Luna Prieto Garcia (1), David Janzén (2), Kajsa Kanebratt (2), Hans Ericsson (2) Hans Lennernäs (1), Anna Lundahl (2)
(1) Department of Pharmacy, Uppsala University, Uppsala, Sweden, (2) Drug Metabolism and Pharmacokinetics Department at Cardiovascular, Renal and Metabolic Diseases, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Introduction: : Itraconazole has emerged as one of the best candidates to use as a standard CYP3A4 inhibitor in clinical drug-drug interaction (DDI) studies [1]. A physiologically based pharmacokinetic (PBPK) model for itraconazole is clearly beneficial for both DDI risk assessment and optimization of clinical trial design [2]. However, PBPK modeling for itraconazole using a ‘bottom-up’ approach is challenging. Not only due to complex saturable pharmacokinetics (PK) and presence of three metabolites exhibiting CYP3A4 inhibition, but also because discrepancies in reported in vitro data. Itraconazole and its metabolites are both substrates and inhibitors of CYP3A4 and the simulations of the plasma PK profiles are sensitive to all parameters included for both the parent and the metabolites. Therefore, the application of sensitivity analysis to the PBPK modeling is key to understand what factors that are of highest importance for the PK and DDI predictions.
Objectives:
- to perform a sensitivity analysis to assess the relative importance of the parameters into this complex model of a parent compound and two sequential metabolites in which all are substrates and inhibitors of the same enzyme.
- to provide a comprehensive ‘bottom-up’ PBPK model for itraconazole to increase the confidence in its DDI predictions.
Methods: The sensitivity analysis was done using two different local methods: the one-factor-at-a-time and the sensitivity index (SI) approach [4,5]. Input parameters were simultaneously experimentally determined for the parent and the metabolites. All generated data (in-vitro and in-vivo) were included in the model using Simcyp® software. To assess the performance of the model on simulating PK profiles a quantitative analysis was conducted according to previously established methods [5]. The difference factor (f1), which is a model-independent parameter, was applied for the comparison of the plasma concentration-time profiles of itraconazole, hydroxy-itraconazole (OH-ITZ) and keto-itraconazole (keto-ITZ). In addition, performance of the model was validated using pre-specified acceptance criteria against different dosing regimens and formulations for 18 DDI studies including midazolam and other CYP3A4 substrates.
Results: Sensitivity analysis was a key element for model development. It was crucial to guide decision-making on additional experimental resources to generate in vitro data that could be applied to the PBPK model development. The sensitivity analysis showed that the predicted area under the concentration- time curve (AUC) were more sensitive to plasma protein binding and enzyme kinetics compared to CYP3A4 inhibition. Overall, the PK profiles were predicted adequately, exhibiting f1 of 43%, 30%, and 52% for itraconazole, OH-ITZ, and keto-ITZ, respectively. The observed plasma concentration fell within the 90% prediction interval and accumulation over days was reasonably well captured by the model for all three compounds under all of the different conditions. In addition, clinical DDI studies with midazolam and other CYP3A4 substrates were successfully predicted within 2-fold error. Prediction precision and bias of DDI expressed as geometric mean fold error were for the AUC and peak concentration, 1.06 and 0.96, respectively.
Conclusions: This model provides improved mechanistic understanding of the PK and DDI of itraconazole and its metabolites. The sensitivity analyses highlight the importance of having robust in vitro and in vivo data to enable complex model building. The predictive DDI risk capability of this model is improved compared with the Simcyp itraconazole library model [6] (100% vs. 80% predicted within 2-fold), showing no bias and good precision. Therefore, our observations suggest that this novel PBPK model built for itraconazole and two of its main metabolites can be successfully used to both evaluate DDI involving new victim compounds and to facilitate optimal study design.
References:
[1] Center for Drug Evaluation and Research (CDER) (2017a) Guidance for Industry: Drug Interaction Studies—Study Design, Data Analysis and Clinical Implications, U.S. Food and Drug Administration, Silver Spring, MD.
[2] Rowland M, Peck C, and Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73.
[3] Marston SA and Polli JE (1997) Evaluation of direct curve comparison metrics applied to pharmacokinetic profiles and relative bioavailability and bioequivalence. Pharm Res 14:1363–1369.
[4] Nestorov IA (1999) Sensitivity analysis of pharmacokinetic and pharmacodynamic systems: I. A structural approach to sensitivity analysis of physiologically based pharmacokinetic models. J Pharmacokinet Biopharm 27:577–596.
[5] Bonate PL (2011) Pharmacokinetic–Pharmacodynamic Modeling and Simulation, 2nd ed, Springer, New York.
[6] Marsousi N, Desmeules JA, Rudaz S, and Daali Y (2017) Prediction of drug–drug interactions using physiologically–based pharmacokinetic models of CYP450 modulators included in Simcyp software. Biopharm Drug Dispos 39: 3-17.
Reference: PAGE 28 (2019) Abstr 8964 [www.page-meeting.org/?abstract=8964]
Poster: Drug/Disease Modelling - Absorption & PBPK