Key Takeaways on the Acceptability of PBPK from the ICH Harmonised Guideline on Drug Interaction Studies

Authors: Garcia-Arieta A

One of the significant challenges faced by pharmaceutical companies is the sometimes unclear guidance on what data regulators want to see included in their submissions and the format in which it should be provided.

On May 21st, the International Council for Harmonisation’s (ICH) Harmonised Guideline on Drug Interaction Studies (M12) was endorsed by the regulatory members of the ICH assembly. While it is worth reviewing this guidance in its entirety, in this blog post I summarize the regulatory acceptance of PBPK in drug-drug interaction (DDI) studies.


What the ICH Harmonised Guideline on Drug Interaction Studies Covers on PBPK

This guideline recognizes the usefulness of PBPK modeling in multiple sections and dedicates a specific section to PBPK (i.e., dynamic mechanistic) models (section 7.5.2 Using PBPK Models to Predict Enzyme or Transporter-Based DDIs).

The guideline describes how mechanistic modeling approaches can be used to:

  1. Characterize the potential for DDIs
  2. Indicate whether a dedicated clinical DDI study should be conducted
  3. Support clinical recommendations in the absence of a clinical DDI study

Recognizing that PBPK models can assist in the evaluation of the DDI potential of an investigational drug and/or a metabolite as an object (victim) or precipitant (perpetrator) of enzyme or transporter-mediated interactions, the guideline also highlights that when PBPK modeling is used to support drug development and regulatory decisions, it is important to justify any model assumptions, the physiological and biochemical plausibility of the model, variability, and measures taken to evaluate the uncertainty in drug-, formulation-, or system-related parameters.

PBPK analysis reports should include a description of the context of use for the model, model structure and development plan, the sources and justifications for both system- and drug-related parameters, and an adequate sensitivity analysis plan. When using predefined models, the software version and any deviations from predefined models should be described.


What the ICH Harmonised Guideline on Drug Interaction Studies Does Not Cover

The guideline does not discuss model verification, validation and reporting. Reference is made to the regional guidelines:

  • Physiologically Based Pharmacokinetic Analyses- Format and Content Guidance for industry. US Department of Health and Human Services, FDA, United States. 2018
  • Guidelines for Analysis Reports Involving Physiologically based Pharmacokinetic Models. PSEHB/PED MHLW, Japan. 2020.
  • Reporting of physiologically based pharmacokinetic (PBPK) modeling and simulation. EMA/CHMP/458101/2016, Europe. 2018
  • Guidance document on the characterization, validation and reporting of Physiologically Based Kinetic (PBK) models for regulatory purposes. 2021

Instead, this guideline describes the utility of PBPK modeling for the evaluation of DDIs, with the understanding that models should be demonstrated as fit-for-purpose. The guideline also highlights specific best practices for use of PBPK modeling for the evaluation of DDIs.


How PBPK Can Be Used for Evaluating DDIs

The guideline recognizes the following applications of PBPK for the evaluation of CYP-mediated DDIs:

  1. Help select the key CYP-mediated DDI studies for a development program.
  2. Inform the study design for clinical DDI studies.
  3. Explain PK observations, due to genetic polymorphism.
  4. Predict DDI on the drug as a potential object of CYP-mediated DDIs with a less potent precipitant after the model has been confirmed with index precipitants.
  5. Predict clinically relevant DDI scenarios, such as the effect following multiple dose administration of the object drug if only single dose administration is evaluated in a clinical DDI study.
  6. Support the lack of clinical DDI potential of the drug as a potential precipitant of CYP-mediated DDIs.
  7. Predict DDI effects of the drug as a potential precipitant of CYP-mediated DDIs under different dosing regimens after the model has been confirmed with a sensitive index substrate.

The guideline also recognizes the following applications of PBPK to the evaluation of transporter mediated DDIs:

  1. Support the initial study design for clinical DDI studies when a DDI liability is identified.
  2. Explain PK observations, such as PK differences that are due to genetic polymorphism (e.g., OATP1B1) when the drug is a potential object of transporter-mediated DDIs.
  3. Explore involvement of specific transporters in a drug’s ADME and DDI liability.
  4. Support negative DDI prediction when the drug is an in vitro inhibitor for a basolateral uptake transporter (e.g., OAT1/3).
  5. Evaluate the effect of the drug as a potential inhibitor of transporter-mediated DDIs on the PK of a transporter substrate with a well characterized pathway.


Key Takeaways from the ICH Guideline

The recent endorsement of the ICH Harmonised Guideline on Drug Interaction Studies (M12) underscores the critical role of PBPK modeling as an essential tool for predicting and evaluating enzyme- or transporter-based DDIs. PBPK models help characterize potential DDIs, determine the necessity for clinical DDI studies, and support clinical recommendations when such studies are absent. The guideline also emphasizes the importance of justifying model assumptions, ensuring physiological and biochemical plausibility, and addressing variability and uncertainty in PBPK analyses.

While this is a welcome roadmap, there are still many paths that are less efficient and more time-consuming than others. Partnering with regulatory and modeling experts who have extensive experience in the application of PBPK modeling to enhance the precision of DDI predictions, optimize study designs, and facilitate regulatory approvals will point you in the direction you need to go.

Our consultants’ thoughtful approach to assessing your current data packages, outlining a PBPK modeling strategy, and delivering reports not only aids in better understanding and mitigating DDI risks but also supports informed decision-making throughout the drug development process.

If you need support in modeling and simulating DDIs or in designing your regulatory approach, we are here to help.