title: Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.
abstract: Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.
authors.affiliation: Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA.
authors.forename: Clara
authors.initials: C
authors.lastname: Hartmanshenn
authors.affiliation: Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA.
authors.forename: Megerle
authors.initials: M
authors.lastname: Scherholz
authors.affiliation: Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA. yannis@rci.rutgers.edu.|Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA. yannis@rci.rutgers.edu.
authors.forename: Ioannis P
authors.initials: IP
authors.lastname: Androulakis
mesh_terms: D000367:Age Factors; D002940:Circadian Rhythm; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000097:blood*; D010599:Pharmacokinetics; D057285:Precision Medicine / Q000379:methods*; D012727:Sex Characteristics
chemical_list: D004364:Pharmaceutical Preparations
keywords: Age; Circadian; Gender; Inter-patient variability; PBPK modeling; Personalized medicine; Sex; Special populations