti Patel, Chitta Baral, James Cai

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Background: With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process. Methodology: In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications. Conclusion/Significance: To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

Original languageEnglish (US)
Article numbere40946
JournalPLoS One
Volume7
Issue number7
DOIs
StatePublished - Jul 23 2012

Fingerprint

drugs
Pharmaceutical Preparations
Drug Recalls
neoplasms
new drugs
Neoplasms
Oncology
Protein Interaction Maps
Knowledge Bases
antineoplastic agents
protein-protein interactions
methodology
Drug Interactions
Molecular interactions
Computer Simulation
clinical trials
Language
Clinical Trials
Pharmacology
Proteins

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Identifying novel drug indications through automated reasoning. / Tari, Luis; Vo, Nguyen; Liang, Shanshan; Patel, Jagruti; Baral, Chitta; Cai, James.

In: PLoS One, Vol. 7, No. 7, e40946, 23.07.2012.

Research output: Contribution to journalArticle

Tari, Luis ; Vo, Nguyen ; Liang, Shanshan ; Patel, Jagruti ; Baral, Chitta ; Cai, James. / Identifying novel drug indications through automated reasoning. In: PLoS One. 2012 ; Vol. 7, No. 7.
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