Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources

Author:

Patrick Matthew T1ORCID,Bardhi Redina12,Raja Kalpana13,He Kevin4,Tsoi Lam C145ORCID

Affiliation:

1. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA

2. School of Medicine, Wayne State University, Detroit, Michigan, USA

3. Morgridge Institute for Research, Madison, Wisconsin, USA

4. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

5. Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

Abstract

Abstract Objective Drug–drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients). Materials and Methods Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug–gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. Results Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records. Conclusions By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.

Funder

National Institutes of Health

NIAMS

Taubman Institute Innovation Project

Dermatology Foundation

National Psoriasis Foundation

Babcock Memorial Trust

Precision Health Scholars Award from the University of Michigan

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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