f Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiplemodel Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, ؊17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was ؊0.7% (interquartile range, ؊7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, ؊1.4 to 14.7%; P ‫؍‬ 0.16 versus the full posterior parameter value) and the dose bias was ؊6.7% (interquartile range, ؊18.7 to 2.4%; P ‫؍‬ 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, ؊13.1 to 18%; P ‫؍‬ 0.32) and a dose bias of ؊3.5% (interquartile range, ؊18 to 14%; P ‫؍‬ 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.) V oriconazole is the approved first-line therapy for aspergillosis in patients who are at least 12 years of age in the United States and at least 2 years of age elsewhere. Numerous reports of studies in both adults (1-7), including a prospective randomized trial (8), and children (9-11) have documented improved outcomes when trough concentrations are maintained above 1 mg/liter, which is a readily measured clinical surrogate for the full area under the concentration-time curve (AUC) that drives efficacy (12-15).However, the pharmacokinetic behavior of voriconazole is complex and nonlinear, such that in many patients, small dose changes are associated with disproportionately large changes in the plasma concentrations of the drug. While it is more common in adults, nonlinear, saturated pharmacokinetic behavior is readily observed in children who receive doses higher than those that have been approved by regulatory agencies (16). This nonlinearity also makes the half-life and the time to steady state dependent on the dose and concentration, complicating the ability to compare steady-state trough concentrations to the accepted therapeu...

Background Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4–9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden. Methods The authors developed a non-parametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded fashion, they compared dosage adjustments using the model in the BestDose™ software to dosage adjustments calculated by non-compartmental estimation of AUC at a national reference laboratory in a cohort of patients not included in model building. Results Mean (range) age of the 53 model-building subjects was 7.8 (0.2 – 19.0) years and weight was 26.5 (5.6 – 78.0) kg, similar to nearly 120 validation subjects. There were 16.7 (6 – 26) samples per subject to build the model. The BestDose cohort was also diverse: 10.2 (0.25 – 18) years and 46.4 (5.2 – 110.9) kg. Mean bias and imprecision of the one-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600–900 ng/mL were 1.1 mg/kg (≤12kg, 67% in the target range and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% CI) bias of BestDose calculations for the third dose was 0.2% (−2.4% to 2.9%, P=0.85), compared with the standard non-compartmental method based on 9 concentrations. With one optimally timed concentration 15 minutes after the infusion (calculated with the authors’ novel MMopt algorithm) bias was −9.2% (−16.7% to −1.5%, P=0.02). With two concentrations at 15 minutes and 4 hours bias was only 1.9% (−0.3% to 4.2%, P=0.08). Conclusions BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only two blood samples per adjustment compared to 6 – 9 samples for standard non-compartmental dose calculations.