Introduction: Nonparametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic subgroups and outliers within a study population. Methods: The authors created “Pmetrics,” a new Windows and Unix R software package that updates the older MM-USCPACK software for nonparametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative 2-stage Bayesian and the nonparametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (K_el) and unimodal volume of distribution (V_d), plus an extreme outlier, for a 1-compartment model of an intravenous drug. Results: The true means (SD) for K_el and V_d in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). The iterative 2-stage Bayesian estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of K_el, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for K_el and to find the outlier in both the population model and in the Bayesian posterior parameter estimates. Conclusions: Built on over 3 decades of work, Pmetrics adopts a robust, reliable, and mature nonparametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic subgroups and an outlier.

The current vancomycin therapeutic guidelines recommend the use of only trough concentrations to manage the dosing of adults with Staphylococcus aureus infections. Both vancomycin efficacy and toxicity are likely to be related to the area under the plasma concentration-time curve (AUC). We assembled richly sampled vancomycin pharmacokinetic data from three studies comprising 47 adults with various levels of renal function. With Pmetrics, the nonparametric population modeling package for R, we compared AUCs estimated from models derived from trough-only and peak-trough depleted versions of the full data set and characterized the relationship between the vancomycin trough concentration and AUC. The trough-only and peak-trough depleted data sets underestimated the true AUCs compared to the full model by a mean (95% confidence interval) of 23% (11 to 33%; P = 0.0001) and 14% (7 to 19%; P < 0.0001), respectively. In contrast, using the full model as a Bayesian prior with trough-only data allowed 97% (93 to 102%; P = 0.23) accurate AUC estimation. On the basis of 5,000 profiles simulated from the full model, among adults with normal renal function and a therapeutic AUC of ≥400 mg · h/liter for an organism for which the vancomycin MIC is 1 mg/liter, approximately 60% are expected to have a trough concentration below the suggested minimum target of 15 mg/liter for serious infections, which could result in needlessly increased doses and a risk of toxicity. Our data indicate that adjustment of vancomycin doses on the basis of trough concentrations without a Bayesian tool results in poor achievement of maximally safe and effective drug exposures in plasma and that many adults can have an adequate vancomycin AUC with a trough concentration of <15 mg/liter.

We hypothesized that dosing vancomycin to achieve trough concentrations of >15 mg/liter overdoses many adults compared to area under the concentration-time curve (AUC)-guided dosing. We conducted a 3-year, prospective study of vancomycin dosing, plasma concentrations, and outcomes. In year 1, nonstudy clinicians targeted trough concentrations of 10 to 20 mg/liter (infection dependent) and controlled dosing. In years 2 and 3, the study team controlled vancomycin dosing with BestDose Bayesian software to achieve a daily, steady-state AUC/MIC ratio of ≥400, with a maximum AUC value of 800 mg · h/liter, regardless of trough concentration. For Bayesian estimation of AUCs, we used trough samples in years 1 and 2 and optimally timed samples in year 3. We enrolled 252 adults who were ≥18 years old with ≥1 available vancomycin concentration. Only 19% of all trough concentrations were therapeutic versus 70% of AUCs ( P < 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter ( P = 0.005), with 36%, 7%, and 6% over 15 mg/liter ( P < 0.0001). Bayesian AUC-guided dosing in years 2 and 3 was associated with fewer additional blood samples per subject (3.6, 2.0, and 2.4; P = 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days; P = 0.03), and reduced nephrotoxicity (8%, 0%, and 2%; P = 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days ( P = 0.002). There was no difference in efficacy by year, with 42% of patients having microbiologically proven infections. Compared to trough concentration targets, AUC-guided, Bayesian estimation-assisted vancomycin dosing was associated with decreased nephrotoxicity, reduced per-patient blood sampling, and shorter length of therapy, without compromising efficacy. These benefits have the potential for substantial cost savings. (This study has been registered at ClinicalTrials.gov under registration no. NCT01932034.)

Background. Voriconazole pharmacokinetic and pharmacodynamic data are lacking in children.Methods. Records at the Childrens Hospital Los Angeles were

Background Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians. Methods A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately. Results Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04). Conclusions Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.

Background: Among women with low or undetectable quantities of HIV-1 RNA in plasma, factors associated with genital HIV-1 RNA shedding, including choice of treatment regimen, are poorly characterized. Methods: We measured HIV-1 RNA in cervical swab specimens obtained from participants in the Women's Interagency HIV Study who had concurrent plasma viral RNA levels <500 copies/mL, and we assessed factors associated with genital HIV shedding. The study was powered to determine the relative effects of antiretroviral protease inhibitors (PIs) versus nonnucleoside reverse transcriptase inhibitors (NNRTIs) on viral RNA shedding. Results: Overall, 44 (15%) of 290 women had detectable HIV-1 RNA in cervical specimens. In the final multivariate model, shedding was independently associated with NNRTI (vs. PI) use (odds ratio [OR], 95% confidence interval [CI]: 2.24, 1.13 to 4.45) and illicit drug use (OR, 95% CI: 2.41, 0.96 to 5.69). Conclusions: This is the largest study to define risks for genital HIV-1 RNA shedding in women with low/undetectable plasma virus. Shedding in this population was common, and NNRTI-based highly active antiretroviral therapy (HAART) (vs. PI-based HAART) was associated with genital HIV shedding. Further study is required to determine the impact of these findings on transmission of HIV from mother to child or to sexual partners.