The application of Bayesian forecasting to explore the effects of sex and high-fat diet on the pharmacokinetics of ropivacaine in the rat.

Bayesian forecasting is commonly applied as part of therapeutic drug monitoring to obtain individual estimates of pharmacokinetic parameters in patients. Here its utility was explored in a preclinical study involving ropivacaine, in which sparse blood sampling data was available in the rat. Initially sample-population estimates of parameters were obtained by injecting male cannulated male Sprague-Dawley rats subcutaneously with ropivacaine HCl. Blood samples were serially drawn from each rat for 12 h after the dose (rich sampling); the concentrations were used with compartmental analysis to optimize model selection and obtain mean and variances of pharmacokinetic parameters. Two additional single doses, spaced by 5 days, were injected, each followed by 1 to 3 sparse blood draws. Other sparsely sampled age-matched groups of male and female rats given standard diet, and a group of males given high-fat diet, were dosed. Bayesian forecasting was conducted for each of these sparsely sampled rats to estimate pharmacokinetic parameters. Plasma was assayed using liquid-chromatographic method using mass spectrometry. For validation, the Bayesian parameter forecasts were compared to those using nonlinear mixed-effects modelling (NLMEM). Ropivacaine had a high clearance compared to hepatic blood flow, and a large volume of distribution. Excellent correlations were present between observed and estimated plasma concentrations using Bayesian forecasting, as was the relationship between those estimates and those obtained from NLMEM. The male rats given high-fat diet had a significant decrease in the weight-normalized clearance of ropivacaine, and female rats had a slower absorption rate. The effects were also identified using NLMEM. Bayesian forecasting has applicability in estimating pharmacokinetic properties of drugs in preclinical studies.