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      Framework for identifying drug repurposing candidates from observational healthcare data


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          Observational 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 Methods

          Our 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.


          We 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.


          Estimating 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.


          Our 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.

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          Most cited references45

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Drug repurposing: progress, challenges and recommendations

              Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

                Author and article information

                JAMIA Open
                JAMIA Open
                JAMIA Open
                Oxford University Press
                December 2020
                31 December 2020
                31 December 2020
                : 3
                : 4
                : 536-544
                [1 ] Healthcare Informatics, IBM Research-Haifa, Mount Carmel Haifa , Israel
                [2 ] Formerly Healthcare Informatics, IBM Research-Haifa, Mount Carmel Haifa , Israel
                Author notes

                Present address: K Health, Tel Aviv, Israel.

                Present address: MeMed Dx, Haifa, Israel.

                Present address: KI Research Institute, Kfar Malal, Israel.

                Corresponding Author: Michal Ozery-Flato, PhD, IBM Research-Haifa, Haifa University Campus, Mount Carmel Haifa, 3498825, Israel; ozery@ 123456il.ibm.com
                Author information
                © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 19 May 2020
                : 11 September 2020
                : 17 September 2020
                Page count
                Pages: 9
                Funded by: IBM, DOI 10.13039/100004316;
                Research and Applications

                drug repositioning,comparative effectiveness research,causal inference,parkinson’s disease,electronic health records


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