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A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data

A preprint version of the article is available at arXiv.

Abstract

Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven’t been indicated for treating coronary artery disease, paving the way for drug repurposing.

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Fig. 1: Flowchart of overall drug repurposing framework.
Fig. 2: Illustration of the deep learning model for predicting treatment probability (or propensity score) that we used to correct confounding from time sequence data (including diagnoses dt, prescriptions pt and demographics bt).
Fig. 3: Distribution of estimated ATE of drugs on defined outcomes across the 50 bootstrap samples.
Fig. 4: The SMD values of the top 20 well-balanced covariates.

Data availability

The data we use is MarketScan Commercial Claims and Encounters (CCAE, more than 100 million patients, from 2012 to 2017) The details of source data structure and prepossessed input data demo are available at the Github repository https://github.com/ruoqi-liu/DeepIPW. Access to the MarketScan data analysed in this manuscript is provided by the Ohio State University. The dataset is available from IBM at https://www.ibm.com/products/marketscan-research-databases.

Code availability

The source code for this paper can be downloaded from the Github repository at https://github.com/ruoqi-liu/DeepIPWor the Zenodo repository at https://doi.org/10.5281/zenodo.4079391.

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Acknowledgements

This work was funded in part by the National Center for Advancing Translational Research of the National Institutes of Health under award number CTSA Grant UL1TR002733. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors

Contributions

P.Z. conceived the project. R.L. and P.Z. developed the method. R.L. conducted the experiments. R.L., L.W. and P.Z. analysed the results. R.L., L.W. and P.Z. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ping Zhang.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Daniel Merk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 CAD cohorts characteristics.

a, The patients’ distribution of total time in the database. b, The patient’s distribution of time before/after CAD initiation date. c, The growth of the number of patients developing outcomes after CAD initiation date. d, The gender distribution with age at CAD initiation date.

Extended Data Fig. 2 Performance comparison of LSTM-IPTW and LR-IPTW using drug candidate: diltiazem (with known CAD indication).

The three figures on the top are results obtained from LSTM-IPTW, while the figures on the bottom are from LR-IPTW. a, and (d) The absolute SMD of each covariate in the original data (orange triangles) and in the weighted data (blue circles). b, and (e) The distribution of estimated propensity scores over user (orange area) and non-user (blue area) cohorts. c, and (f) The ROC curves for the propensity model (orange), expected value (green) and weighted propensity (blue).

Extended Data Fig. 3 Distribution of estimated ATE of drug classes on defined outcomes across the 50 bootstrap samples.

All these showing drug classes satisfy two conditions: adjusted p-value less than 0.05 and post unbalanced ratio less than 2%. Within the boxplot, the central line denotes the median, and the bottom and the top edges denote the 25th(Q1) and 75th(Q3) and percentiles respectively. The whiskers extend to 1.5 times the interquartile range.

Extended Data Fig. 4 The list of significant drug classes.

The drug classes are denoted using ATC code and corresponding names.

Extended Data Fig. 5 The estimated treatment effects for CAD over balanced and statistically significant drug combinations.

The drug combinations are ranked by the estimated ATE values.

Extended Data Fig. 6 Performance comparison of proposed method and three pre-clinical methods evaluated by Precision@K.

The values of K are selected from {6, 9}.

Extended Data Fig. 7 Retrieved additional repurposing candidates under different thresholds’ setting.

The adjusted p-value is changed to 0.15 and the post unbalanced ratio remains the same as previous setting (less than 2%).

Extended Data Fig. 8 The definition of user and non-user cohorts.

Index date refers to the first prescription of the trial’s drug (user cohort) or the alternative drug (non-user cohort). The time period before the index date is the baseline period, and the time after the index date is the follow-up period. The patient covariates are collected during the baseline period and the treatment effects areevaluated at the follow-up period.

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Supplementary Tables 1–6 and Figs. 1 and 2.

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Liu, R., Wei, L. & Zhang, P. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nat Mach Intell 3, 68–75 (2021). https://doi.org/10.1038/s42256-020-00276-w

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