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Population pharmacokinetic (pop-PK) models constructed for model-informed precision dosing often have limited utility due to the low number of patients recruited. To augment such models, an approach is presented for generating fully artificial quasi-models which can be employed to make individual estimates of pharmacokinetic parameters. Based on 72 concentrations obtained in 12 patients, one- and two-compartment pop-PK models with or without creatinine clearance as a covariate were generated for piperacillin using the nonparametric adaptive grid algorithm. Thirty quasi-models were subsequently generated for each model type, and nonparametric maximum a posteriori probability Bayesian estimates were established for each patient. A significant difference in performance was found between one- and two-compartment models. Acceptable agreement was found between predicted and observed piperacillin concentrations, and between the estimates of the random-effect pharmacokinetic variables obtained using the so-called support points of the pop-PK models or the quasi-models as priors. The mean squared errors of the predictions made using the quasi-models were similar to, or even considerably lower than those obtained when employing the pop-PK models. Conclusion: fully artificial nonparametric quasi-models can efficiently augment pop-PK models containing few support points, to make individual pharmacokinetic estimates in the clinical setting.

Abstract The inadequate adherence of patients whose hyperlipidemia is treated with atorvastatin (ATR) to medical instructions presents a serious health risk. Our aim was to develop a flexible approach based on therapeutic drug monitoring (TDM), nonparametric population pharmacokinetic modeling, and Monte Carlo simulation to differentiate adherent patients from partially and nonadherent individuals in a nonrandomized, unicentric, observational study. Sixty‐five subjects were enrolled. Nonparametric, mixed‐effect population pharmacokinetic models of the sums of atorvastatin and atorvastatin lactone concentrations (ATR+ATRL) and of the concentrations of the acid and lactone forms of ATR and its 2‐ and 4‐hydroxylated pharmacologically active metabolites (ATR+MET) were elaborated by including the TDM results obtained in 128 samples collected from thirty‐nine subjects. Monte Carlo simulation was performed based on the elaborated models to establish the probabilities of attaining a specific ATR+ATRL or ATR+MET concentration in the range of 0.002–10 nmol (mg dose)−1 L−1 at 1–24 h postdose by adherent, partially adherent, and nonadherent patients. The results of the simulations were processed to allow the estimation of the adherence of further 26 subjects who were phlebotomized at sampling times of 2–20 h postdose by calculating the probabilities of attaining the ATR+ATRL and ATR+MET concentrations measured in these subjects in adherent, partially adherent, and nonadherent individuals. The best predictive values of the estimates of adherence could be obtained with sampling at early sampling times. 61.54% and 38.46% of subjects in the adherence testing set were estimated to be fully and partially adherent, respectively, while in all cases the probability of nonadherence was extremely low. The evaluation of patient adherence to ATR therapy based on pharmacokinetic modeling and Monte Carlo simulation has important advantages over the collection of trough samples and the use of therapeutic ranges.

The antihyerlipidemic drug atorvastatin (ATR) is used worldwide as part of the strategy to prevent cardiovascular events. The high prevalence of patient nonadherence remains an important challenge which could be addressed efficiently by precision pharmacotherapy based on therapeutic drug monitoring (TDM). ATR is metabolized to pharmacologically active metabolites, and evidence shows that the sums of ATR acid and lactone form concentrations (ATR + ATRL), or of ATR and hydroxylated metabolites (ATR + MET) should be assayed. A method is presented for the analysis of these substances in serum. Method validation included the estimation of the quantitative relationship between the concentrations and the standard deviations (SD), which supports the optimal incorporation of TDM results into nonparametric pharmacokinetic models. The concentrations of the analytes were evaluated in human subjects receiving ATR. The method’s performance improved by taking the sums of acid and lactone concentrations into account. The concentration–SD relationship was linear, and we recommend applying Theil’s regression for estimating the assay error. All analytes could be detected by 2 h post dose in the samples of human subjects. The changes in metabolite/parent drug concentration ratios in time depended on the dose. The method is suitable for the TDM of ATR with a focus on precision pharmacotherapy.