Enabling personalized medicine through pharmacokinetic modeling

Personalized medicine strives to deliver the ‘right drug’ at the ‘right dose’ at the ‘right time’ by considering the unique characteristics that define specialized populations of patients and contribute to inter-individual variability, a leading cause of therapeutic failure when not properly considered. Given the challenges of studying specialized patient subgroups in clinical trials as well as the high degree of control necessary to tease out differences across populations, physiologically based pharmacokinetic (PBPK) modeling emerged as a key tool to evaluate complex clinical phenotypes and to predict the potential distribution of patient responses. Unfortunately, the inherent variability of biological systems and knowledge gaps in physiological data often limit confidence in model predictions for special populations. Thus, a critical step in model development for special populations involves an in-depth analysis of estimated model input and evaluation of the underlying physiological mechanisms leading to variability in pharmacokinetics, both of which may be guided by global sensitivity analysis and advanced statistical techniques.The benefits of global sensitivity as a means to refine parameter estimates and to understand how model behavior depended on the input parameter space were demonstrated using GastroPlus™ model, a well-known commercially available platform. Global sensitivity analysis was performed in two stages using the Morris Method to screen for significant factors followed by quantitative assessment of variability using Sobol’s sensitivity analysis. The 2-staged approach significantly reduced computational cost without sacrificing interpretation of model behavior, revealing nonlinearities and parameter interactions that would have been missed by local approaches. Furthermore, the utility of pharmacokinetic models to study the underlying and complex physiological mechanisms contributing to clinical differences across patient subgroups was revealed using Monte Carlo simulations by restricting model input to parameter combinations that described only biologically plausible model output. Through an integrated approach using a support vector machine, principal component analysis and global sensitivity analysis, specific combinations of parameters were shown to give rise to clinical phenotype, while individual parameters influenced the shape of the exposure profile. Augmenting analysis of the model input with global sensitivity analysis enabled an understanding of sexual dimorphism and inter-individual variability in pharmacokinetics.Finally, a dynamic semi-mechanstici model that considered pharmacokinetics and pharmacodynamics was used to demonstrate how patients benefit from careful timing of drug delivery. In this study, a mathematical model was developed to explore chronopharmacological dosing of synthetic glucocorticoids and its influence on the endogenous glucocorticoid secretion. Considering the central regulatory function of endogenous glucocorticoids for metabolic, anti-inflammatory, immunosuppressive and cognitive signaling, maintenance of normal physiological functions regulated by glucocorticoids is essential to host survival, while chronic disruption leads to severe systemic complications. Therefore, a key objective in glucocorticoid research is the development of novel dosing regimens that minimize the disruption of endogenous activity, while maintaining the pharmacological benefits of long-term therapy. Physiologically based modeling showed how chronic daily dosing resulted in modification of endogenous glucocorticoid activity with the extent of said changes dependent on the administration time and dose. However, simulations also revealed that endogenous glucocorticoid activity was preserved with proper timing of administration dependent on the dosage form. Furthermore, amending the model to account for inter-sex and inter-individual variability showed chronopharmacological dosing regimens can be further optimized by identifying the ‘right dose’ and ‘right time’ in the targeted patient populations by considering the underlying regulatory differences between males and females.