Framework for identifying drug repurposing candidates from observational healthcare data.

Michal Ozery-Flato,Sivan Ravid,Oded Shaham,Yaara Goldschmidt,Chen Yanover

doi:10.1093/jamiaopen/ooaa048

Abstract

ObjectiveObservational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs.Materials and MethodsOur framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database.ResultsWe demonstrate the utility of the framework in a case study of Parkinson’s disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates.DiscussionEstimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases.ConclusionOur framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.

Highlights

Background and SignificanceDrug repurposing, or repositioning [1], is the quest to identify new uses for existing drugs
We propose a framework for generating repurposing candidates by emulating randomized controlled trials (RCTs) for on-market drugs using observational real-world data
To the best of our knowledge, our study is the first to demonstrate systematic screening for drug repurposing candidates in observational data using RCT emulation, and to compare drug repurposing hypotheses generated in different data resources

Summary

Introduction

Background and SignificanceDrug repurposing, or repositioning [1], is the quest to identify new uses for existing drugs. The term “real-world data” refers to information collected outside the clinical research settings; for example, in electronic health records (EHRs) or claims and billing data [9]. We propose a framework for generating repurposing candidates by emulating RCTs for on-market drugs using observational real-world data. We applied the described drug repurposing framework to Parkinson’s disease and emulated RCTs for hundreds of drugs, estimating their effect on three disease progression outcomes. To the best of our knowledge, our study is the first to demonstrate systematic screening for drug repurposing candidates in observational data using RCT emulation, and to compare drug repurposing hypotheses generated in different data resources

Methods

Results

Discussion

Conclusion