Abstract
BackgroundWith 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.MethodologyIn 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/SignificanceTo 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.
Highlights
The current model of drug discovery and development is perceived as a costly and time-consuming process [1]
To assess the performance of our approach, our evaluation involves two aspects: (i) whether the drug indications suggested by our mechanism of action (MOA)-based approach are the original indications of the drugs, without the direct use of such information; (ii) whether our suggested drug indications are currently under clinical trials for the indications according to ClinicalTrials.gov
We downloaded the records of the clinical trials from http:// clinicaltrials.gov dated in December 2011. 289 drugs that do not have cancer as their original indications are found to be currently investigated as therapeutics for various types of cancers
Summary
The current model of drug discovery and development is perceived as a costly and time-consuming process [1]. It is a discovery process on how an existing drug compound can be used for the treatment of diseases other than its original indication Reusing these drug compounds has the advantage of bypassing many of the expensive steps of drug development, such as in vitro and in vivo screening, chemical optimization, toxicology, bulk manufacturing, formulation development. 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 This kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process
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