The CYP2B6*6 allele occurs at a high frequency in people of African origin and is associated with high efavirenz concentrations. Simulations indicate that an a priori 35% dose reduction in homozygous CYP2B6*6 patients would maintain drug exposure within the therapeutic range in this group of patients. Our preliminary results suggest the conduct of a prospective clinical dose optimization study to evaluate the utility of genotype-driven dose adjustment in this population.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Efavirenz is metabolized by highly polymorphic enzymes, CYP2B6 and CYP3A. The effect of the different variant alleles on efavirenz population pharmacokinetics has not yet been fully explored.• CYP2B6*6 influences efavirenz steady-state pharmacokinetics. Together with sex it explains 11% of the between-subject variability in apparent oral clearance, but predictions could potentially be improved if additional alleles causing reduced drug metabolism were identified.• ABCB1 (3435C→T) may have effect on efavirenz single-dose and steady-state pharmacokinetics. WHAT THIS STUDY ADDS• A new polymorphism in ABCB1 gene (rs3842) and CYP2B6*11 in addition to sex and CYP2B6*6 genotype predict efavirenz single-dose pharmacokinetics.• A combined population pharmacogenetic/pharmacokinetic modelling approach allows determination and simulation of determinant factors for efavirenz single-dose pharmacokinetics based on data on gender, biochemical variables and genetic factors in relevant genes (a total of 30 SNPs in CYP2B6, ABCB1 and CYP3A4 genes) in Ugandan population. AIMSEfavirenz exhibits pharmacokinetic variability causing varied clinical response. The aim was to develop an integrated population pharmacokinetic/pharmacogenetic model and investigate the impact of genetic variations, sex, demographic and biochemical variables on single-dose efavirenz pharmacokinetics among Ugandan subjects, using NONMEM. METHODSEfavirenz plasma concentrations (n = 402) from 121 healthy subjects were quantified by high-performance liquid chromatography. Subjects were genotyped for 30 single nucleotide polymorphisms (SNPs), of which six were novel SNPs in CYP2B6, CYP3A5 and ABCB1. The efavirenz pharmacokinetics was described by a two-compartment model with zero-followed by first-order absorption. RESULTSApparent oral clearance (95% confidence interval) was 4 l h l -1 (3.5, 4.5) in extensive metabolizers. In the final model, incorporating multiple covariates, statistical significance was found only for CYP2B6*6 and CYP2B6*11 on apparent oral clearance as well as ABCB1 (rs3842) on the relative bioavailability. Subjects homozygous for CYP2B6*6 (G516T, A785G) and *11 displayed 21 and 20% lower apparent oral clearance, respectively. Efavirenz relative bioavailability was 26% higher in subjects homozygous for ABCB1 (rs3842). The apparent peripheral volume of distribution was twofold higher in women compared with men. CONCLUSIONSThe model identified the four factors CYP2B6*6, CYP2B6*11, a novel variant allele in ABCB1 (rs3842) and sex as major predictors of efavirenz plasma exposure in a healthy Ugandan population after single-dose administration. Use of mixed-effects modelling allowed the analysis and integration of multiple pharmacogenetic and demographic covariates in a pharmacokinetic population model.

The study aimed to characterize the population pharmacokinetics of amodiaquine (AQ) and its major metabolite N-desethylamodiaquine (N-DEAQ), and to assess the correlation between exposure to N-DEAQ and treatment outcome. Blood samples from children in two studies in Zanzibar and one in Papua New Guinea were included in the pharmacokinetic analysis (n = 86). The children had been treated with AQ in combination with artesunate or sulphadoxine-pyrimethamine. The population pharmacokinetics of AQ and N-DEAQ were modeled using the non-linear mixed effects approach as implemented in NONMEM. Bayesian post-hoc estimates of individual pharmacokinetic parameters were used to generate individual profiles of N-DEAQ exposure. The correlation between N-DEAQ exposure and effect was studied in 212 patients and modeled with logistic regression in NONMEM. The pharmacokinetics of AQ and N-DEAQ were best described by two parallel two-compartment models with a central and a peripheral compartment for each compound. The systemic exposure to AQ was low in comparison to N-DEAQ. The t(1/2 lambda) of N-DEAQ ranged from 3 days to 12 days. There was a statistically significant, yet weak, association between N-DEAQ concentration on day 7 and treatment outcome. The age-based dosing schedule currently recommended in Zanzibar appeared to result in inadequate exposure to N-DEAQ in many patients.

Piperaquine pharmacokinetics after repeated oral doses were characterized by multiple concentration peaks and multiphasic disposition, resulting in a long terminal half-life. Sustained exposure to the drug after treatment should be taken into account when designing future clinical studies, e.g. duration of follow-up, and may also drive resistance development in areas of high malaria transmission.

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose–response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-017-9550-0) contains supplementary material, which is available to authorized users.