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BackgroundOral 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.MethodsClinical data from a fenofibrate PK study involving three different conditions (fasting, standard meals and high-fat meals) were used. The model was developed by nonlinear mixed effect modeling method. Both linear and nonlinear effects were evaluated to explain the impact of food intake on drug absorption. Similarly, to explain changes in gastric emptying time for the drug due to food effects was evaluated.ResultsThe gastric emptying rate increased by 61.7% during the first 6.94 h after food consumption. Increased calories in the duodenum increased the absorption rate constant of the drug in fed conditions (standard meal = 16.5%, high-fat meal = 21.8%) compared with fasted condition. The final model displayed good prediction power and precision.ConclusionsA mechanistic GI absorption model for quantitatively evaluating the effects of food on fenofibrate absorption was successfully developed, and acceptable parameters were obtained. The mechanism-based PK model of fenofibrate can quantify the effects of food on drug absorption by food type and calorie content.Electronic supplementary materialThe online version of this article (10.1186/s40360-018-0194-5) contains supplementary material, which is available to authorized users.

BackgroundExploratory 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.MethodsIn 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. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results.ResultsThe rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset.ConclusionsThe classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.Electronic supplementary materialThe online version of this article (10.1186/s12874-017-0427-0) contains supplementary material, which is available to authorized users.

Aims Dose adjustment for drugs eliminated by the kidneys generally assume a linear relationship between renal drug clearance (CLR) 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. Methods In this simulation and estimation study, the study designs based on the EMA and FDA guidelines for Phase I renal drug studies were evaluated for their ability to discriminate a linear from a nonlinear relationship between CLR and GFR. The number of subjects for each simulated study ranged from 4 to 960. Power, relative standard error and bias were calculated. Results Study designs under the EMA and FDA guidelines required ≥8 and ≥48 subjects, respectively, to achieve ≥80% power to discriminate a linear from nonlinear relationship between CLR and GFR. The relative standard error of estimated parameters were 13–37 and 17–44% for the designs with 24 subjects under the EMA and FDA guidelines, respectively. The bias in parameter estimates under the EMA designs were not evident, however, they were biased (13–21%) under the FDA designs. Conclusion The EMA design was found to require fewer subjects (n = 8) compared to the FDA (n = 48) to discriminate linear from nonlinear drug renal handling at ≥80% study power while both the designs perform poorly for the parameter precision.

This research explored the intact nephron hypothesis (INH) as a model for metformin dosing in patients with chronic kidney disease (CKD). The INH assumes that glomerular filtration rate (GFR) will account for all kidney drug handling even for drugs eliminated by tubular secretion like metformin. We conducted two studies: (1) a regression analysis to explore the relationship between metformin clearance and eGFR metrics, and (2) a joint population pharmacokinetic analysis to test the relationship between metformin renal clearance and gentamicin clearance. The relationship between metformin renal clearance and eGFR metrics and gentamicin clearance was found to be linear, suggesting that a proportional dose reduction based on GFR in patients with CKD is reasonable.

The objective of this study was to describe the population pharmacokinetics (PK) of mosapride under fasting and fed conditions. A single 5-mg oral dose of mosapride was administered to fasted (n = 15) and fed (n = 12) beagle dogs. Plasma concentrations of mosapride were subsequently measured by liquid chromatography-tandem mass spectrometry. Data were analyzed using modeling approaches with the NONMEM 7.2 software. A one-compartment open PK model utilizing model event time (MTIME) with first-order absorption and first-order elimination was found to be more appropriate than all other PK models tested. The absorption rate constants of mosapride were significantly decreased under fed conditions, compared to fasting conditions. The observed bootstrap medians of PK parameters were generally consistent with the corresponding population mean estimates. Furthermore, with the exception of some mosapride concentrations, most of observed data fell into the range of the 5th and 95th percentiles of the simulated values. Overall, the final model was able to describe the observed mosapride concentrations reasonably well. These findings suggest that food intake affects both the rate and extent of absorption of mosapride and that the pharmacological effect of mosapride can differ significantly depending on food intake.

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