Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018‐2019 Submissions to the US FDA's Office of Clinical Pharmacology

Zhang, Xinyuan; Yang, Yuching; Grimstein, Manuela; Fan, Jianghong; Grillo, Joseph A.; Huang, Shiew‐Mei; Zhu, Hao; Wang, Yaning

doi:10.1002/jcph.1767

Cited by 120 publications

(141 citation statements)

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“…Although limited, there are a number of cases that have successfully used PBPK models to predict transporter‐mediated drug interactions 43 . The PBPK model presented in this article was considered adequate for prediction of DDIs of ivosidenib with CYP3A4, CYP2B6, CYP2C8, CYP2C9, OATP1B1/OATP1B3, and OAT3 substrates by the US Food and Drug Administration (FDA) 26,44 . The capability of the model to predict DDI owing to ivosidenib as a P‐gp inhibitor was considered inadequate by the agency due to lack of mechanism‐based oral absorption model 26,44 .…”

Section: Discussionmentioning

confidence: 99%

“…The PBPK model presented in this article was considered adequate for prediction of DDIs of ivosidenib with CYP3A4, CYP2B6, CYP2C8, CYP2C9, OATP1B1/OATP1B3, and OAT3 substrates by the US Food and Drug Administration (FDA) 26,44 . The capability of the model to predict DDI owing to ivosidenib as a P‐gp inhibitor was considered inadequate by the agency due to lack of mechanism‐based oral absorption model 26,44 . Although modeling results showed that the inhibition of OAT3 and P‐gp by ivosidenib will have minimal clinical relevance at 500 mg dose level, based on the agency’s feedback, the language for these two transporters was derived for ivosidenib label.…”

Section: Discussionmentioning

confidence: 99%

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PBPK modeling to predict drug‐drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Bolleddula

Yang

et al. 2021

CPT Pharmacom & Syst Pharma

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Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid Leukemia (AML) who are ≥75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Accepted ArticleThis article is protected by copyright. All rights reserved Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of Pglycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based PK (PBPK) model was developed to predict DDIs of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean AUC and C max ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

PBPK modeling to predict drug‐drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Bolleddula

Yang

et al. 2021

CPT Pharmacom & Syst Pharma

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“…The major PBPK applications (among PBPK-related publications) were associated with the study design, predicting formulation effects and metabolic DDIs, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter-mediated DDIs have increased proportionally over the last decade [ 75 ]. In recent surveys of the PBPK-based submissions to FDA [ 76 , 77 ], about 56–67% of the submissions used PBPK modeling to evaluate the drug interaction potential.…”

Section: Dynamic Pbpk Model For Pk Ddismentioning

confidence: 99%

Evaluation of Pharmacokinetic Drug–Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

2021

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Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

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“…Physiologically based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development 1,2 for a variety of purposes such as predicting optimal dosing, drug-drug interactions (DDIs), food effects, and pharmacokinetic profiles in specific populations (eg, pediatric subjects, patients with hepatic, or renal impairment). 3,4 As a result, the number of regulatory submissions including PBPK applications submitted to the US Food and Drug Administration (FDA) has significantly increased over the past few years. 5 PBPK models can be developed based on parameters estimated from in vitro tests.…”

mentioning

confidence: 99%

Usage of In Vitro Metabolism Data for Drug‐Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

Lee

Yang

Zhang

et al. 2021

The Journal of Clinical Pharma

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The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL int ) and fractional contribution of the metabolizing enzyme toward total metabolism (f m ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL int and f m , 29 and 20 new drug applications were included for evaluation, respectively. For CL int , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f m , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL int were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f m from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f m when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL int and f m still need to be optimized based on in vivo data.

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Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018‐2019 Submissions to the US FDA's Office of Clinical Pharmacology

Cited by 120 publications

References 8 publications

PBPK modeling to predict drug‐drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

PBPK modeling to predict drug‐drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Evaluation of Pharmacokinetic Drug–Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Usage of In Vitro Metabolism Data for Drug‐Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

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