CardiacPBPK: A tool for the prediction and visualization of time-concentration profiles of drugs in heart tissue

BACKGROUND AND OBJECTIVE Prediction of drug concentration in heart tissue is important in terms of drug safety and efficacy. This work presents the Open-Source CardiacPBPK platform for the prediction of the time-concentration profile of drugs, which could potentially reduce the risk of drug development failure due to cardiotoxicity. The objective of the CardiacPBPK development is to accelerate and simplify the in-silico toxicological assessment of new drugs, and to provide supportive material for the research community to use. METHODS The CardiacPBPK software provides a modular implementation of the PBPK model of heart tissue. It can be easily accessed via the Internet or installed locally. The graphical user interface and tabular design are easy to configure and use. RESULTS CardiacPBPK is a tool designed to predict and visualize the time-concentration profiles of a parent compound, and one metabolite, in venous plasma and heart tissue after oral or intravenous drug administration. CardiacPBPK is built on the R-environment framework and supports shiny application features such as interactive visualization of the results, and web applications interface by default. A shiny application refers to a computer program created with the use of shiny package in R. The application is freely available at https://github.com/jszlek/CardiacPBPK and https://sourceforge.net/projects/cardiacpbpk/. This open-source application runs on all platforms supporting R-environment (Linux, Windows, Mac OS X, Solaris). CONCLUSIONS We demonstrate the application of CardiacPBPK by simulating the study of amitriptyline intoxication in the case of CYP2D6 genetic polymorphism.

[1]  Sebastian Polak,et al.  Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development. , 2019, Drug discovery today.

[2]  Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals , 2018, Journal of Pharmacokinetics and Pharmacodynamics.

[3]  S. Polak,et al.  Mechanistic Physiologically Based Pharmacokinetic (PBPK) Model of the Heart Accounting for Inter-Individual Variability: Development and Performance Verification. , 2017, Journal of pharmaceutical sciences.

[4]  Stephanie Läer,et al.  Physiologically Based Pharmacokinetic Modeling: Methodology, Applications, and Limitations with a Focus on Its Role in Pediatric Drug Development , 2011, Journal of biomedicine & biotechnology.

[5]  Sebastian Polak,et al.  Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram , 2017, The AAPS Journal.

[6]  J. Brockmöller,et al.  Cytochrome P450 2D6 variants in a Caucasian population: allele frequencies and phenotypic consequences. , 1997, American journal of human genetics.

[7]  N. Patel,et al.  Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin , 2018, The AAPS Journal.

[8]  L. Bertilsson,et al.  10‐hydroxylation of nortriptyline in white persons with 0, 1, 2, 3, and 13 functional CYP2D6 genes , 1998, Clinical pharmacology and therapeutics.

[9]  M. Wiese,et al.  Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation , 2018, British journal of clinical pharmacology.

[10]  Ivan Nestorov,et al.  Whole Body Pharmacokinetic Models , 2003, Clinical pharmacokinetics.

[11]  K Rowland-Yeo,et al.  Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development , 2013, CPT: pharmacometrics & systems pharmacology.

[12]  Kennon Heard,et al.  A novel approach for estimating ingested dose associated with paracetamol overdose. , 2016, British journal of clinical pharmacology.

[13]  F. Sjöqvist,et al.  Plasma levels of monomethylated tricyclic antidepressants during treatment with imipramine-like compounds. , 1967, Life sciences.