Mechanistic Modeling to Predict Midazolam Metabolite Exposure from In Vitro Data

Nguyen, Hoa; Kimoto, Emi; Callegari, Ernesto; Obach, R. Scott

doi:10.1124/dmd.115.068601

Cited by 38 publications

(47 citation statements)

References 56 publications

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“…In the present study, desipramine, an active metabolite of the tricyclic antidepressant imipramine, was selected as a test case to apply the approach of integrating in vitro data into static and dynamic PBPK models for metabolite exposure prediction following administration of parent drug, which was proposed in the previous study (Nguyen et al, 2016). Both imipramine and desipramine were demonstrated to undergo hydroxylation catalyzed by CYP2D6 (Brøsen and Gram, 1988;Sallee and Pollock, 1990).…”

Section: Introductionmentioning

confidence: 99%

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Nguyen

Callegari

Obach

2016

Drug Metabolism and Disposition

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Major circulating drug metabolites can be as important as the drugs themselves in efficacy and safety, so establishing methods whereby exposure to major metabolites following administration of parent drug can be predicted is important. In this study, imipramine, a tricyclic antidepressant, and its major metabolite desipramine were selected as a model system to develop metabolite prediction methods. Imipramine undergoes N-demethylation to form the active metabolite desipramine, and both imipramine and desipramine are converted to hydroxylated metabolites by the polymorphic enzyme CYP2D6. The objective of the present study is to determine whether the human pharmacokinetics of desipramine following dosing of imipramine can be predicted using static and dynamic physiologically-based pharmacokinetic (PBPK) models from in vitro input data for CYP2D6 extensive metabolizer (EM) and poor metabolizer (PM) populations. The intrinsic metabolic clearances of parent drug and metabolite were estimated using human liver microsomes (CYP2D6 PM and EM) and hepatocytes. Passive diffusion clearance of desipramine, used in the estimation of availability of the metabolite, was predicted from passive permeability and hepatocyte surface area. The predicted area under the curve (AUC m /AUC p ) of desipramine/imipramine was 12-to 20-fold higher in PM compared with EM subjects following i.v. or oral doses of imipramine using the static model. Moreover, the PBPK model was able to recover simultaneously plasma profiles of imipramine and desipramine in populations with different phenotypes of CYP2D6. This example suggested that mechanistic PBPK modeling combined with information obtained from in vitro studies can provide quantitative solutions to predict in vivo pharmacokinetics of drugs and major metabolites in a target human population.

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Section: Introductionmentioning

confidence: 99%

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Nguyen

Callegari

Obach

2016

Drug Metabolism and Disposition

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“…M18 can be formed from M2 or M20; however, for modeling purposes it was assumed that it is formed solely from M2 (supported by the observation that M2 is the major route of metabolism). Hence, the systemic clearance of the secondary metabolite M18 (CL M18 ) was calculated according to equation

C {normalL}_{M 18} = {normalf}_{m, 2 - 18} \times C {normalL}_{M 2} \times \frac{AU {normalC}_{0 - \infty M 2}}{AU {normalC}_{0 - \infty M 18}}

where f m,2‐18 is the fraction of M2 forming M18, CL M2 was the systemic clearance of M2 calculated from equation , AUC 0‐∞M2 was the observed area under the plasma concentration‐time curve of M2 (from rifampin interaction study), and AUC 0‐∞M18 was the observed area under the plasma concentration‐time curve of M18 (from the human mass balance study).…”

Section: Methodsmentioning

confidence: 99%

Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling

Posada

Morse

Turner

et al. 2020

The Journal of Clinical Pharma

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Abemaciclib, a selective inhibitor of cyclin‐dependent kinases 4 and 6, is metabolized mainly by cytochrome P450 (CYP)3A4. Clinical studies were performed to assess the impact of strong inhibitor (clarithromycin) and inducer (rifampin) on the exposure of abemaciclib and active metabolites. A physiologically based pharmacokinetic (PBPK) model incorporating the metabolites was developed to predict the effect of other strong and moderate CYP3A4 inhibitors and inducers. Clarithromycin increased the area under the plasma concentration‐time curve (AUC) of abemaciclib and potency‐adjusted unbound active species 3.4‐fold and 2.5‐fold, respectively. Rifampin decreased corresponding exposures 95% and 77%, respectively. These changes influenced the fraction metabolized via CYP3A4 in the model. An absolute bioavailability study informed the hepatic and gastric availability. In vitro data and a human radiolabel study determined the fraction and rate of formation of the active metabolites as well as absorption‐related parameters. The predicted AUC ratios of potency‐adjusted unbound active species with rifampin and clarithromycin were within 0.7‐ and 1.25‐fold of those observed. The PBPK model predicted 3.78‐ and 7.15‐fold increases in the AUC of the potency‐adjusted unbound active species with strong CYP3A4 inhibitors itraconazole and ketoconazole, respectively; and 1.62‐ and 2.37‐fold increases with the concomitant use of moderate CYP3A4 inhibitors verapamil and diltiazem, respectively. The model predicted modafinil, bosentan, and efavirenz would decrease the AUC of the potency‐adjusted unbound active species by 29%, 42%, and 52%, respectively. The current PBPK model, which considers changes in unbound potency‐adjusted active species, can be used to inform dosing recommendations when abemaciclib is coadministered with CYP3A4 perpetrators.

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“…The authors acknowledged that some pharmacologic or toxicologic endpoints might be C max driven, and that predictions of metabolite C max would require additional in vivo data on the metabolite. Recently, Nguyen et al (2016) described a mechanistic approach for predicting the pharmacokinetics of midazolam and its metabolites using in vitro data and a physiologically based pharmacokinetic model. The predicted AUC ratios of metabolite (1-OH and 4-OH midazolam) to parent drug were in good agreement with clinical observations, and the model also afforded predicted plasma concentration versus time curves of midazolam and its metabolites.…”

Section: Discussionmentioning

confidence: 99%

A Pharmacokinetic Modeling Approach to Predict the Contribution of Active Metabolites to Human Efficacious Dose

Martin

Hill

Baker

et al. 2016

Drug Metabolism and Disposition

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A preclinical drug candidate, MRK-1 (Merck candidate drug parent compound), was found to elicit tumor regression in a mouse xenograft model. Analysis of samples from these studies revealed significant levels of two circulating metabolites, whose identities were confirmed by comparison with authentic standards using liquid chromatography-tandem mass spectrometry. These metabolites were found to have an in vitro potency similar to that of MRK-1 against the pharmacological target and were therefore thought to contribute to the observed efficacy. To predict this contribution in humans, a pharmacokinetic (PK) modeling approach was developed. At the mouse efficacious dose, the areas under the plasma concentration time curves (AUCs) of the active metabolites were normalized by their in vitro potency compared with MRK-1. These normalized metabolite AUCs were added to that of MRK-1 to yield a composite efficacious unbound AUC, expressed as "parent drug equivalents," which was used as the target AUC for predictions of the human efficacious dose. In vitro and preclinical PK studies afforded predictions of the PK of MRK-1 and the two active metabolites in human as well as the relative pathway flux to each metabolite. These were used to construct a PK model

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Mechanistic Modeling to Predict Midazolam Metabolite Exposure from In Vitro Data

Cited by 38 publications

References 56 publications

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling

A Pharmacokinetic Modeling Approach to Predict the Contribution of Active Metabolites to Human Efficacious Dose

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