Aims: Dose adjustment for drugs eliminated by the kidneys generally assume a linear relationship between renal drug clearance (CL ) and glomerular filtration rate (GFR). This assumption may not hold for drugs that undergo extensive tubular secretion where nonlinearity in drug handling is expected. The aim of this study is to determine if renal drug study designs recommended by the European Medicines Agency (EMA) and Food and Drug Administration (FDA) could distinguish linear from nonlinear renal drug handling.
View Article and Find Full Text PDFEur J Clin Pharmacol
February 2019
Purpose: The intact nephron hypothesis (INH) states that impaired renal function results from a reduction in the number of complete (intact) nephrons. Under this model, renal drug clearance is assumed to be a linear function of glomerular filtration while tubular handling is ignored. The aims of this study were to systematically review published studies designed to test the INH and to assess the strength of the study designs used.
View Article and Find Full Text PDFBackground: Oral administration of drugs is convenient and shows good compliance but it can be affected by many factors in the gastrointestinal (GI) system. Consumption of food is one of the major factors affecting the GI system and consequently the absorption of drugs. The aim of this study was to develop a mechanistic GI absorption model for explaining the effect of food on fenofibrate pharmacokinetics (PK), focusing on the food type and calorie content.
View Article and Find Full Text PDFBackground: Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects.
Methods: In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution.