Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative

Zhihua Li, Bradley J. Ridder, Xiaomei Han, Wendy W. Wu, Jiansong Sheng, Phu N. Tran, Min Wu, Aaron Randolph, Ross H. Johnstone, Gary R. Mirams, Yuri Kuryshev, James Kramer, Caiyun Wu, William J. Crumb, David G. Strauss

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro-arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi-ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre-specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

Original languageEnglish (US)
Pages (from-to)466-475
Number of pages10
JournalClinical pharmacology and therapeutics
Volume105
Issue number2
DOIs
StatePublished - Feb 2019

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ASJC Scopus subject areas

  • Pharmacology
  • Pharmacology (medical)

Cite this

Li, Z., Ridder, B. J., Han, X., Wu, W. W., Sheng, J., Tran, P. N., Wu, M., Randolph, A., Johnstone, R. H., Mirams, G. R., Kuryshev, Y., Kramer, J., Wu, C., Crumb, W. J., & Strauss, D. G. (2019). Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative. Clinical pharmacology and therapeutics, 105(2), 466-475. https://doi.org/10.1002/cpt.1184