Purpose The aim of this study was to explore possible differences in the pharmacokinetics (PK) of recombinant factor VIII:C (ReFacto® -ReFacto ) in HIV+ vs. HIVpatients and also differences in the chromogenic substrate bioassay (CHS) and one-stage clotting (OSC) methods. Methods Twenty-eight haemophilia A adults (20 HIV-and eight HIV+) were assayed with both the CHS and OSC methods. An average of two and six samples were collected per patient for HIV-/+, respectively, after one, and occasionally two more, prophylactic doses (mean 2,003 IU; range 1,000-4,300 IU). The observations were analysed with the mixed-effects (population) compartmental PK modelling package NONMEM (nonlinear mixed-effects modelling) and the FOCE (first-order conditional estimation) method. Base modelling was performed independently for the CHS and OSC bioassays for comparison, and covariate models and simulation tests were done only for the commonly used OSC bioassay. The final covariate model was validated using the bootstrap method. Monte Carlo simulations were used to estimate the expected probability of exceeding 20%, 40% or 60% of normal factor VIII:C in plasma after a single dose, corresponding to required levels for preventing mild, moderate and lifethreatening haemorrhages. Results One-compartment base-model population PK parameters were [mean parameter (interpatient variability %)] for CHS: clearance (CL)=2.56 dl h −1 (33.2%); volume of distribution (V)=34.8 dl (12.8%); and for OSC: CL= 3.83 dl h −1 (47.8%), V=53.7 dl (22.4%). The volumes differed significantly between the CHS and OSC methods (p<0.0001), and variabilities were higher for OSC. Nevertheless, the empirical half-lives (t 1/2 =l n (2) × V/CL) were similar for CHS and OSC, [(mean ± standard deviation (SD)], 9.5 ± 3 h and 10.2 ± 4 h, respectively. In covariate modelling with the OSC-derived model, HIV status (VIR) was a significant categorical predictor (p<0.005) for V.The final covariate models with OSC were for CL=3.93 + 0.09 × (WT-75) and for V=48.6 × (1 + 0.36 × VIR) + 0.55 × (WT-75); therefore, V for the typical HIV+ patient was 36% higher than for the HIV-patient. Conclusions Both HIV-and HIV+ patients showed 100% success with the 20% threshold at doses >20 IU/kg. HIVpatients receiving >50 IU/kg had a 100% expected chance of success for all thresholds. HIV+ patients for moderate or life-threatening haemorrhage treatment need 10 IU/kg more than the HIV-patient equivalent to have the same probability of success.

The rapid achievement of efficacious exposure to sirolimus (SRL) after renal transplantation is crucial. However, there is high unpredictability in the dose to exposure relationship. Part of the variation is related to patients originating from subpopulations of fast or slow metabolizers via the CYP3A5*1/*3 genotype. The probability of achieving therapeutic SRL blood concentrations for each subpopulation under two equal-intensity increasing-frequency protocols after the start of treatment was explored with Monte Carlo simulation. The population pharmacokinetic model and inter-patient variability distributions of Djebli et al. (DRH2006) were sampled. They developed a base and final model with a genotype covariate for CL/F in patients receiving calcineurin inhibitor (CNI)-free therapy with SRL, mycophenolate mofetil and corticosteroids. Fast metabolizers (expressers) had a CL/F of 28.3 l/h whilst slow metabolizers (non-expressers) had a CL/F of 14.1 l/h. Here, in simulation, a standard 10 mg QD SRL was contrasted with a higher frequency of 5 mg BID SRL as related to the proportion of next dosed patients being within the 15-30 ng/ml trough levels on day 7 after transplantation. Near 0% of expressers on either regimen reached or exceeded the 30 ng/ml trough on day 7. Expressers showed protocol dependence for the chance of being within the 15-30 ng/ml range with the 5 mg BID protocol doubling those chances. Non-expressers appeared less protocol dependent for the probability of being above or below the 15-30 ng/ml range. The ability to determine the genotype early on may help to rationalize the initial titration of individual patients receiving CNI-free renal transplantation treatment with SRL.

Kinetic nomograms allow rapid and reliable dosage adjustment after the start of drug therapy. They are interesting alternatives to the cumbersome Bayesian procedure, and they provide dosage adjustment even for drugs that exhibit large intraindividual variability. In the clinical context, kinetic nomograms render individual dosage adjustment a simplified bedside application, and they could assist population studies aiming at dose individualization.