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Updated: May 24, 2026
12:08
Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
Data-driven prediction of drug effects and interactions.
Nicholas P Tatonetti1, Patrick P Ye, Roxana Daneshjou
1Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA.
Science Translational Medicine
|March 17, 2012
Summary
This study introduces a novel method to correct for unknown factors in adverse drug event reports, improving the detection of drug side effects and interactions. The approach enhances post-marketing surveillance for patient safety.
Area of Science:
- Pharmacovigilance and Drug Safety
- Computational Biology
- Health Informatics
Background:
- Adverse drug events (ADEs) are a major global health concern, often undetected during clinical trials.
- Existing spontaneous reporting systems lack crucial confounding data (e.g., demographics, medical history).
- This data limitation hinders accurate quantitative analysis of drug effects from post-marketing surveillance.
Purpose of the Study:
- To develop and validate an adaptive, data-driven method to correct for unmeasured confounding factors in ADE data.
- To introduce novel databases, Offsides and Twosides, for drug effects and drug-drug interaction side effects.
- To demonstrate the utility of these resources in identifying drug targets, predicting indications, and discovering drug class interactions.
Main Methods:
- An adaptive, data-driven approach was developed to correct for unknown or unmeasured confounding variables in ADE data.
- This method was combined with existing techniques for improved analysis of drug effects.
- New databases (Offsides, Twosides) were created and utilized for biological applications.
Main Results:
- The approach successfully improved analyses of drug effects across three test datasets.
- The study identified numerous drug class interactions, with 47 corroborated by independent electronic medical record analysis.
- A significant association was found between selective serotonin reuptake inhibitors (SSRIs) and thiazides, leading to prolonged QT intervals.
Conclusions:
- Confounding effects in observational clinical data can be effectively controlled through advanced data analysis techniques.
- This improved analysis enhances the detection and prediction of adverse drug effects and interactions.
- The developed methods and databases offer valuable tools for post-marketing drug safety surveillance and research.
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