Physiologically Based Predictions of the Impact of Inhibition of Intestinal and Hepatic Metabolism on Human Pharmacokinetics of CYP3A Substrates

Fenneteau, Frédérique; Poulin, Patrick; Nekka, Fahima

doi:10.1002/jps.21802

Cited by 59 publications

(61 citation statements)

References 112 publications

(172 reference statements)

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“…Simcyp-simulated results were comparable to those generated with other PBPK models (Zhang et al, 2009;Fenneteau et al, 2010). For midazolam drug interaction studies, greater than 90% of the predictions had an error less than 2; more accurate predictions were achieved when the observed and simulated profiles were superimposed (e.g., diltiazem SR and verapamil SR).…”

Section: Discussionsupporting

confidence: 57%

“…Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al, 2000;Zhang et al, 2009;Fenneteau et al, 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models.…”

Section: Introductionmentioning

confidence: 99%

“…Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al, 2000;Zhang et al, 2009;Fenneteau et al, 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models.…”

mentioning

confidence: 99%

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Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Wang¹

2010

Drug Metab Dispos

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ABSTRACT:Accurate prediction of the extent of mechanism-based CYP3A inhibition is critical in determining the timing of clinical drug interaction studies in drug development. To evaluate the prediction accuracy of the static and Simcyp time-based approaches, 54 clinical drug interactions involving mechanism-based CYP3A inhibitors were predicted using both methods. The Simcyp timebased approach generated better prediction when 0.03 h ؊1 was used as the hepatic CYP3A enzyme degradation rate constant (k deg ) value. Of the predictions 87 and 55% had an error less than 2 and 0. Drug-drug interactions remain an area of focus in drug discovery and development. Mechanism-based inhibition of CYP3A is one of the major causes of clinical drug-drug interactions and generally leads to greater concern, as highlighted by the list of moderate and strong CYP3A inhibitors in the Food and Drug Administration (2006) Drug Interaction Guidance. The inhibitory effects of a mechanism-based CYP3A inhibitor persist long after the compound is eliminated from the body because the recovery of CYP3A enzyme activity requires de novo protein synthesis or slow release of the enzyme from the enzyme-inhibitor complex. Because of the primary role of CYP3A in drug disposition, prediction of the clinical drug interaction potential of a mechanism-based CYP3A inhibitor would be helpful in guiding the timing and design of clinical studies.Several approaches have been developed to predict clinical outcomes of mechanism-based inhibition. The static mathematical model developed by Mayhew et al. (2000) is a commonly used approach to predict mechanism-based drug interactions from in vitro estimated inactivation parameters. Several modifications to the original model have been made to incorporate the effects of intestinal wall metabolism (Wang et al., 2004b), competitive inhibition, and induction (Fahmi et al., 2009). Nonetheless, these static models are only capable of predicting the average magnitude of drug interactions across a population, assuming that the steady state of enzyme inhibition has been reached. Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al., 2000;Zhang et al., 2009;Fenneteau et al., 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models. They can be used to simulate drug concentrationtime profiles and to explore the effects of various dosing regimens. Simcyp (Simcyp Limited, Sheffield, UK) is a commercially available absorption, distribution, metabolism, and...

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Section: Discussionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Wang¹

2010

Drug Metab Dispos

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“…Recently, there has been an increasing focus on translational modelling approaches compared to the traditional practice of fitting model parameters to preclinical in vivo data [65,66]. IVIVE-PBPK models reported by Fenneteau et al [67] and Ball et al [68] demonstrate promising examples. This review has addressed imperative differences among various PBPK models in use, including details on brain compartmentalization and parameterization.…”

Section: Ivive-pbpk Model For the Central Nervous Systemmentioning

confidence: 99%

“…Similar to empirical PBPK models, the IVIVE-PBPK model uses a bottom-up approach through IVIVE, with the use of appropriate physiological scaling factors. Fenneteau and colleagues scaled in vitro passive and efflux permeabilities of domperidone to in vivo intrinsic permeabilities, and then using literature-based physiological values for in vivo membrane surface area, they further scaled the permeabilities to whole organ permeabilities [67]. The in vitro intrinsic P-gp efflux permeability determined in Caco-2 cells was corrected using the relative fraction of MDR1A/1B messenger RNA expression measured in the brain compared to that in the intestine.…”

Section: Ivive-pbpk Model For the Central Nervous Systemmentioning

confidence: 99%

Utilizing in vitro transporter data in IVIVE-PBPK: an overview

Mallick

2017

ADMET DMPK

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<p class="ADMETabstracttext">In vitro-in vivo extrapolation (IVIVE) integrated in physiologically-based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development processes to predict human pharmacokinetic (PK) parameters. Drug transporters can influence drug pharmacokinetics and are key aspects contributing to the development of a successful drug. This review provides a snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics. The paper also describes the potential of IVIVE-PBPK models as prospective approaches to predict the role of drug transporters in drug discovery and development.</p>

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In SilicoModels of Drug Metabolism and Drug Interactions

Dimelow

Metcalfe

Thomas

2012

Encyclopedia of Drug Metabolism and Interactions

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In silico methods have multiple roles to play in drug discovery by reducing costs and increasing screening throughput compared to in vitro and in vivo methods, and by providing information to help guide chemical synthesis in producing compounds having desired properties. In terms of drug metabolism, in silico methods can make predictions regarding the net rate of metabolism of a compound, the identity of enzyme isoforms that are likely to metabolise a compound, the concentration dependency of metabolism and the identity of expected metabolites. In terms of drug–drug interactions, models have been described for the inhibition of metabolism of one compound by another, and for the compound–dependent induction of drug–metabolising enzymes. Analogous models have been described for a number of properties of drug–transporting proteins. Physiologically‐based pharmacokinetic (PBPK) models can predict the in vivo consequences of drug–drug interactions observed in in vitro assays or predicted by in silico models. In this chapter we discuss several areas in which in silico modeling can contribute to the quantitative or qualitative understanding and prediction of drug metabolism and drug–drug interactions. We describe a number of the available in silico approaches to these application areas and discuss, in some detail, a number of specific application areas in which these methods have been used. We describe the use of physiologically based modelling to obtain predictions of the extent of drug–drug interactions expected in vivo . We finish with a discussion of some practical aspects of applying in silico methods in drug discovery.

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Physiologically Based Predictions of the Impact of Inhibition of Intestinal and Hepatic Metabolism on Human Pharmacokinetics of CYP3A Substrates

Cited by 59 publications

References 112 publications

Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Utilizing in vitro transporter data in IVIVE-PBPK: an overview

In SilicoModels of Drug Metabolism and Drug Interactions

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