A PBPK analysis uses models and simulations that combine physiology, population and drug characteristics to mechanistically describe the pharmacokinetic (PK) behavior of a drug. Throughout a drug’s life cycle, PBPK model predictions can be used to support decisions on whether, when and how to conduct certain clinical pharmacology studies and to support dosing recommendations in product labeling.
Use PBPK Modeling for . . .
- Early phase development work (bioanalysis, formulation optimization, PK prediction)
- Dose regimen design and optimization
- Interspecies scaling/Phase I FIH exposure predictions
- Population factors (age, ethnicity, gene expression, etc.)
- Formulation bridging/bioequivalence
- Prediction of drug-drug interaction
- Antibody PBPK to understand target coverage
An Iterative Modeling Process
As an iterative process, PBPK modeling can be started with very limited information and also be conducted at any point along the drug development continuum. Sometimes the model is used and refined multiple times throughout development.
For example, a very simple PBPK model for a small molecule can be initiated with only molecule structure information and predicted physical chemical properties. This model can then be used to inform future study design where additional data can be fed back to the model to create a more accurate prediction. The process can be repeated throughout the development process to further refine the model as it moves towards the clinic.
Once in the clinic, actual human PK data can be used to validate/inform the model so clinical study design can be refined and streamlined.
While PBPK is not specifically required for a regulatory filing, the FDA has issued the 2018 FDA Guidance on PBPK Modeling Submissions and has strongly suggested including modeling in your data package. In addition, the 2017 EMA guideline for first-in-human (FIH) dosing recommends that estimation of FIH should be based on state-of-the-art modelling (e.g. PK/PD and PBPK) and/or using allometric factors.
PBPK can be used to support drug-drug interaction assessments and, in some cases, has been accepted in lieu of running clinical DDI studies.
- For small molecules: Physical chemical properties of the molecule; in vitro ADME data (such as plasma protein binding, in vitro stability and CYP phenotyping).
- For large molecules: Information on target affinity and expression, turnover rate and occupancy are useful.
- For both: Some preclinical in vivo PK data to validate the model.
- Fully formatted report and data, ready for regulatory submission
- In vitro characterization of small molecule
- In vitro antibody assays to assess binding potency, occupancy, etc.
- In vivo PK
- Clinical PK