Bayesian knowledge integration for an in vitro-in vivo correlation model.

The primary goal of "in vitro-in vivo correlation" (IVIVC) is the reliable prediction of the in vivo serum concentration-time course, based on the in vitro drug dissolution or release profiles. IVIVC methods are particularly appropriate for formulations that are released over an extended period of time or with a lag in absorption and may support approving a change in formulation of a drug without additional bioequivalence trials in human subjects. Most of the current IVIVC models are assessed using frequentist methods, such as linear regression, based on averaged data and entail complex and potentially unstable mathematical deconvolution. The proposed IVIVC approach includes (a) a nonlinear-mixed effects model for the in vitro release data; (b) a population pharmacokinetic (PK) compartment model for the in vivo immediate release (IR) data; and (c) a system of ordinal differential equations (ODEs), containing the submodels (a) and (b), which approximates and predicts the in vivo controlled release (CR) data. The innovation in this paper consists of splitting the parameter space between submodels (a) and (b) versus (c). Subsequently, the uncertainty on these parameters is accounted for using a Bayesian framework, that is estimates from the first two submodels serve as priors for the Bayesian hierarchical third submodel. As such, the Bayesian method explained ensures a natural integration and transfer of knowledge between various sources of information, balancing possible differences in sample size and parameter uncertainty of in vitro and in vivo studies. Consequently, it is a very flexible approach yielding results for a broad range of data situations. The application of the method is demonstrated for a transdermal patch (TD).

Pharmacological Treatment of Arterial Hypertension in Children and Adolescents: A Network Meta-Analysis.

Pharmacological treatment is indicated in children and adolescents with hypertension unresponsive to lifestyle modifications, but there is not enough evidence to recommend 1 class of antihypertensive drugs over others. We performed a network meta-analysis to compare the results of available randomized clinical trials on pharmacological treatment of pediatric hypertension. From a total of 554 potentially relevant studies, 13 randomized placebo-controlled clinical trials enrolling ≥50 patients and a follow-up ≥4 weeks were included. The reduction of systolic blood pressure (SBP) and diastolic BP (DBP) after treatment were the coprimary end points. A total of 2378 pediatric patients, with a median age of 12 years, were included in the analysis. After a median follow-up of 35 days, lisinopril and enalapril were found to be superior to placebo in reducing SBP and DBP, whereas only for DBP, losartan was found to be superior to placebo and lisinopril and enalapril were found to be superior to eplerenone. Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers were associated with a greater SBP and DBP reduction compared with placebo, likewise the mineralocorticoid receptor antagonist was inferior to angiotensin-converting enzyme inhibitors in DBP reduction. The analysis was adjusted for study-level mean age, percentage of women, mean baseline blood pressure, and mean weight, only the latter significantly affected DBP reduction. According to the present analysis, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers could represent the best choice as antihypertensive treatment for pediatric hypertension. However, because of the paucity of available data for the other classes of antihypertensive drugs, definitive conclusions are not allowed and further randomized controlled trials are warranted.