Model-based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

Genetics of Nevirapine Metabolic Pathways at Steady State in HIV-Infected Cambodians.

Nevirapine is metabolized by several hepatic cytochrome P450 (CYP) isoforms to generate four primary hydroxylated metabolites: 2-hydroxynevirapine, 3-hydroxynevirapine, 8-hydroxynevirapine, and 12-hydroxynevirapine. The present study characterized associations between genetic polymorphisms and metabolite ratios in HIV-infected Cambodians. We demonstrate associations between CYP2B6 polymorphisms and metabolite ratios for both 3-hydroxynevirapine and 8-hydroxynevirapine, suggesting involvement of CYP2B6 in generating these metabolites.

Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments.

Longitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to "treatment" or "placebo," we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias.

Staphylococcus aureus CC30 Lineage and Absence of sed,j,r-Harboring Plasmid Predict Embolism in Infective Endocarditis.

Staphylococcus aureus induces severe infective endocarditis (IE) where embolic complications are a major cause of death. Risk factors for embolism have been reported such as a younger age or larger IE vegetations, while methicillin resistance conferred by the mecA gene appeared as a protective factor. It is unclear, however, whether embolism is influenced by other S. aureus characteristics such as clonal complex (CC) or virulence pattern. We examined clinical and microbiological predictors of embolism in a prospective multicentric cohort of 98 French patients with monomicrobial S. aureus IE. The genomic contents of causative isolates were characterized using DNA array. To preserve statistical power, genotypic predictors were restricted to CC, secreted virulence factors and virulence regulators. Multivariate regularized logistic regression identified three independent predictors of embolism. Patients at higher risk were younger than the cohort median age of 62.5 y (adjusted odds ratio [OR] 0.14; 95% confidence interval [CI] 0.05-0.36). S. aureus characteristics predicting embolism were a CC30 genetic background (adjusted OR 9.734; 95% CI 1.53-192.8) and the absence of pIB485-like plasmid-borne enterotoxin-encoding genes sed, sej, and ser (sedjr; adjusted OR 0.07; 95% CI 0.004-0.457). CC30 S. aureus has been repeatedly reported to exhibit enhanced fitness in bloodstream infections, which might impact its ability to cause embolism. sedjr-encoded enterotoxins, whose superantigenic activity is unlikely to protect against embolism, possibly acted as a proxy to others genes of the pIB485-like plasmid found in genetically unrelated isolates from mostly embolism-free patients. mecA did not independently predict embolism but was strongly associated with sedjr. This mecA-sedjr association might have driven previous reports of a negative association of mecA and embolism. Collectively, our results suggest that the influence of S. aureus genotypic features on the risk of embolism may be stronger than previously suspected and independent of clinical risk factors.

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates.

Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H1), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H1. We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.