Physiologically based mechanistic modelling to predict complex drug–drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—The effect of diltiazem on the time-course of exposure to triazolam

Yeo, Karen Rowland; Jamei, Masoud; Yang, Jiansong; Tucker, Geoffrey T.; Rostami‐Hodjegan, Amin

doi:10.1016/j.ejps.2009.12.002

Cited by 169 publications

(107 citation statements)

References 53 publications

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“…Increasingly, however, it is accepted that there are several limitations (such as the appropriate selection of [I] (Rowland et al, 2006)) associated with the use of classical extrapolation of in vitro data when assessing mDDIs that preclude sensible assessment of the clinical relevance of novel interactions. Indeed, the substantial benefits of mechanistic (PBPK) extrapolation of mDDI assessments have been extensively reported and adopted (Sheiner and Steimer, 2000; Danhof et al, 2008; Rowland Yeo et al, 2010; Rowland et al, 2011) and this approach is recommended for the preclinical assessment of mDDIs for regulatory review (Zhao et al, 2011; FDA, 2012). …”

Section: Discussionmentioning

confidence: 99%

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Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

et al. 2016

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In vivo cocktail pathway phenotyping (ICPP) is routinely used to assess the metabolic drug–drug interaction (mDDI) potential of new drug candidates (NDC) during drug development. However, there are a number of potential limitations to this approach and the use of validated drug cocktails and study protocols is essential. Typically ICPP mDDI studies assess only the impact of interactions following multiple postulated perpetrator doses and hence the emphasis in terms of validation of these studies has been ensuring that there are no interactions between probe substrates. Studies assessing the comparative impact of single and multiple doses of the postulated perpetrator have the potential to provide richer information regarding both the clinical impact and mechanism of mDDIs. Using modafinil as a model compound, we sought to develop an optimized ICPP mDDI study protocol to evaluate the potential magnitude and clinical relevance of mDDIs using a physiologically based pharmacokinetic modeling approach.

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Section: Discussionmentioning

confidence: 99%

“…The differential equations used by the simulator describing enzyme kinetics and the impact of co-variates have been described previously (Rowland Yeo et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

et al. 2016

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“…In contrast to a 'full' PBPK model in which organs and tissues are separately represented, a 'semi' PBPK model combines tissues having similar drug partitioning and distribution equilibrium with the plasma compartment [12]. These semi-PBPK models have been utilized to study the dynamics or time-based characteristics of drug-drug interactions [13][14][15]. A semi-PBPK model was also constructed for erythromycin.…”

Section: General Pbpk Model Buildingmentioning

confidence: 99%

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Grillo

Zhao

Bullock

et al. 2012

Biopharm & Drug Disp

102

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Background: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor. Methods: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin. Results: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4-or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9-or 3.0-fold increase in exposure in older subjects. Conclusions: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban. Copyright © 2012 John Wiley & Sons, Ltd.

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“…In addition, they have been used to evaluate aspects of experimental design including dosage, choice of dosage form, and the timing of dosage of interacting compounds (153,154). These models also have been used to assess more complex scenarios involving simultaneous dose-dependent inhibition and induction (124,155) as well as competition for plasma binding, the inhibitory effects of both parent drug and metabolites (149,156,157) (Figure 7), the lack of adherence, and multiple DDIs. The latter are a particular regulatory concern and impose prohibitive limitations on in vivo studies to cover the various permutations of combinations.…”

Section: Prediction Of Drug-drug Interactionsmentioning

confidence: 98%

Physiologically-Based Pharmacokinetics in Drug Development and Regulatory Science

Rowland

Peck

Tucker

2011

Annu. Rev. Pharmacol. Toxicol.

566

436

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The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.

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Physiologically based mechanistic modelling to predict complex drug–drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—The effect of diltiazem on the time-course of exposure to triazolam

Cited by 169 publications

References 53 publications

Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Physiologically-Based Pharmacokinetics in Drug Development and Regulatory Science

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