Expert Discussion of the Role of Rate Constant Versus Clearance Approaches to Define Drug Pharmacokinetics: Theoretical and Clinical Considerations

This article provides a dialogue covering an ongoing controversy on the use of clearance versus rate constant approaches for model parameterization when assessing pharmacokinetic (PK) data. It reflects the differences in opinions that can exist among PK experts. Importantly, this discussion extends beyond theoretical arguments to demonstrate how these different approaches impact the analysis and interpretation of data acquired in clinical situations. By not shying away from such dialogues, this article showcases how dissimilarity in well-grounded perspectives can influence how one applies PK and mathematical principles

Population Pharmacokinetic and Pharmacodynamic Modeling for Assessing Risk of Bisphosphonate-related Osteonecrosis of the Jaw

Objective: We hypothesized that patients with bisphosphonate (BP)-related osteonecrosis of the jaw (BRONJ) accumulate higher levels of BP in bone than those without BRONJ. Study Design: Using the Pmetrics package and published data, we designed a population pharmacokinetic model of pamidronate concentration in plasma and bone and derived a toxic bone BP threshold of 0.2 mmol/L. With the model, and using patient individual BP duration and bone mineral content estimated from lean body weight, we calculated bone BP levels in 153 subjects. Results: Mean bone BP in 69 BRONJ cases was higher than in 84 controls (0.20 vs 0.10 mmol/L, P \u3c 0.001), consistent with the toxic bone threshold of 0.2 mmol/L. BRONJ was also associated with longer duration BP therapy (5.3 vs 2.7 years, P \u3c 0.001), older age (76 vs 70 years, P \u3c 0.001), and Asian race (49% vs 14%, P \u3c 0.001). Conclusions: Our model accurately discriminated BRONJ cases from controls among patients on BP therapy. © 2013 Elsevier Inc

Population Pharmacokinetic Analysis of Nonlinear Behavior of Piperacillin during Intermittent or Continuous Infusion in Patients with Cystic Fibrosis

The purpose of this study was to describe the nonlinear pharmacokinetics of piperacillin observed during intermittent infusion and continuous infusion by using a nonparametric population modeling approach. Data were 120 serum piperacillin concentration measurements from eight adult cystic fibrosis (CF) patients. Individual pharmacokinetic parameter estimates during intermittent infusion or continuous infusion were calculated by noncompartmental analysis and with a maximum iterative two-stage Bayesian estimator. To simultaneously describe concentration-time data during intermittent infusion and continuous infusion, nonlinear models were parameterized as two-compartment Michaelis-Menten models. Models were fit to the data with the nonparametric expectation maximization algorithm. The calculations were executed on a remote supercomputer. Nonlinear models were evaluated by log-likelihood estimates, residual plots, and R(2) values, and predictive performance was based on bias (mean weighted error [MWE]) and precision (mean weighted square error [MWSE]). A linear pharmacokinetic model could not describe combined intermittent infusion and continuous infusion data well. A good population model fit to the intermittent infusion and continuous infusion data was obtained with the constructed nonlinear models. Maximum a posteriori probability (MAP) Bayesian R(2) values for the nonlinear models were 0.96 to 0.97. Median parameter estimates for the best nonlinear model were as follows: K(m), 58 ± 75 mg/liter (mean and standard deviation); V(max), 1,904 ± 1,009 mg/h; volume of distribution of the central compartment, 14.1 ± 3.0 liters; k(12), 0.63 ± 0.41 h(−1); and k(21), 0.37 ± 0.19 h(−1). The median bias (MWE) and precision (MWSE) values for MAP Bayesian estimation with the Michaelis-Menten model were 0.05 and 4.6 mg/liters, respectively. The developed nonlinear pharmacokinetic models can be used to optimize piperacillin therapy administered via continuous infusion in patients with CF and have distinct advantages over conventional linear models