CiPA In Silico

MODEL

CiPA In Silico

A new model that combines dynamic drug-hERG interactions and multichannel pharmacology to improve the prediction of the torsadogenic risk of drugs

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Broadly Validated

Developed by the Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science at FDA

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Display and Export

Results can easily be visualized and can be downloaded in JSON or CSV format

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Easy to Use

Well-designed wizard that step by step guides through the setup and run of three different simulation workflows

CiPA In Silico can be used to set up three different simulation workflows: hERG fitting, Hill fitting, and AP simulation.

hERG fitting performs uncertainty characterization for drug binding kinetics of the human Ether-à-go-go-Related Gene (hERG) channel gating model. It requires the upload of a CSV file containing laboratory data on time of recording, fractional current recorded, drug concentration, cell, and sweep number.

Hill fitting performs uncertainty characterization of dose-response curves for up to six ionic currents (ICaL, INaL, INa, Ito, IKs, and IK1). It requires the upload of a CSV file containing pharmacology data on drug concentration, ion channel type and percentage of ion channel block.

AP simulation combines the results of the modules hERG fitting and Hill fitting to simulate for a compound at a given concentration its effects on the Action Potential (AP) of a population of ventricular cardiomyocyte cells and to estimate from them the population value of the safety marker qNet and its uncertainty interval. The module requires the upload of CSV files for hERG and Hill fitting as described before. In addition, it requires the expected maximal therapeutic concentration (Cmax) of the compound of interest and a few Cmax multipliers in the range 1 – 30.

Research team

CiPA In Silico is the model developed by Dr. Li and colleagues at the Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science at FDA (Li et al. 2017).

It combines a dynamic drug-hERG interactions model integrated into the O’Hara-Rudy cardiomyocyte model (O’Hara et al. 2011) and multichannel pharmacology (up to 7 ion currents, i.e. IKr, IKs, ICaL, INa, INaL, Ito and IK1) to simulate the net charge metric qNet for Torsade de Pointes risk prediction (Li et al. 2019).

Articles & publications

O’Hara, T. et al. PLoS Comput. Biol. 7 2011 DOI: 10.1371/journal.pcbi.1002061

Li, Z. et al. Circ. Arrhythm. Electrophysiol. 10 2017 DOI: 10.1161/CIRCEP.116.004628

Chang, K. et al. Front. Physiol. 8:917 2017 DOI: 10.3389/fphys.2017.00917

Li, Z. et al. Clinical Pharmacology & Therapeutics 105 2019 DOI: 10.1002/cpt.1184

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