An integrated model for the regulation of glucose and insulin concentrations following intravenous glucose provocations in healthy volunteers and type 2 diabetic patients was developed. Data from 72 individuals were included. Total glucose, labeled glucose, and insulin concentrations were determined. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Integrated models for glucose, labeled glucose, and insulin were developed. Control mechanisms for regulation of glucose production, insulin secretion, and glucose uptake were incorporated. Physiologically relevant differences between healthy volunteers and patients were identified in the regulation of glucose production, elimination rate of glucose, and secretion of insulin. The model was able to describe the insulin and glucose profiles well and also showed a good ability to simulate data. The features of the present model are likely to be of interest for analysis of data collected in antidiabetic drug development and for optimization of study design.
This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters ⅐ h ؊1 , while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.
ObjectivesMycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states.MethodsThe natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time–kill curves from log-phase (0.25–16 mg/L) and stationary-phase (0.5–64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure–response relationships.ResultsThe final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R2 = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively.ConclusionsThe model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro.
Accumulating evidence suggests that increasing doses of rifampicin may shorten tuberculosis treatment. The PanACEA HIGHRIF1 trial assessed safety, pharmacokinetics, and antimycobacterial activity of rifampicin at doses up to 40 mg/kg. Eighty‐three pulmonary tuberculosis patients received 10, 20, 25, 30, 35, or 40 mg/kg rifampicin daily over 2 weeks, supplemented with standard doses of isoniazid, pyrazinamide, and ethambutol in the second week. This study aimed at characterizing rifampicin pharmacokinetics observed in HIGHRIF1 using nonlinear mixed effects modeling. The final population pharmacokinetic model included an enzyme turnover model accounting for time‐dependent elimination due to autoinduction, concentration‐dependent clearance, and dose‐dependent bioavailability. The relationship between clearance and concentration was characterized by a Michaelis–Menten relationship. The relationship between bioavailability and dose was described using an Emax relationship. The model will be key in determining exposure–response relationships for rifampicin and should be considered when designing future trials and when treating future patients with high‐dose rifampicin.
An integrated model for the glucose-insulin system describing oral glucose tolerance test data was developed, extending on a previously introduced model for intravenous glucose provocations. Model extensions comprised the description of glucose absorption by a chain of transit compartments with a mean transit time of 35 minutes, a bioavailability of 80%, and a representation of the incretin effect, expressed as a direct effect of the glucose absorption rate on insulin secretion. The ability of the model to predict the incretin effect was assessed by simulating the observed difference in insulin response following an oral glucose tolerance test compared with an isoglycemic glucose infusion mimicking an oral glucose tolerance test profile. The extension of the integrated glucose-insulin model to gain information from oral glucose tolerance test data considerably expands its range of applications because the oral glucose tolerance test is one of the most common glucose challenge experiments for assessing the efficacy of hypoglycemic agents in clinical drug development.
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