(M0930-02-07) Comparative Analysis of Pharmacokinetics and Drug-Drug Interactions in Microdosing Cocktail Studies Using Physiologically-Based Pharmacokinetic Modeling

Purpose: Microdosing cocktail or cassette dosing clinical trials have shown to be a promising approach to assess potential drug-drug interactions (DDIs) with major enzymes and transporters, such as cytochrome P450 (CYP) 3A and organic anion-transporting polypeptides (OATPs) simultaneously.  Factors such as ethnicity, disease status, and nonlinear absorption and clearance mechanisms can impact the pharmacokinetics (PK) of probe drugs, which can cause issues with the translatability of DDI results from microdose levels to clinically relevant doses. In the present study, physiologically-based PK (PBPK) models were created using PK data from individual PK studies. Comparative analyses and sensitive analysis were carried out on the model to study the PK variations of probe drugs and to assess the translatability of DDI data from microdosing cocktail studies to clinically relevant doses.

Methods: Literature data on the PK and DDI of substrates for CYP3A, P-glycoprotein (P-gp), and OATP probes (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) were digitized for modeling analysis to assess the effects of cocktail microdosing on PK as compared to the clinical dose studies. For the microdosing cocktail studies, a cocktail of 5 probe substrates was dosed to healthy young Caucasian subjects with or without CYP3A/OATP inhibitors (1), or to young or elderly Thai subjects with normal or impaired renal function (2). SimCYP^® simulator library files were used for PK simulations of these probe substrates, and the models were refined using the midazolam, dabigatran, and pitavastatin datasets to identify mechanisms causing the PK variability. DDI simulations were further conducted on refined models to capture DDIs with CYP3A/OATP inhibitors, and the PK variations of rosuvastatin and atorvastatin in renal function impaired populations

Results: The dose-normalized plasma concentration of midazolam was lower in healthy Thai subjects compared to healthy Caucasian subjects. After refining the CYP3A activity, the PBPK model was able to capture the plasma concentration-time profile of midazolam in Thai subjects. The library PBPK model file for dabigatran etexilate and dabigatran (3) did not capture the plasma concentration of dabigatran in both healthy Caucasian and Thai subjects at the microdose level. After refining the model, it was suggested that the renal clearance of dabigatran is dose-dependent. Renal clearance at microdose level was higher than that at clinically relevant dose levels. The refined model was found to be effective in capturing the plasma profiles of dabigatran in elderly Thai population with chronic kidney disease. Our previous pitavastatin PBPK model (4) captured the pitavastatin plasma profile in both Caucasian and Thailand subjects at the microdose level; however, further adjustment of the tissue distribution (tissue/plasma concentration ratio (Kp) scaler adjustment) was required to fully capture the plasma profile of pitavastatin with single dose of OATP inhibitor rifampicin. After refining the PBPK model using midazolam and pitavastatin data, the model was able to capture rosuvastatin and atorvastatin PK profiles in both Caucasian subjects with or without CYP3A/OATP inhibitors, as well as healthy and elderly Thailand subjects with renal impairment.

Conclusion: In conclusion, our analyses indicate that caution should be exercised when using microdosing cocktail studies to capture clinical DDIs. For OATP-mediated DDIs, an adjustment of tissue distribution is necessary to capture the PK profiles. The renal clearance saturation of dabigatran at clinically relevant doses requires careful interpretation for DDI data at microdose levels. When conducting DDI studies on Thai subjects, the lower CYP3A4 activity levels should also be considered.

References: 1. Prueksaritanont, T., Tatosian, D. A., Chu, X., Railkar, R., Evers, R., Chavez-Eng, C., et al. (2017). Validation of a Microdose Probe Drug Cocktail for Clinical Drug Interaction Assessments for Drug Transporters and CYP3A. Clin. Pharmacol. Ther. 101, 519–530.
2. Rattanacheeworn P., Kerr S. J., Kittanamongkolchai W., Townamchai N., Udomkarnjananun S., Praditpornsilpa K., et al. (2021). Quantification of CYP3A and drug transporters activity in healthy young, healthy elderly and chronic kidney disease elderly patients by a microdose cocktail approach. Front. Pharmacol. 12
3. Moj D., Maas H., Schaeftlein A., Hanke N., Gomez-Mantilla J. D., Lehr T. (2019). A comprehensive whole-body physiologically based pharmacokinetic model of dabigatran etexilate, dabigatran and dabigatran glucuronide in healthy adults and renally impaired patients. Clin. Pharmacokinet. 58, 1577–1593.
4. Liang, X., Koleske, M. L., Yang, J., & Lai, Y. (2024). Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example. The AAPS journal, 26(1), 13.