Dataset Information

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model.

ABSTRACT: Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Indmax) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Indmax was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Indmax = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

SUBMITTER: Almond LM

PROVIDER: S-EPMC4885489 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model.

Almond Lisa M LM Mukadam Sophie S Gardner Iain I Okialda Krystle K Wong Susan S Hatley Oliver O Tay Suzanne S Rowland-Yeo Karen K Jamei Masoud M Rostami-Hodjegan Amin A Kenny Jane R JR

Drug metabolism and disposition: the biological fate of chemicals 20160329 6

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Indmax) fo ...[more]

PMID: 27026679

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Project description:The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Project description:Eliglustat is a glucosylceramide synthase inhibitor indicated as a long-term substrate reduction therapy for adults with type 1 Gaucher disease, a lysosomal rare disease. It is primarily metabolized by cytochrome P450 2D6 (CYP2D6), and variants in the gene encoding this enzyme are important determinants of eliglustat pharmacokinetics (PK) and drug-drug interactions (DDIs). The existing drug label addresses the DDIs to some extent but has omitted scenarios where both metabolizing CYPs (2D6 and 3A4) are mildly or moderately inhibited. The objectives of this study were (i) to develop and validate an eliglustat physiologically-based pharmacokinetic (PBPK) model with and without drug interactions, (ii) to simulate untested DDI scenarios, and (iii) to explore potential dosing flexibility using lower dose strength of eliglustat (commercially not available). PK data from healthy adults receiving eliglustat with or without interacting drugs were obtained from literature and used for the PBPK model development and validation. The model-predicted single-dose and steady-state maximum concentration (Cmax ) and area under the concentration-time curve (AUC) of eliglustat were within 50-150% of the observed values when eliglustat was administered alone or coadministered with ketoconazole or paroxetine. Then as model-based simulations, we illustrated eliglustat exposure as a victim of interaction when coadministered with fluvoxamine following the US Food and Drug Administration (FDA) dosing recommendations. Second, we showed that with lower eliglustat doses (21 mg, 42 mg once daily) the exposure in participants of intermediate and poor metabolizer phenotypes was within the outlined safety margin (Cmax <250 ng/mL) when eliglustat was administered with ketoconazole, where the current recommendation is a contraindication of coadministration (84 mg). The present study demonstrated that patients with CYP2D6 deficiency may benefit from lower doses of eliglustat.

Dataset Information

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model.

Publications

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model.

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