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

DImitris E. Messinis, Ioannis N. Melas, Junguk Hur, Navya Varshney, Leonidas G. Alexopoulos, Jane P.F. Bai

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)166-174
Number of pages9
JournalCPT: Pharmacometrics and Systems Pharmacology
Issue number3
StatePublished - Mar 2018
Externally publishedYes

ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)


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