Introduction Non-parametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic sub-groups 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 non-parametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative two-stage Bayesian (IT2B) and the non-parametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a one-compartment model of an intravenous drug. Results The true means (SD) for Kel and Vd 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). IT2B estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates. Conclusion Built on over three decades of work, Pmetrics contains a robust, reliable, and mature non-parametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic sub-groups and an outlier.

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 ( < 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter ( = 0.005), with 36%, 7%, and 6% over 15 mg/liter ( < 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; = 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days; = 0.03), and reduced nephrotoxicity (8%, 0%, and 2%; = 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days ( = 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.).

A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release was modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. When low affinity (20 microM) and rapid kinetics were assumed for the calcium receptor triggering exocytosis, and stimulus parameters were selected to match those of experiments, the simulations predicted a virtual invariance of the time course of transmitter release to paired stimulation, stimulation with pulses of different amplitude, and stimulation in different calcium solutions. The large temperature sensitivity of experimental release time course was explained by a temperature sensitivity of the model's final rate limiting exocytotic process. Inclusion of calcium tail currents and a saturable buffer with finite binding kinetics resulted in high peak calcium transients near release sites, exceeding 100 microM. Models with a single class of calcium binding site to the secretory trigger molecule failed to produce sufficient synaptic facilitation under this condition. When at least one calcium ion binds to a different site having higher affinity and slow kinetics, facilitation again reaches levels similar to those seen experimentally. It is possible that the neurosecretory trigger molecule reacts with calcium at more than one class of binding site.

Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approazches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.