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New FDA Requirement to Support Labeling on DDIs in the Pediatric Population

The effects of drug-drug interactions (DDIs) could differ in the pediatric population compared to adults. The resulting changes in exposure of the victim drug, because of a DDI, at a metabolism and/or transport level could be larger or smaller, depending on the relative contribution of the affected enzyme/transporter to the disposition of the drug in children.  

A very well-known example of this is the CYP1A2 probe caffeine, which in neonates is mainly eliminated through renal excretion, and for whom it is used as respiratory stimulant. In the latter case, the dominance of the respective clearance mechanisms (CYP1A2 in adults versus renal in neonates) reflects the CYP1A2 ontogeny.

The impact of a drug as an inhibitor or inducer could be different at therapeutic drug exposures for pediatric doses. In most cases, the exposure in the pediatric population is likely to be like that in adults, but there may be exceptions to the rule depending on the therapeutic window. Furthermore, the DDI potential at a solubility/absorption level can potentially be different in the pediatric population due to smaller gastric fluid volume in relation to administered dose. These are just some of the factors that could lead to differing DDI potential in children and adults.

Labeling suitable for the approved patient populations

The impact of age and maturation on DDIs can be simulated using physiologically-based pharmacokinetic (PBPK) approaches. Mechanistic translation of these effects, informing drug labeling, can provide key support to the prescriber who may not

  • think about the impact of age, or
  • be prepared to consider combinations of drugs if treatment recommendations are only available for adults, or
  • reduce doses to deal with uncertainty, or
  • avoid co-treatment

FDA and EMA requirements

The FDA draft guidance on General Clinical Pharmacology Considerations for Pediatric Studies of Drugs, Including Biological Products published in September 2022, states that “Planning for DDI evaluations should be included as a section of the initial pediatric study plan (iPSP) under Pediatric Pharmacokinetic Studies and should address the impact of DDIs on drug dosing in specific age groups”, “quantitative approaches such as PBPK analyses should be explored to address pediatric DDIs during drug development when differences in DDI are expected.”

At an EU level, guidance has been available for a long time already. The 2006 EU Guideline on the Role of Pharmacokinetics in the development of medicinal products in the paediatric population, states that differences in DDI effects should be explored. But as the guideline came into force in 2007, the recommendations were not fully explored. PBPK approaches were at an early stage of development and not leveraged in applications. Scientifically, many findings on maturation have yet to emerge.

In the EU SmPC guideline from 2009, the format of the SmPC was adapted to the new EU Pediatric regulation. From then on, many pediatric applications were expected. The SmPC guideline requests drug interaction treatment recommendations for the pediatric population, expected similarities in interaction outcomes, etc. Other than following a requirement to state that DDI studies have been performed in adults, this has not yet been extensively implemented. Based on the FDA requirements and the resulting documentation submitted in a pediatric application, it is envisaged that the available EU guidelines will be implemented.

PBPK approaches are highlighted in the FDA guideline as a useful tool to increase understanding of DDIs in the pediatric population. The use of virtual populations to mimic the developmental physiology at different pediatric ages is a powerful approach for making quantitative predictions based on the complex interplay between multiple factors impacting the DDI such as enzymes, transporters, GI physiology, and blood flows, to name just a few. Many “what if” scenarios can be investigated, and parameter uncertainties addressed through sensitivity analysis. An example of how these types of simulations are conducted relates to deflazacort which informed the US labeling on DDIs in DMD patients down 2 years of age.

So, will PBPK use be expanded in pediatric drug development?

Yes, that is likely to be the case. This is mainly due to the new DDI requirements but also in recognition of the benefits of applying a mechanistic quantitative approach to simulate drug exposure in pediatric populations. The optimal solution is to use an integrated approach inclusive of PBPK and population PK (popPK), and exposure-response (E-R) approaches in parallel, supporting dose selection, study design including sampling, as well as the extrapolation concept of the development program. This has been requested previously by regulatory reviewers for the youngest age group and is now likely to be expanded to older children.

Why is this a good idea?

During drug development, we already know the PK characteristics, the interaction potential, and the safety and efficacy of the drug. In many cases, a PBPK model has already been developed and verified in adults using clinical DDI data. Combined with the knowledge on relevant maturational aspects in a virtual pediatric population, the PBPK model can be used to mechanistically assess the DDI outcome in children. Complex scenarios such as these should be simulated at this point to allow the prescriber to make an informed decision. It needs to be recognized that the goal of having efficacious and safe drugs for our young patients also includes assessment of DDIs and should be valid for the full approved population, regardless of age.

To learn more about how PBPK can inform pediatric drug development, please watch this recent webinar.

About the authors

Karen Rowland Yeo, PhD
By: Karen Rowland Yeo, PhD

Since 2002, Karen has led projects relating to the extrapolation of in vitro data to predict in vivo pharmacokinetics in humans. This has included development and implementation of the models into the Simcyp Simulator.  Her specific research interests include physiologically based pharmacokinetic modeling and prediction of drug-drug interactions.

Eva Gil Berglund, PhD
By: Eva Gil Berglund, PhD

Eva is a pharmacist by training and has a PhD in Clinical Pharmacology, both from Uppsala University, Sweden. She has been a Clinical Pharmacology reviewer at the Swedish Medical Products Agency for over 20 years and a Senior Expert for 12 years, working with all types of molecules in marketing applications, clinical trials and scientific advice procedures in the EMA Network of National agencies. Eva joined Certara in 2019 and provides her Clinical Pharmacology experience and Regulatory strategy knowledge in GAP analyses, regulatory stress tests and mock meetings, regulatory interactions, filing and clin pharm response support, pediatric submissions (PIP, PSP, new indications).