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CPT Pharmacometrics Syst Pharmacol. 2018 Mar;7(3):166-174. doi: 10.1002/psp4.12272. Epub 2018 Jan 17.

Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.

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Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Department of Biomedical Sciences, University of North Dakota, School of Medicine & Health Sciences, Grand Forks, North Dakota, USA.
College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece.


Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers.

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