ArticleCPT: pharmacometrics & systems pharmacology2018
Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.
Article in CPT: pharmacometrics & systems pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Pharmacotherapy for Cancer Treatment-Related Cardiac Dysfunction and Heart Failure in Childhood Cancer Survivors.Paediatric drugs · 2023Review
- Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development.CPT: pharmacometrics & systems pharmacology · 2022Review
- Catalpol ameliorates doxorubicin-induced inflammation and oxidative stress in H9C2 cells through PPAR-γ activation.Experimental and therapeutic medicine · 2020Article
- Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges.CPT: pharmacometrics & systems pharmacology · 2020Article
- Dual Transcriptomic and Molecular Machine Learning Predicts all Major Clinical Forms of Drug Cardiotoxicity.Frontiers in pharmacology · 2020Article
- Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.The AAPS journal · 2019Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.