The effect of drugs with ion channel-blocking activity on the early embryonic rat heart.

This study investigated the effects of a range of pharmaceutical drugs with ion channel-blocking activity on the heart of gestation day 13 rat embryos in vitro. The general hypothesis was that the blockade of the I(Kr)/hERG channel, that is highly important for the normal functioning of the embryonic rat heart, would cause bradycardia and arrhythmia. Concomitant blockade of other channels was expected to modify the effects of hERG blockade. Fourteen drugs with varying degrees of specificity and affinity toward potassium, sodium, and calcium channels were tested over a range of concentrations. The rat embryos were maintained for 2 hr in culture, 1 hr to acclimatize, and 1 hr to test the effect of the drug. All the drugs caused a concentration-dependent bradycardia except nifedipine, which primarily caused a negative inotropic effect eventually stopping the heart. A number of drugs induced arrhythmias and these appeared to be related to either sodium channel blockade, which resulted in a double atrial beat for each ventricular beat, or I(Kr)/hERG blockade, which caused irregular atrial and ventricular beats. However, it is difficult to make a precise prediction of the effect of a drug on the embryonic heart just by looking at the polypharmacological action on ion channels. The results indicate that the use of the tested drugs during pregnancy could potentially damage the embryo by causing periods of hypoxia. In general, the effects on the embryonic heart were only seen at concentrations greater than those likely to occur with normal therapeutic dosing.

Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making.

In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.

A population pharmacokinetic model of gabapentin developed in nonparametric adaptive grid and nonlinear mixed effects modeling.

Gabapentin is used in analgesic treatment of neuropathic pain, and large interindividual variation has been observed in the pharmacokinetics (PK) of the drug. The aim of this study was to develop a population PK model for gabapentin appropriate for monitoring patients with neuropathic pain and for individualizing their dose regimens. Steady-state serum concentrations of gabapentin, distributed over a dosage interval, were obtained from 16 adult patients. Data were analyzed with an iterative 2-stage Bayesian and a nonparametric adaptive grid algorithm (NPAG) (USC*PACK) and with nonlinear mixed effects modeling (NONMEM). Compartmental population models for gabapentin PK were developed in NPAG and NONMEM using creatinine clearance and body weight as covariates. Bioavailability was included in the models as a function of dose by using a hyperbolic function derived from data previously reported in the literature. The mean population parameter estimates from the final NPAG model predicted individual serum concentrations reasonably well. The models developed in NONMEM provided additional information about the relevance of the various possible covariates and also allowed for further evaluation by simulation from the model. The population PK model may be utilized in the MM-USCPACK monitoring software (MM: multiple model dosage design) for predicting and achieving individually optimized steady-state serum concentrations of gabapentin.

Development of a population pharmacokinetic model for carbamazepine based on sparse therapeutic monitoring data from pediatric patients with epilepsy.

BACKGROUND: Population models can be important extensions of therapeutic drug monitoring (TDM), as they allow estimation of individual pharmacokinetic parameters based on a small number of measured drug concentrations. OBJECTIVE: This study used a Bayesian approach to explore the utility of routinely collected and sparse TDM data (1 sample per patient) for carbamazepine (CBZ) monotherapy in developing a population pharmacokinetic (PPK) model for CBZ in pediatric patients that would allow prediction of CBZ concentrations for both immediate- and controlled-release formulations. METHODS: Patient and TDM data were obtained from a pediatric neurology outpatient database. Data were analyzed using an iterative 2-stage Bayesian algorithm and a nonparametric adaptive grid algorithm. Models were compared by final log likelihood, mean error (ME) as a measure of bias, and root mean squared error (RMSE) as a measure of precision. RESULTS: Fifty-seven entries with data on CBZ monotherapy were identified from the database and used in the analysis (36 from males, 21 from females; mean [SD] age, 9.1 [4.4] years [range, 2-21 years]). Preliminary models estimating clearance (Cl) or the elimination rate constant (K(el)) gave good prediction of serum concentrations compared with measured serum concentrations, but estimates of Cl and K(el) were highly correlated with estimates of volume of distribution (V(d)). Different covariate models were then tested. The selected model had zero-order input and had age and body weight as covariates. Cl (L/h) was calculated as K(el) . V(d), where K(el) = [K(i) - (K(s) . age)] and V(d) = [V(i) + (V(s) . body weight)]. Median parameter estimates were V(i) (intercept) = 11.5 L (fixed); V(s) (slope) = 0.3957 L/kg (range, 0.01200-1.5730); K(i) (intercept) = 0.173 h(-1) (fixed); and K(s) (slope) = 0.004487 h(-1) . y(-1) (range, 0.0001800-0.02969). The fit was good for estimates of steady-state serum concentrations based on prior values (population median estimates) (R = 0.468; R(2) = 0.219) but was even better for predictions based on individual Bayesian posterior values (R(2) = 0.991), with little bias (ME = -0.079) and good precision (RMSE = 0.055). CONCLUSIONS: Based on the findings of this study, sparse TDM data can be used for PPK modeling of CBZ clearance in children with epilepsy, and these models can be used to predict Cl at steady state in pediatric patients. However, to estimate additional pharmacokinetic model parameters (eg, the absorption rate constant and V(d)), it would be necessary to combine sparse TDM data with additional well-timed samples. This would allow development of more informative PPK models that could be used as part of Bayesian dose-individualization strategies.