PBPK modeling and simulation in drug research and development

Abstract

Physiologically based pharmacokinetic (PBPK) modeling and simulation can be used to predict the pharmacokinetic behavior of drugs in humans using preclinical data. It can also explore the effects of various physiologic parameters such as age, ethnicity, or disease status on human pharmacokinetics, as well as guide dose and dose regiment selection and aid drug-drug interaction risk assessment. PBPK modeling has developed rapidly in the last decade within both the field of academia and the pharmaceutical industry, and has become an integral tool in drug discovery and development. In this mini-review, the concept and methodology of PBPK modeling are briefly introduced. Several case studies were discussed on how PBPK modeling and simulation can be utilized through various stages of drug discovery and development. These case studies are from our own work and the literature for better understanding of the absorption, distribution, metabolism and excretion (ADME) of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed.

Keywords: Absorption; Drug–drug interaction; Metabolism; PBPK; PK prediction; Special population.

Graphical abstract

Figure 1

Schematic of a PBPK model.

Figure 2

Observed (□) and PBPK model–simulated (-) plasma concentration–time profiles of YQA-14 in rats (A and B) and dogs (C and D) after a single i.v. (A and C) or (p.o.) (B and D) administration. Observed plasma concentration–time profiles (OBS) were obtained for rats and dogs after single i.v. and p.o. administration of YQA-14 at 25 and 5 mg/kg, respectively (n=3 rats/group; n=4 dogs/group). This figure is adapted from Ref. with permission.

Figure 3

Simcyp simulation results of phenacetin AUC_0–24 at 1400 mg daily×10 days in the presence of IPRN (60 mg daily×10 days) and absence of IPRN in healthy subjects (A) and smokers (B), or the presence of PRN (60 mg daily×10 days) and absence of PRN in healthy subjects (C) and smokers (D). The outer curves represent phenaceitn concentration in the presence of PRN or IPRN. This figure is adapted from Ref. with permission. IPRN, isopsoralen; PRN, psoralen.

Figure 4

Simulated and actual mean orteronel concentration-versus-time curves. The line represents the simulated mean area under the concentration-versus-time curve after a single dose of orteronel at 400 mg; the circles represent the actual data points from the high-fat diet group (n=42) treated with a single dose of orteronel 400 mg. This figure was adapted from Ref. with permission.

Figure 5

Physiologically based pharmacokinetic (PBPK) simulation of orteronel in (A) healthy subjects (observed and simulated values), subjects with moderate renal impairment (simulated values), and subjects with severe renal impairment (simulated values), and (B) regression of orteronel clearance vs. glomerular filtration rate (GFR) based on PBPK simulations in healthy subjects, subjects with moderate renal impairment, and subjects with severe renal impairment. Observed data for healthy subjects (high-fat diet group, n=42) were obtained from clinical study C21007. The clinical scenario assumed 100% bioavailability with all uncharacterized metabolism treated as hepatic clearance (orteronel dose: 400 mg BID for 10 days). CL, total clearance; RI, renal impairment. This figure was adapted from Ref. with permission.

Figure 6

PBPK modeling strategy employed to predict exposure in neonates and infants. A stepwise approach is followed with verification against in vivo data at each step. Simulations in juveniles are based on a model incorporating age dependencies in physiology and incorporating data from relevant in vitro systems. Verification in juvenile animals allows for model refinement before prediction in children. This figure was adapted from Ref. with permission.

Figure 7

Application of physiologically based pharmacokinetic modeling and simulation in various stages of drug discovery and development. Models were initially built with preclinical data, and later refined with available clinical information. This figure was adapted from Ref. with permission.