Clinical trials with antidepressant drugs often fail to detect drug effect, even with drugs that are known to be efficacious. In a previous publication, we showed that a model-based approach is... Show moreClinical trials with antidepressant drugs often fail to detect drug effect, even with drugs that are known to be efficacious. In a previous publication, we showed that a model-based approach is required to address some of the existing challenges in the design of clinical trial protocols. Here, we illustrate how the implementation of an interim analysis (IA) may help to identify studies that are headed for failure, early in the trial before completion of treatment. In contrast to traditional IA procedures, an adaptive Bayesian approach is proposed to optimize the timing of analysis and decision criteria for futility and efficacy, taking into account enrollment rate and treatment response at intermediate visits in the trial. Validation procedures involving re-enrollment of patients confirmed the performance of the method. Our findings reveal that optimization of the timing and decision criteria at the interim stage is critical for the accuracy of the conclusions about treatment efficacy or futility. Show less
Optimal ciclosporin A (CsA) exposure in kidney transplant recipients is difficult to attain because of variability in CsA pharmacokinetics. A better understanding of the variability in CsA exposure... Show moreOptimal ciclosporin A (CsA) exposure in kidney transplant recipients is difficult to attain because of variability in CsA pharmacokinetics. A better understanding of the variability in CsA exposure could be a good means of individualizing therapy. Specifically, genetic variability in genes involved in CsA metabolism could explain exposure differences. Therefore, this study is aimed at identifying a relationship between genetic polymorphisms and the variability in CsA exposure, while accounting for non-genetic sources of variability. De novo kidney transplant patients (n = 33) were treated with CsA for 1 year and extensive blood sampling was performed on multiple occasions throughout the year. The effects of the non-genetic covariates hematocrit, serum albumin concentration, cholesterol, demographics (i.e., body weight), CsA dose interval, prednisolone dose and genetic polymorphisms in genes encoding ABCB1, CYP3A4, CYP3A5, and PXR on CsA pharmacokinetics were studied using non-linear mixed effect modeling. The pharmacokinetics of CsA were described by a two-compartment disposition model with delayed absorption. Body weight was identified as the most important covariate and explained 35% of the random inter-individual variability in CsA clearance. Moreover, concurrent prednisolone use at a dosage of 20 mg/day or higher was associated with a 22% higher clearance of CsA, hence lower CsA exposure. In contrast, no considerable genotype effects (i.e., greater than 30-50%) on CsA clearance were found for the selected genes. It appears that the selected genetic markers explain variability in CsA exposure insufficiently to be of clinical relevance. Therefore, therapeutic drug monitoring is still required to optimize CsA exposure after administration of individualized doses based on body weight and, as this study suggests, co-administration of prednisolone. Show less
