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. 2013 Aug 14;2(8):e63.
doi: 10.1038/psp.2013.41.

Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development

Hm Jones  1 , K Rowland-Yeo
Affiliations

Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development

Hm Jones et al. CPT Pharmacometrics Syst Pharmacol. .
No abstract available

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Figures

Figure 1
Figure 1
Schematic of a PBPK model. Insert denotes detailed representation of the intestine. CLint, intrinsic clearance; PBPK, physiologically based pharmacokinetics.
Figure 2
Figure 2
Perfusion vs. permeability rate-limited tissue models. (a) Perfusion rate limited; (b) Permeability rate limited. Kp, tissue to plasma partition coefficient; RBC, red blood cell.
Figure 3
Figure 3
PBPK modeling strategy in drug discovery and development. An iterative “learn, confirm, and refine” approach to PBPK simulation is recommended. Initially, the PBPK simulation is performed in animals using animal PBPK models, animal in vitro data, and compound-specific physicochemical data. The animal simulation is compared with the in vivo data, if this simulation in animals is reasonable then the healthy volunteer simulation is performed using a human PBPK model built using healthy volunteer physiology, human in vitro data, and compound-specific physicochemical data. These simulations can then be extended to various patient populations using relevant physiology. If the simulation at any stage is inaccurate, this would indicate a violation of one or more of the model assumptions, in this case further experiments may be performed to understand the mismatch. PBPK, physiologically based pharmacokinetics.
Figure 4
Figure 4
Observed vs. predicted plasma concentration–time profiles in rat, dog, and human. (a) Rat i.v. (1 mg/kg); (b) Dog i.v. (0.5 mg/kg); (c) = Rat oral (2 mg/kg); (d) Dog oral (1 mg/kg); (e) Human oral (100 mg). Simulations were performed using the SimCYP Population-Based Simulator. In a–d: open squares = observed data; solid line = model prediction; In e: open squares = mean observed data ± SD; solid line = mean model prediction; dashed line = model predicted 5 and 95 percentiles. i.v., intravenous.
Figure 5
Figure 5
PBPK modeling strategy for prediction of DDIs. C (green) = clarithromycin; T (red) = trimethoprim; CY (yellow) = cyclosporine. Step A refers to the development of the initial substrate model using a combination of “top-down/bottom-up” approaches. Assessment of model accuracy via simulation and recovery of known DDI studies is then required to confirm the relative contribution of CYP and OATP components. Step B refers to validation of the inhibitor models before simulation of the DDIs by performing a comparison of simulated and observed profiles. Step C refers to simulation of the DDIs using validated substrate and inhibitor models to confirm that the final substrate model is able to recover the observed DDIs. AUC, area under the plasma concentration–time curve; CYP, cytochrome P450; DDI, drug–drug interaction; OATP, organic anion transport protein; PBPK, PBPK, physiologically based pharmacokinetics.
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