Development of a Physiology-Based Whole-Body Population Model for Assessing the Influence of Individual Variability on the Pharmacokinetics of Drugs

Abstract

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P(3)M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.

Physiologically Based Pharmacokinetic Approach to Determine Dosing on Extracorporeal Life Support: Fluconazole in Children on ECMO

“…Model parameters were optimized based on the observed data using the Monte Carlo simulation in the PK Sim parameter identification toolbox. The optimized model was used to generate population predictions of plasma concentration vs. time profiles for a virtual population of healthy adults ( n  = 1,000) created using the PK‐Sim population module 25. Interindividual variability of UGT2B7 expression was assumed to have log‐normal distribution with a geometric SD of 1.34 26.…”

“…In order to evaluate the performance of the pediatric PBPK model, model predictions were generated using a simulated population of infants ( N  = 1,000) created with the PK‐Sim population module25 and compared with raw data from a PK study of fluconazole in critically ill infants (Pediatric Validation Dataset, Table S2). The median (5th and 95th percentiles) area under the concentration‐time curve (AUC 0–24 ) for these 13 children was 485 mg hour/l (350 and 664) and was calculated using compartmental methods as described in Piper et al 34…”

A Workflow Example of PBPK Modeling to Support Pediatric Research and Development: Case Study with Lorazepam

“…Outputs were generated using the average observed clearance in conjunction with the mean age and weight of participants in the studies to define anatomical and physiological values (23). A visual check was used to evaluate predictive accuracy between simulated (PBPK model) versus observed concentration-time data and appropriateness of line shape.…”

Dose Selection Based on Physiologically Based Pharmacokinetic (PBPK) Approaches

“…tissue volumes, blood flows, transit times, expression levels). Knowledge of the variability of these different parameters enables the use of Monte Carlo methods, to predict variability in PK and to anticipate individuals whose attributes combine to give extreme PK risk (35,97,98). Further extensions of the approach taken by SimCYP to incorporate individual characteristics within the PBPK simulations will continue to evolve and be more widely applied.…”