Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Ramsden, Diane; Perloff, Elke S.; Whitcher-Johnstone, Andrea; Ho, Thuy; Patel, R. C.; Kozminski, Kirk; Fullenwider, Cody L.; Zhang, George

doi:10.1124/dmd.121.000718

Cited by 10 publications

(10 citation statements)

References 62 publications

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“…The performance of MSMs and PBPK models in the case studies discussed in this paper confirms earlier reports of good predictive performance of MSMs [ 4 , 9 , 11 , 12 , 38 ] and shows that neither the complexity (e.g. gut model) nor the dynamic variation in perpetrator concentrations makes a big difference for the investigated scenarios.…”

Section: Discussionsupporting

confidence: 89%

“…Publications from the FDA provide a comprehensive list of New Drug Approvals by the FDA [6,13] as well as the agency's assessment of adequacy or inadequacy of the PBPK models for an intended purpose [13] (Table 18). The performance of MSMs and PBPK models in the case studies discussed in this paper confirms earlier reports of good predictive performance of MSMs [4,9,11,12,38] and shows that neither the complexity (e.g. gut model) nor the dynamic variation in perpetrator concentrations makes a big difference for the investigated scenarios.…”

Section: Discussionsupporting

confidence: 89%

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Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

2023

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Background and Objective Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications. Methods Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs. Results Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications. ConclusionsThe results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 89%

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

2023

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“…The DDI risk potential for vonoprazan with sensitive CYP3A substrates was evaluated using basic and mechanistic static modeling as proposed by regulatory guidance documents 11–13 . The mechanistic static model has been described elsewhere 21,22 . Several input parameters for vonoprazan were investigated, including using the maximum and average unbound plasma concentrations at steady‐state ( C max,ss , C av,ss,u ) observed following 20‐mg once‐daily (q.d.)…”

Section: Methodsmentioning

confidence: 99%

“…11 , 12 , 13 The mechanistic static model has been described elsewhere. 21 , 22 Several input parameters for vonoprazan were investigated, including using the maximum and average unbound plasma concentrations at steady‐state ( C max,ss , C av,ss,u ) observed following 20‐mg once‐daily (q.d.) or twice‐daily (b.i.d.)…”

Section: Methodsmentioning

confidence: 99%

Tiered approach to evaluate the CYP3A victim and perpetrator drug–drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling

Mulford¹,

Ramsden

Zhang

et al. 2023

CPT Pharmacom & Syst Pharma

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Vonoprazan is metabolized extensively through CYP3A and is an in vitro timedependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug-drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically-based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug-and system-specific parameters, and clinical data and observations from a [ 14 C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time-dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co-administration with moderate and strong CYP3A inducers should be avoided.How to cite this article: Mulford DJ, Ramsden D, Zhang L, et al. Tiered approach to evaluate the CYP3A victim and perpetrator drug-drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling. CPT

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“…Identification of P450 TDI by experimental methods is generally time-consuming and labor-intensive. − Thus, in silico methods have been developed for P450 TDI identification. Most of the in silico TDI studies are based on pharmacokinetic modeling, and few are by machine learning methods. − Galetin et al developed a PK/PD model for CYP3A4 TDI prediction based on pharmacokinetic assays. Li and co-authors applied data of voriconazole and proved it to be a time-dependent inhibitor of CYP3A4 via PK-Sim software.…”

Section: Introductionmentioning

confidence: 99%

Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4

et al. 2022

Mol. Pharmaceutics

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Cytochrome P450 3A4 (CYP3A4) is one of the major drug metabolizing enzymes in the human body and metabolizes ∼30–50% of clinically used drugs. Inhibition of CYP3A4 must always be considered in the development of new drugs. Time-dependent inhibition (TDI) is an important P450 inhibition type that could cause undesired drug–drug interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient way is of great importance to any new drug discovery effort. Here, we report the development of in silico classification models for prediction of potential CYP3A4 time-dependent inhibitors. On the basis of the CYP3A4 TDI data set that we manually collected from literature and databases, both conventional machine learning and deep learning models were constructed. The comparisons of different sampling strategies, molecular representations, and machine-learning algorithms showed the benefits of a balanced data set and the deep-learning model featured by GraphConv. The generalization ability of the best model was tested by screening an external data set, and the prediction results were validated by biological experiments. In addition, several structural alerts that are relevant to CYP3A4 time-dependent inhibitors were identified via information gain and frequency analysis. We anticipate that our effort would be useful for identification of potential CYP3A4 time-dependent inhibitors in drug discovery and design.

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Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Cited by 10 publications

References 62 publications

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

Tiered approach to evaluate the CYP3A victim and perpetrator drug–drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling

Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4

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