Document Type
Article
Publication Date
1-17-2018
Publication Title
CPT: Pharmacometrics and Systems Pharmacology
Volume
7
Abstract
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.
Issue
3
First Page
166
Last Page
174
DOI
10.1002/psp4.12272
ISSN
21638306
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Dimitris E. Messinis, Ioannis N. Melas, Junguk Hur, et al.. "Translational Systems Pharmacology-Based Predictive Assessment of Drug-Induced Cardiomyopathy" (2018). Biomedical Sciences Faculty Publications. 20.
https://commons.und.edu/bms-fac/20