Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

Area of Science:

• Pharmacology
• Bioinformatics
• Clinical Research

Background:

• Drug repositioning, or repurposing, identifies new therapeutic uses for existing drugs.
• Historically, drug repurposing relied on serendipitous findings.
• Advancements in electronic health data and text mining enable systematic drug repurposing.

Purpose of the Study:

• To present a novel computational method for drug repositioning.
• To leverage ClinicalTrials.gov data for identifying potential drug repurposing candidates.
• To systematically mine clinical trial data for new drug indications.

Main Methods:

• Utilized text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer).
• Extracted Serious Adverse Events (SAE) data from randomized controlled trials in ClinicalTrials.gov.
• Developed a statistical algorithm to rank drugs with reduced SAE in treatment arms compared to control arms.

Main Results:

• Identified drugs associated with a lower incidence of predefined Serious Adverse Events.
• The method suggests drugs that may reduce specific adverse events, indicating potential therapeutic effects.
• Generated hypotheses for novel drug applications based on observed SAE patterns.

Conclusions:

• The described method offers a systematic approach to drug repositioning.
• Mining clinical trial data, specifically SAEs, can uncover new therapeutic potentials for marketed drugs.
• This approach facilitates hypothesis generation for drug repurposing, potentially accelerating the discovery of new treatments.