Table 2.

Summary of the most important points discussed at the panel discussions

Panel questions	Summary
Panel Session 1: Applications of PBPK
The goal of this session was to discuss potential applications of PBPK in drug evaluation, and to determine which areas relevant to drug development and review are currently amenable to the use of PBPK.
General	Use of PBPK should be appropriately weighed with the complexity of the question. Its utility becomes more significant in situations (e.g., products under accelerated approval process) or populations where it is difficult or not ethical to conduct clinical trials. PBPK provides a more mechanistic understanding of the various factors influencing pharmacokinetics (e.g., nonlinearity) and helps drug developers understand their molecule better. It provides a learning platform where knowledge can be accumulated and turned into information to assess dosing recommendations in patient populations.
A. Drug–drug interactions 1. Under what circumstances can and should PBPK models be used to predict the effect of concomitant medications on the pharmacokinetics of an investigational drug via modulation of CYP-mediated metabolism? How should we use such models to design studies and inform drug labeling?	For the prediction of the effect of enzyme modulators on the pharmacokinetics of substrate (“victim” DDI), the substrate's fractional metabolism via the pathway(s) of interest (fm) is central, and the early availability of mass balance data (typically conducted with radiolabeling the substrate) are useful. In cases where a healthy subject phenotype does not represent target population or explain the variability, a PBPK model may be used to inform design to obtain additional sparse PK data from efficacy/safety trials, which can supplement existing data. This comment also applies to applications beyond DDIs.
2. What are current knowledge (data, model) and confidence in using PBPK to predict the effect of an investigational drug on CYP-mediated metabolism? How should we use such models to design studies and inform drug labeling?	Two areas that need additional research are predicting DDIs in the gut and predicting time dependent inhibition (current PBPK systems tend to over-predict the extent of inhibition).
3. What is the current knowledge (data, model) and confidence in using PBPK to predict drug–drug interactions related to drug transporters systems? How should we use such models to design studies and inform drug labeling?	Transporter biology, tissue expression, and predicting intracellular drug concentration are areas that require more research to improve predictive performance of PBPK. Confidence in model prediction varies for different transporters. PBPK as a platform should be used to evaluate the role of transporters and to design studies.
B. Pharmacokinetic prediction in humans: first-in-human (FIH) Under what circumstances should PBPK be used to predict PK prior to a FIH? Comment on its utility vs. other methods (e.g., allometry) and predicting PK for biologics.	Primarily for drug developers, FIH prediction using PBPK is important for decision-making and allows additional learning of the molecule and coping with situations when other methods may not be adequate.
C. Other specific populations and scenarios	Organ impairment
1. Is there sufficient knowledge to use PBPK to predict pharmacokinetics for the following: a. Organ impairment (hepatic or renal) b. Age (pediatric or geriatric)	Disease progression and underlying co-morbidities should be considered when predicting the effect of organ impairment. Data-sharing especially from longitudinal studies and at the subject level may be useful.
For pediatrics, what is the utility of using a PBPK approach in humans older than 2 years? c. Different ethnicity/race groups d. Pregnancy e. Concomitant food intake and new formulations f. Intracellular concentrations	Pediatrics Effect on elimination pathways should be better defined across the entire age spectrum. PBPK and allometry are complementary methods, and it will be important to know when they do not agree. PBPK adds value when age-dependent drug absorption plays a role. To this end, effect of formulation in pediatric patients needs to be considered. Other patient populations/scenarios were not discussed.
Panel Session 2: PBPK Model Verification and Reporting in Regulatory Submissions
The goal of this session was to discuss assessment of model fidelity and best practices in reporting. There is heterogeneity in the level of detail on PBPK models included in submissions to the FDA. The FDA would like to establish basic requirements for a PBPK-related regulatory submission to ensure completeness, consistency, and efficiency in the review process.
1. What would be the critical elements for each of the following categories within a PBPK study report? Comment on the following:	PBPK modeling should remain more iterative than conventional PK/PD modeling, because new findings help improve the model and overall understanding.
Purpose Summary input parameters and assumptions Necessary sensitivity analysis Model verification process Model application Simulation results Discussion/conclusion	Although level of details in PBPK submissions may vary, purpose and parameters considered critical by the sponsors should be clearly presented. Variability assessment is often missing in regulatory submissions. Adequacy of a submitted PBPK work should be assessed in conjunction with known therapeutic index of the drug and modeling purpose. Model optimization may lead to a more predictive model. However the process should be transparent, consider other data (e.g., emerging in vitro, clinical interaction, urine, human mass-balance data) in addition to plasma PK data, be purpose driven, and be discussed with regard to model plausibility. A reasonable range for sensitivity analysis should be provided and justified. Standardization on the general utility of system model and inclusion of database for other drugs are needed. Model parameters with known certainty can be pre-specified, allowing more informed determination of candidate parameters for sensitivity analysis.
2. How should model fidelity be assessed? For example, given the significant inter-study variability of PK across various studies of a given drug, should model verification focus on the ability of the model to reasonably describe the PK data from all available clinical studies in the target populations? 2a. What other approaches should be used? 2b. When data from multiple studies are available, what external verification approaches should be utilized?	At FDA-sponsor meetings, attendance of individuals knowledgeable of the modeling work is preferred from both sides.

For more detailed information, see ref. 2.