Development and evaluation of a physiologically based pharmacokinetic model to predict carvedilol-paroxetine metabolic drug–drug interaction in healthy adults and its extrapolation to virtual chronic heart failure patients for dose optimization

ABSTRACT

Purpose: The metabolic drug–drug interactions (mDDIs) are one of the most important challenges faced by the pharmaceutical industry during the drug development stage and are frequently associated with labeling restrictions and withdrawal of drugs. The capacity of physiologically based pharmacokinetic (PBPK) models to absorb and upgrade with the newly available information on drug and population-specific parameters, makes them a preferred choice over the conventional pharmacokinetic models for predicting mDDIs.

Method: A PBPK model capable of predicting the stereo-selective disposition of carvedilol after administering paroxetine by incorporating mechanism (time) based inhibition of CYP2D6 and CYP3A4 was developed by using the population-based absorption, distribution, metabolism and elimination (ADME) simulator, Simcyp®.

Results: The model predictions for both carvedilol enantiomers were in close agreement with the observed PK data, as the ratios for observed/predicted PK parameters were within the 2-fold error range. The developed PBPK model was successful in capturing an increase in exposures of R and S-carvedilol, due to the time-based inhibition of CYP2D6 enzyme caused by paroxetine.

Conclusion: The developed model can be used for exploring complex clinical scenarios, where multiple drugs are given concurrently, particularly in diseased populations where no clinical trial data is available.

KEYWORDS:

• Carvedilol
• paroxetine
• simcyp
• drug-drug interaction
• pbpk

Acknowledgments

The authors thank Certara for providing academic research licenses for the, Simcyp® and WinNonLin® programs. The authors thank Barry E. Bleske and colleagues for providing the original data from their clinical study [22].

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

M.F.R, and S.L, participated in the research design. M.F.R performed the simulations and data analysis. S.L supervised the work. Both authors contributed to the writing of the manuscript.

Supplemental Material

Supplemental data for this article can be accessed here.

Additional information

Funding

This paper was not funded.