Table 1.

Key points and recommendations from discussion sessions

Key points	Recommendations
Topic 1: What are appropriate data standards for drug input data for PBPK models?
Not all parameters are equal: critical values depend on the physicochemical properties of the molecule and on the application or interest (DDI, specific populations such as pediatrics, biopharmaceutics). Critical parameters are those that have an impact on addressing clinically relevant questions.	A consensus should be developed on the important input parameters for specific applications, e.g., a list of important input parameters for each category (DDI, specific populations, such as pediatrics, biopharmaceutics). More consideration is required on the incorporation of uncertainty in input parameters in models; consideration of covariance of parameters is also important.
Methodologies are not consistent across companies or even within some companies. While considered highly desirable, the ideal of a standard in vitro methodology across the industry was not thought to be a realistic aim. Rather, a full understanding and description of methodology with adoption of common reference standards to be utilized across companies should be encouraged.	Agreement and adoption of common reference standards to be utilized across companies should be encouraged.
There is mixed acceptance around scaling factors. Physiological scaling factors can be easily accepted, e.g., MPPGL (mg of protein per gram of liver), and where a consensus exists would not be expected to be altered. Empirical scaling factors, derived to account for a lack of direct extrapolation from in vitro to in vivo, are likely to be dependent on the in vitro methodology utilized and on the compound, and will be company- or lab-specific.	Guidance on the justification of scaling factors in models should be developed. If companies develop their own scaling factors then they must fully and transparently justify these.
Depending on the stage of development, in silico values may be useful, but measured values are often preferred. The exception to this is where there is evidence that in silico values are more accurate (e.g., log D for highly lipophilic compounds).
It is important to try to understand when poor simulations result from inadequate in vitro data or are due to incomplete understanding of in vivo drug disposition.
Suggested decision metric should be “Does it modulate dose requirements?”
Topic 2: Verification of drug specific input parameters
During drug development, it is best practice to have a quantitative understanding of the contribution of the various pathways involved in a drug's ADME.	All companies should be encouraged to present “Quantitative Drug Disposition Diagrams” as part of their Clinical Pharmacology documentation. A statement should be developed, supported by appropriate rationale that explains the expectation of IV data as a key element in the quantitative mechanistic understanding of drug disposition.
Given the discussion around sensitivity analysis, it would appear that a general guidance could be developed around the choice of parameters and range of values included in sensitivity analysis.	General guidance should be developed around the choice of parameters and range of values included in sensitivity analysis based on the physico chemical properties of a molecule, and the experimental system. Companies should systematically document the relationship between in vitro Ki, KI, and in vivo DDI results to inform the range for sensitivity analysis for perpetrators.
Although the focus of the discussion was on sensitivity analysis to address input parameter uncertainty, attention should also be paid to the experimental systems themselves and the possibility of improving confidence in key input parameters.	Companies should consider whether it is better to resolve uncertainty experimentally where this is possible, rather than addressing this issue solely through sensitivity analysis.
There are a number of gaps to be filled before PBPK models of enzyme induction will be viewed as sufficiently reliable to support waiver of in vivo studies for a potential perpetrator within the European regulatory system. Further development in this area would be welcomed.
Topic 3: Best practice for qualification of system models
It was acknowledged that system model qualification is an area that has not been extensively discussed within the PBPK community and that standards are not currently agreed. Further progress to establish best practice is needed.	A follow-up meeting should be convened to address PBPK system model validation and explore solutions (such as open source validation sets), while considering the needs of software companies.
There is some mismatch between the terminology used within PBPK and computational science communities: “qualification” or “verification” vs. “validation.”	A working group should be established to compare the use of system model evaluation and assessment terminology in other related fields such as statistics, mathematics, and modeling and simulation with the aim of reaching agreement on the terminology and definitions for PBPK.
The meeting identified that each software provider has developed internal systems to evaluate and track the reliability of their system models and associated libraries.
In the field of PBPK, open source software published with the training datasets utilized presents a challenge for software providers wishing to protect their intellectual property. Other solutions, such as open source validation datasets against which commercially available software are validated (for a specific condition of use), could potentially serve the same purpose.
Topic 4: What should a PBPK report look like?
The model development “story” should be presented but ought to be fit for purpose and sufficiently detailed to facilitate regulatory review without being overly detailed.	A fit for purpose model development story should be included in the PBPK report. A clear statement of the assumptions underlying the modeling, the input parameters and the relationship of the parameters and the appropriateness of these assumptions, as well as the impact on the predictions, should also be included.
For regulatory submissions it is important to contextualize the purpose of the PBPK model.	A clear account of the purpose of the modeling effort should be included in the report.
The acceptability of the PBPK model in terms of targets for successful prediction of clinical data should be interpreted within the context of therapeutic index.	Relevant targets for successful prediction vs. actual clinical data should be set with reference to the therapeutic index of the drug.
Development of a PBPK model during a drug development program can be helpful in promoting a full and integrated understanding of a drug's quantitative disposition. Alongside this overall objective, it is helpful to develop specific plans for the application of PBPK modeling to clinical pharmacology programs.
Whether PBPK modeling is used or not, dose adjustment for DDIs or other extrinsic and intrinsic factors should be framed within the context of the therapeutic index.