Daniel Röshammar

A novel polymorphism in ABCB1 gene, CYP2B6*6 and sex predict single-dose efavirenz population pharmacokinetics in Ugandans

AIMS Efavirenz 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. METHODS Efavirenz 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. RESULTS Apparent 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. CONCLUSIONS The 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.

In silico prediction of efavirenz and rifampicin drug-drug interaction considering weight and CYP2B6 phenotype

AIMS This study aimed to test whether a pharmacokinetic simulation model could extrapolate nonclinical drug data to predict human efavirenz exposure after single and continuous dosing as well as the effects of concomitant rifampicin and further to evaluate the weight-based dosage recommendations used to counteract the rifampicin-efavirenz interaction. METHODS Efavirenz pharmacokinetics were simulated using a physiologically based pharmacokinetic model implemented in the Simcyp™ population-based simulator. Physicochemical and metabolism data obtained from the literature were used as input for prediction of pharmacokinetic parameters. The model was used to simulate the effects of rifampicin on efavirenz pharmacokinetics in 400 virtual patients, taking into account bodyweight and CYP2B6 phenotype. RESULTS Apart from the absorption phase, the simulation model predicted efavirenz concentration-time profiles reasonably well, with close agreement with clinical data. The simulated effects of rifampicin co-administration on efavirenz treatment showed only a minor decrease of 16% (95% confidence interval 13-19) in efavirenz area under the concentration-time curve, of the same magnitude as what has been clinically observed (22%). Efavirenz exposure depended on CYP2B6 phenotype and bodyweight. Increasing the efavirenz dose during concomitant rifampicin was predicted to be most successful in patients over 50 kg regardless of CYP2B6 status. CONCLUSIONS Our findings, although based on a simulation approach using limited in vitro data, support the current recommendations for using a 50 kg bodyweight cut-off for efavirenz dose increment when co-treating with rifampicin.

HIV/AIDS patients display lower relative bioavailability of efavirenz than healthy subjects.

AbstractBackground: Pharmacokinetic studies of antiretroviral drugs are often conducted in adult healthy subjects, and the results are extrapolated to HIV/AIDS patients. HIV/AIDS, however, is known to cause morphological and physiological changes that may alter the pharmacokinetics of antiretroviral drugs. We examined the effect of HIV/AIDS on the pharmacokinetics of efavirenz in Ugandans. Methods: After a first oral dose of efavirenz 600 mg in treatment-naïve HIV-infected patients, blood samples were collected at nine time points up to 24 hours. The plasma-concentration time data from these patients were merged with previously reported data from adult healthy subjects. Population pharmacokinetic models were fitted to the data, using NONMEM VI software. Covariate analyses were performed to estimate the effects of HIV/AIDS disease, demographic characteristics (sex, bodyweight, age), biochemical variables (serum creatinine, urea, alanine aminotransferase) and pharmacogenetic variation in cytochrome P450 (CYP) 2B6, CYP3A5 and adenosine triphosphate-binding cassette, sub-family B, member 1 (ABCB1) on the population pharmacokinetic parameters. Results: Efavirenz plasma concentration-time data obtained from 29 HIV-1-infected, treatment-naïve patients were merged with previously reported data from 32 adult healthy subjects. The model identified sex and HIV/AIDS disease as statistically significant categorical predictors of efavirenz pharmacokinetics. Females were predicted to have a 2-fold higher volume of distribution of the peripheral compartment after oral administration (V2/F) than males (95% CI 1.53,2.63), while HIV/AIDS patients were found to have 30% lower relative bioavailability (95% CI 18.7, 40.7) than healthy subjects. The increased V2/F in females resulted in a 2-fold longer elimination half-life than in males. Conclusion: On the basis of the findings of this analysis, we conclude that, apart from bodyweight-based differences, both HIV/AIDS disease and sex affect efavirenz pharmacokinetics in Ugandans. HIV/AIDS disease is associated with reduced relative bioavailability of efavirenz. We recommend that findings from healthy subject studies be confirmed in HIV/AIDS patients and that caution be applied in direct extrapolation of exposure data to the target patient population.

External Validation of the Bilirubin-Atazanavir Nomogram for Assessment of Atazanavir Plasma Exposure in HIV-1-Infected Patients

Atazanavir increases plasma bilirubin levels in a concentration-dependent manner. Due to less costly and readily available assays, bilirubin has been proposed as a marker of atazanavir exposure. In this work, a previously developed nomogram for detection of suboptimal atazanavir exposure is validated against external patient populations. The bilirubin nomogram was validated against 311 matching bilirubin and atazanavir samples from 166 HIV-1-infected Norwegian, French, and Italian patients on a ritonavir-boosted regimen. In addition, the nomogram was evaluated in 56 Italian patients on an unboosted regimen. The predictive properties of the nomogram were validated against observed atazanavir plasma concentrations. The use of the nomogram to detect non-adherence was also investigated by simulation. The bilirubin nomogram predicted suboptimal exposure in the patient populations on a ritonavir-boosted regimen with a negative predictive value of 97% (95% CI 95–100). The bilirubin nomogram and monitoring of atazanavir concentrations had similar predictive properties for detecting non-adherence based on simulations. Although both methods performed adequately during a period of non-adherence, they had lower predictive power to detect past non-adherence episodes. Using the bilirubin nomogram for detection of suboptimal atazanavir exposure in patients on a ritonavir-boosted regimen is a rapid and cost-effective alternative to routine measurements of the actual atazanavir exposure in plasma. Its application may be useful in clinical settings if atazanavir concentrations are not available.

Exposure‐Response Analyses Supporting Ticagrelor Dosing Recommendation in Patients With Prior Myocardial Infarction

The relationships between drug exposure and the composite risk of cardiovascular (CV) death, myocardial infarction (MI), and stroke as well as the risk of TIMI major bleeding were estimated following long‐term treatment with ticagrelor 60 or 90 mg twice daily in 20,942 patients with prior MI. These analyses support the primary reported efficacy and safety evaluations by showing that there were clear separations from placebo early in treatment with both doses, regardless of ticagrelor exposure, for both endpoints. In addition, the exposure‐response analyses provided new insight into the contribution of individual exposure levels, rather than dose, as a predictor of events and accounted for differences in the baseline risk between patients. The predicted risks of CV death/MI/stroke were similar despite an increase in the median predicted ticagrelor average steady‐state concentration from 606 nmol/L with ticagrelor 60 mg to 998 nmol/L with ticagrelor 90 mg (hazard ratios vs placebo of 0.83 and 0.81, respectively). The corresponding predicted risk of TIMI major bleeding slightly increased (hazard ratios vs placebo of 2.4 and 2.6, respectively). Apart from Japanese patients, showing a lower risk of CV death/MI/stroke, the response to ticagrelor was consistent across the study population, as supported by the combination of relatively flat exposure‐response relationships in the studied exposure range, similar sensitivity to ticagrelor exposure, and small exposure differences. Consequently, the present analyses support the selection of the 60‐mg dose for all demographic subgroups of patients studied.

Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection

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.