Purpose: The WHO reported in 2016 that 39% of adults aged 18 years and over were overweight, and 13% were obese. With the larger fat mass and other physiological changes, the distribution of lipophilic drugs into adipose tissues is increased and pharmacokinetics (PK) may differ in obese subjects. Numerous reports about effects of obesity led the European Medicines Agency (EMA) to publish a draft reflection paper in Feb 2018 on needs to investigate PK and pharmacodynamics in the obese population. As a top-down approach, Population PK (“PopPK”) analysis is a useful tool, but this approach needs improved physiological components as covariates and suitable data sets to build adequate models for obese subjects. In contrast, Physiologically-Based PK (PBPK) models can be assembled by extending average subject data from a limited population (“Conventional PBPK”). As a bottom-up approach, Conventional PBPK can simulate clinical PK using a virtually generated population adjusted with physiological components. The inter-individual variability (IIV) of observed clinical data is ignored using an average subject approach. The IIV can be provided as physiological variabilities of virtual subjects generated by an in silico method. This approach uses an average subject data as a baseline and the IIV depends on the baseline parameters and model structure. Therefore, predicting variance of clinical PK using Conventional PBPK has limitations. An extended PBPK modeling approach for clinical PK prediction (“Extended PBPK”) is proposed (Figure 1). Our new approach involves: [1] using individual subject data to estimate parameters, [2] defining IIV and residual variability (RV) from observed population data, and [3] building in diverse physiologic changes in obesity in the structural model. The present study demonstrates the usefulness of an extended PBPK modeling approach for a clinical study that includes obese subjects with comparisons of conventional PBPK and PopPK methods.
Methods: Individual plasma concentration-time profiles of 26 subjects including normal, obese, and extremely obese women who received 1.5 mg oral levonorgestrel (LNG) were utilized. Body weight, height, age, sex and race were included as individual physiological data. Relationships for organ/tissue weights, composition, and blood flows in relation to these factors for adult humans were found and/or adapted from literature sources. Specific PBPK body components were then generated from the physiologic metrics for individual subjects. Tissue/plasma partition coefficients (Kp) were calculated using published methods. All analyses and parameter estimates were conducted using Phoenix NLME (ver. 8.0, Certara, NJ).
Results: The plasma concentration-time profiles of LNG showed considerable IIV. Obese subjects exhibited smaller Cmax and AUC values and longer half-lives compared with normal subjects. PopPK analysis with a 2-compartment model could describe the large IIV, but covariate Body Mass Index (BMI) for distribution CL and peripheral volume were needed to explain the IIV. Parameter estimates of our extended PBPK model were fitted using individual subject data and then typical values and random effects were obtained using nonlinear mixed effect (NLME) models (Table 1). Three of four IIVs did not require BMI as a covariate, but the IIV of absorption rate (ka) that had no physiological basis was slightly affected (Figure 2). When parameters only from normal subjects was used for simulation of a virtual population, the effect of obesity on PK was well reproduced (Table 2, Figure 3). The extended PBPK model well described the general PK variance of LNG without any covariates such as BMI despite including extremely obese subjects.
Conclusion: An extended PBPK modeling approach was developed using PK data and individual subject metrics for normal, obese, and extremely obese subjects. Unlike conventional PBPK using average subject physiologic data, the extended PBPK approach for individual subjects includes realistic parameters for disturbed physiology. This concept could be adapted in early clinical drug development using limited PK study data to anticipate the variability in PK of more diverse subjects who may receive study drugs.
Toshimichi Nakamura
– Visiting researcher, University at Buffalo, Williamsville, New YorkToshimichi Nakamura
– Visiting researcher, University at Buffalo, Williamsville, New YorkEmilie Molins
– Department of Pharmaceutical Sciences, University at Buffalo, New YorkMelissa Natavio
– Assistant Professor, Department of Obstetrics and Gynecology, Keck School of Medicine, University of Southern California, CaliforniaFrank Stanczyk
– Research Professor, Department of Obstetrics and Gynecology, Keck School of Medicine, University of Southern California, CaliforniaWilliam Jusko
– SUNY Distinguished Professor, Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, New YorkWilliam Jusko
– SUNY Distinguished Professor, Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, New YorkToshimichi Nakamura
– Visiting researcher, University at Buffalo, Williamsville, New York338 Views