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Background There has been an increased focus on population pharmacokinetics (PK) to improve the drug development process since the "Critical Path paper" by the Food and Drug Administration. This increased interest has given rise to additional algorithms. Objectives The purpose of this exercise was to compare the new algorithms iterative-2-stage (ITS) and maximum likelihood expectation maximization (MLEM) available in ADAPT 5 with other methods. Methods A total of 29 clinical trials with different study designs were simulated. Different algorithms were used to fit the simulated data, and the estimated parameters were compared with the true values. The algorithms ITS and MLEM were compared with the standard-2-stage, Iterative-2-Stage (IT2S) method in the IT2S package and the first-order conditional estimate (FOCE) method in NONMEM version VI. Imprecision and bias for the population PK parameters, variances, and individual PK parameters were used to compare the methods. Results Population PK parameters were well estimated and bias low for all nonlinear mixed effect modeling approaches. These approaches were superior to the standard-2-stage analyses. The algorithm MLEM was better than IT2S and ITS in predicting the PK and variability parameters. Residual variability was better estimated using MLEM and FOCE. A difference in the estimation of the variance exists between FOCE and the other methods. Variances estimated with FOCE often had shrinkage issues, whereas MLEM in ADAPT 5 had practically no shrinkage problems. Using MLEM, a reduction from 3000 to 1000 samples in the expectation maximization step had no impact on the results. Conclusions The new algorithm MLEM in ADAPT 5 was consistently better than IT2S and ITS in its prediction of PK parameters, variances, and the residual variability. It was comparable with the FOCE method with significantly fewer shrinkage issues in the estimation of variance. The number of samples used in the expectation maximization step with MLEM did not influence the results.

Therapeutic drug monitoring of factor VIII is well established in the treatment of patients with hemophilia attributable to important interindividual variability. The individual initial factor VIII dosage is usually calculated according to individual pharmacokinetic parameters obtained after a dose test administered before the surgery, using at least five-concentration data. The authors proposed a limited sampling strategy to estimate individual pharmacokinetic parameters from one- or two-concentration data in patients with hemophilia A before surgery. The mean population pharmacokinetic parameters and the interindividual variability (CV) were obtained from a group of 33 patients according to a two-compartment model using NONMEM. Eighteen additional patients were used to estimate the predictive performances of the population parameters and to evaluate the limited sampling strategies. Population parameters were clearance 2.6 mL/h per kilogram (CV 45.4%), initial volume of distribution 2.8 L (CV 21.1%). From two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion), the estimation of pharmacokinetic parameters was precise and not biased. Until now, in the hemophilic center of Lyon, the factor VIII dosage before surgery was based on the determination of the clearance, estimated from five- to nine-concentration data and on the target concentration (infusion rate = clearance x target). Ruffo et al proposed a limited sampling strategy (two-stage method) to estimate pharmacokinetic parameters from two concentration measurements drawn 3 and 9 hours after the dose. No information was given on the bias and precision of the estimation. This paper reports a one-stage method for a population pharmacokinetic study of factor VIII. The Bayesian estimation of individual pharmacokinetic parameters based on only two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion) is useful to define the best factor VIII dosage in hemophilic patients before surgery.

Cyclosporin, the drug of first choice in transplantation surgery, is characterized by a low therapeutic index and variable absorption, so close monitoring of the drug is required to optimize the dosing. Predose blood cyclosporin levels are measured routinely for therapeutic monitoring, but this approach is not optimal because the area under the concentration-time curve (AUC) correlates better with clinical events. However, conventional methods of measuring AUC require many blood samples, which is not viable in a routine clinical setting. AUC monitoring can be simplified for use in a clinical setting by using a limited sampling strategy (LSS) that allows AUC to be estimated using a small number of blood samples collected at specific times. This article reviews the current literature on estimating cyclosporin AUC using LSS. Thirty-eight papers suggesting the use of specific time points were found. LSS has been developed for different transplant types, with different dosing regimens, and with different assays. Most authors suggested either two- or three-sample equations. Results from authors who validated their models suggest that equations defined on one transplant type may be applicable to other transplant types, to both adults and children, and to early or late after transplantation. Moreover, it seems that there is flexibility in the choice of equations available to clinicians. The number of samples to collect for accurate estimations is a matter of debate, but a wise choice can minimize the number. The choice of the optimal LSS and validation are discussed.