Caspofungin is an echinocandin antifungal commonly used as the first-line therapy for invasive candidiasis, salvage therapy for invasive aspergillosis. Pharmacokinetic variabilities and suboptimal exposure have been reported for caspofungin, increasing the risk of insufficient efficacy. We aimed to consolidate information from population pharmacokinetic studies, compare model performance, identify significant covariates affecting caspofungin’s PKs, evaluate probability of target attainment in different studies and assemble pharmacokinetic/pharmacodynamic target to address existing knowledge gaps that may warrant further investigation in future. We performed a systematic search strategy to review the PPK studies of caspofungin. Four databases were searched. We extracted information for the comparison of models, evaluation of the impact of covariates on clearance and apparent volume and the calculation of probability of target attainment under specific minimum inhibitory concentration. Thirteen studies were included. The simulation results showed that under labeled dose, pediatrics exhibited notably higher exposure than adults. Body size was the most identified covariate that affected both clearance and volume of distribution. For C. albicans and C. parapsilosis, none of the populations achieved a PTA of ≥ 90%. In contrast, for C. glabrata, 70% of the adult patients reached a PTA of ≥ 90%, while all pediatric patients achieved the same PTA level. At the recommended dosage, adults showed lower exposure to caspofungin compared to pediatrics. It is crucial to consider body size, liver function and serum albumin when determining caspofungin dosage regimens. Furthermore, further research is required to comprehensively understand the pharmacokinetics of caspofungin in pediatrics.

Aim: nlmixr offers first-order conditional estimation with or without interaction (FOCE or FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin’s population pharmacokinetics with flip-flop characteristics within nlmixr framework and investigated SAEM and FOCEi’s performance with respect to bias, precision, and robustness. Method: Compartmental pharmacokinetic models were fitted. The final model was determined based on the lowest objective function value and visual inspection of goodness-of-fit plots. To examine flip-flop pharmacokinetics, k_a values of a typical concentration-time profile based on the final model were perturbed and changes in the steepness of the terminal elimination phase were evaluated. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100-times and resultant changes evaluated. Results: A one-compartment model with transit compartment for absorption best described the data. At low n, Stirling’s approximation of n! over-approximated plasma concentration unlike the log-gamma function. Flip-flop pharmacokinetics were evident as the steepness of the terminal elimination phase changed with k_a. Mean rRMSE for fixed-effect parameters was 0.932. When initial estimates were perturbed, FOCEi estimates of k_a and food effect on k_a appeared bimodal and were upward biased. Discussion: nlmixr is reliable for NLMEM even if flip-flop is present but caution should be exercised when using Stirling’s approximation for n! in the transit compartment model. SAEM was marginally superior to FOCEi in bias and precision, but SAEM was superior against initial estimate perturbations.