A Physiologically Based Pharmacokinetic Model of Voriconazole Integrating Time-Dependent Inhibition of CYP3A4, Genetic Polymorphisms of CYP2C19 and Predictions of Drug–Drug Interactions
Background Voriconazole, a first-line antifungal drug, exhibits nonlinear pharmacokinetics (PK), together with large interindividual variability but a narrow therapeutic range, and markedly inhibits cytochrome P450 (CYP) 3A4 in vivo. This causes difficulties in selecting appropriate dosing regimens of voriconazole and coadministered CYP3A4 substrates. Objective This study aimed to investigate the metabolism of voriconazole in detail to better understand dose- and time-dependent alterations in the PK of the drug, to provide the model basis for safe and effective use according to CYP2C19 genotype, and to assess the potential of voriconazole to cause drug–drug interactions (DDIs) with CYP3A4 substrates in more detail. Methods In vitro assays were carried out to explore time-dependent inhibition (TDI) of CYP3A4 by voriconazole. These results were combined with 93 published concentration–time datasets of voriconazole from clinical trials in healthy volunteers to develop a whole-body physiologically based PK (PBPK) model in PK-$ Sim^{®} $. The model was evaluated quantitatively with the predicted/observed ratio of the area under the plasma concentration–time curve (AUC), maximum concentration (Cmax), and trough concentrations for multiple dosings (Ctrough), the geometric mean fold error, as well as visually with the comparison of predicted with observed concentration–time datasets over the full range of recommended intravenous and oral dosing regimens. Results The result of the half maximal inhibitory concentration ($ IC_{50} $) shift assay indicated that voriconazole causes TDI of CYP3A4. The PBPK model evaluation demonstrated a good performance of the model, with 71% of predicted/observed aggregate AUC ratios and all aggregate Cmax ratios from 28 evaluation datasets being within a 0.5- to 2-fold range. For those studies reporting CYP2C19 genotype, 89% of aggregate AUC ratios and all aggregate Cmax ratios were inside a 0.5- to 2-fold range of 44 test datasets. The results of model-based simulations showed that the standard oral maintenance dose of voriconazole 200 mg twice daily would be sufficient for CYP2C19 intermediate metabolizers (IMs; *1/*2, *1/*3, *2/*17, and *2/*2/*17) to reach the tentative therapeutic range of > 1–2 mg/L to < 5–6 mg/L for Ctrough, while 400 mg twice daily might be more suitable for rapid metabolizers (RMs; *1/*17, *17/*17) and normal metabolizers (NMs; *1/*1). When the model was integrated with independently developed CYP3A4 substrate models (midazolam and alfentanil), the observed AUC change of substrates by voriconazole was inside the 90% confidence interval of the predicted AUC change, indicating that CYP3A4 inhibition was appropriately incorporated into the voriconazole model. Conclusions Both the in vitro assay and model-based simulations support TDI of CYP3A4 by voriconazole as a pivotal characteristic of this drug’s PK. The PBPK model developed here could support individual dose adjustment of voriconazole according to genetic polymorphisms of CYP2C19, and DDI risk management. The applicability of modeling results for patients remains to be confirmed in future studies..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:59 |
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| Enthalten in: |
Clinical pharmacokinetics - 59(2019), 6 vom: 19. Dez., Seite 781-808 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Xia [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Anmerkungen: |
© Springer Nature Switzerland AG 2019 |
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| doi: |
10.1007/s40262-019-00856-z |
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| 520 | |a Background Voriconazole, a first-line antifungal drug, exhibits nonlinear pharmacokinetics (PK), together with large interindividual variability but a narrow therapeutic range, and markedly inhibits cytochrome P450 (CYP) 3A4 in vivo. This causes difficulties in selecting appropriate dosing regimens of voriconazole and coadministered CYP3A4 substrates. Objective This study aimed to investigate the metabolism of voriconazole in detail to better understand dose- and time-dependent alterations in the PK of the drug, to provide the model basis for safe and effective use according to CYP2C19 genotype, and to assess the potential of voriconazole to cause drug–drug interactions (DDIs) with CYP3A4 substrates in more detail. Methods In vitro assays were carried out to explore time-dependent inhibition (TDI) of CYP3A4 by voriconazole. These results were combined with 93 published concentration–time datasets of voriconazole from clinical trials in healthy volunteers to develop a whole-body physiologically based PK (PBPK) model in PK-$ Sim^{®} $. The model was evaluated quantitatively with the predicted/observed ratio of the area under the plasma concentration–time curve (AUC), maximum concentration (Cmax), and trough concentrations for multiple dosings (Ctrough), the geometric mean fold error, as well as visually with the comparison of predicted with observed concentration–time datasets over the full range of recommended intravenous and oral dosing regimens. Results The result of the half maximal inhibitory concentration ($ IC_{50} $) shift assay indicated that voriconazole causes TDI of CYP3A4. The PBPK model evaluation demonstrated a good performance of the model, with 71% of predicted/observed aggregate AUC ratios and all aggregate Cmax ratios from 28 evaluation datasets being within a 0.5- to 2-fold range. For those studies reporting CYP2C19 genotype, 89% of aggregate AUC ratios and all aggregate Cmax ratios were inside a 0.5- to 2-fold range of 44 test datasets. The results of model-based simulations showed that the standard oral maintenance dose of voriconazole 200 mg twice daily would be sufficient for CYP2C19 intermediate metabolizers (IMs; *1/*2, *1/*3, *2/*17, and *2/*2/*17) to reach the tentative therapeutic range of > 1–2 mg/L to < 5–6 mg/L for Ctrough, while 400 mg twice daily might be more suitable for rapid metabolizers (RMs; *1/*17, *17/*17) and normal metabolizers (NMs; *1/*1). When the model was integrated with independently developed CYP3A4 substrate models (midazolam and alfentanil), the observed AUC change of substrates by voriconazole was inside the 90% confidence interval of the predicted AUC change, indicating that CYP3A4 inhibition was appropriately incorporated into the voriconazole model. Conclusions Both the in vitro assay and model-based simulations support TDI of CYP3A4 by voriconazole as a pivotal characteristic of this drug’s PK. The PBPK model developed here could support individual dose adjustment of voriconazole according to genetic polymorphisms of CYP2C19, and DDI risk management. The applicability of modeling results for patients remains to be confirmed in future studies. | ||
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