Population pharmacokinetic meta-analysis with efavirenz
- PMID: 12698988
- DOI: 10.5414/cpp40507
Population pharmacokinetic meta-analysis with efavirenz
Abstract
A population-based pharmacokinetic (PK) model has been developed for efavirenz based on 16 phase I studies. The combined data set consisted of 334 healthy volunteers, 2,907 efavirenz dose administrations and 9,342 measured plasma concentrations across a range of doses from 100-600 mg. The pharmacokinetic structural model was a 2-compartment model with first-order absorption with differentiation between single- and multiple-dose exposure to account for known hepatic cytochrome P450 induction of efavirenz metabolism. Model-building was performed on the index data set (66% of the total database), as a data-splitting technique was used to validate the final model using NONMEM. The final model confirmed the appropriateness of separate clearance terms for single and multiple dose administration (2.65 versus 10.2 l/h, respectively). Clearance increased with dose and frequency of administration. A lower clearance was predicted in Asians and Blacks relative to Caucasians. A slightly lower clearance was observed in females relative to males (9.08 compared to 10.2 l/h in males) and interactions on clearance due to co-administration of fluconazole, ritonavir, rifampin, indinavir and azithromycin were identified. The magnitudes of these effects were small and did not suggest dose adjustment in the various subpopulations. With little exception, these results agree with the findings from the non-compartmental analyses. The residual variability was 21% CV and the intersubject variation in CL/F and V/F was 48 and 85%, respectively. The phase I meta-analysis was able to substantiate the pharmacokinetic characteristics of efavirenz derived from the composite of individual well-defined studies. The model was deemed adequate for subsequent evaluation in HIV-infected patients. Covariates and outlier classes identified in this phase I meta-analysis were similarly identified in subsequent analyses of patient data.
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