AAPS PharmSci最新文献

The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. The bias in the estimated coefficients (selection bias) was assessed. The model-building procedures performed similarly in the data sets explored. No major differences in the resulting covariate models were seen, and the predictive performances overlapped. Therefore, the choice of model-building procedure in these examples could be based on other aspects such as analyst- and computer-time efficiency. There was a tendency to selection bias in the estimates, although this was small relative to the overall variability in the estimates. The predictive performances of the stepwise models were also reasonably good. Thus, selection bias seems to be a minor problem in this typical PK covariate analysis.

Residues are composed of the parent drug and metabolites, and therefore interspecies comparisons must involve a consideration of comparative xenobiotic metabolism. The focus of this article will be the residue studies that are required to establish human food safety, and the interspecies pharmacokinetic differences and similarities that impact drug residues in animal- derived foods. To illustrate the factors that can complicate and assist these comparisons, 2 drugs will be examined in detail: ivermectin and fenbendazole. In addition, the activities of 2 US programs, the Food Animal Residue Avoidance Databank (FARAD) and the NRSP-7 (National Research Support Project Number 7) Minor Use Animal Drug Program will be presented, along with strategies that may be employed in the study of species differences.

Increasing resistance to antimicrobial agents is of growing concern to public health officials worldwide. The concern includes infections acquired in hospitals, community infections acquired in outpatient care settings, and resistant foodborne disease associated with drug use in food-producing animals. In the United States, a significant source of antimicrobial-resistant foodborne infections in humans is the acquisition of resistant bacteria originating from animals. The US Food and Drug Administration's (FDA's) goal in resolving the public health impact arising from the use of antimicrobial drugs in food-producing animals is to ensure that significant human antimicrobial therapies are not compromised or lost while providing for the safe use of antimicrobials in food animals. The FDA's approach to the problem is multipronged and innovative. The strategy includes revision of the pre-approval safety assessment for new animal drug applications, use of risk assessment to determine the human health effect resulting from the use of antimicrobials in food animals, robust monitoring for changes in susceptibilities among foodborne pathogens to drugs that are important both in human and veterinary medicine, research, and risk management.

Metronidazole was formulated in mucoadhesive vaginal tablets by directly compressing the natural cationic polymer chitosan, loosely cross-linked with glutaraldehyde, together with sodium alginate with or without microcrystalline cellulose (MCC). Sodium carboxymethylcellulose (CMC) was added to some of the formulations. The drug content in tablets was 20%. Drug dissolution rate studies from tablets were carried out in buffer pH 4.8 and distilled water. Swelling indices and adhesion forces were also measured for all formulations. The formula (FIII) containing 6% chitosan, 24% sodium alginate, 30% sodium CMC, and 20% MCC showed adequate release properties in both media and gave lower values of swelling index compared with the other examined formulations. FIII also proved to have good adhesion properties with minimum applied weights. Moreover, its release properties (% dissolution efficiency, DE) in buffer pH 4.8, as well as release mechanism (n values), were negligibly affected by aging. Thus, this formula may be considered a good candidate for vaginal mucoadhesive dosage forms.

The purpose of this study was to construct a pharmacokinetic (PK) model and to determine PK parameters of 2,3,5,6-tetramethylpyrazine (TMP) after application of TMP transdermal delivery system. Data were obtained in Sprague-Dawley (SD) rats following a single dose of TMP transdermal delivery system. Blood samples were obtained at 0, 0.25, 0.5, 1, 2, 4, 6, 16, and 24 hours after the transdermal application. In the brain level study, 18 SD rats were divided into 6 groups. Three SD rats before and after transdermal application were culled and sacrificed at each of the following time intervals: 2, 4, 6, 16, and 24 hours after the TMP-TTS application. TMP concentrations in plasma and brain tissues were determined using high performance liquid chromatography and data were fitted using a zero-order absorption and a first-order-elimination 3-compartment PK model. Fitted parameters included 2 volumes of distribution (V1, V2) and 2 elimination rate constants (k10, k20). The elimination half-life for TMP in plasma and brain was 26.5 and 31.2 minutes, respectively. The proposed PK model fit observed concentrations of TMP very well. This model is useful for predicting drug concentrations in plasma and brain and for assisting in the development of transdermal systems.