Thank you for joining us for another episode of the Guidance Recap Podcast. The Guidance Recap Podcast provides highlights for FDA guidance documents straight from the authors. My name is Kylie Haskins, and I am the host for today’s podcast. I am a member of the Guidance, Policy, and Communications Team in the Office of Translational Sciences here at the FDA. In today’s episode, I am excited to be talking with Dr. Xinning Yang, who is a Policy Lead in the Office of Clinical Pharmacology in CDER. Dr. Yang will be sharing some thoughts with us on the recently published, drug interaction final guidance titled, “In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions.” Welcome, Dr. Yang! Thank you for speaking with us today.
Can you explain to the audience why the in vitro drug interaction studies described in this guidance are important and provide some of the reasons that FDA issued this document?
Many Americans take more than one medication, including both prescription and over-the-counter drugs. Unanticipated, unrecognized, or mismanaged drug-drug interactions, also known as DDIs, can change the systemic exposure of a drug when taken in combination with specific other drugs. The change in exposure may alter the drug’s effectiveness or cause unexpected side effects. DDIs are a significant but avoidable cause of morbidity and mortality associated with prescription drugs.
Once a drug has been taken by a patient, the drug goes through the processes of absorption, distribution, metabolism, and/or excretion that influence the concentration of the drug at the site of action. A coadministered drug can alter the processes by which another drug is absorbed, distributed, metabolized or eliminated, and this is what we refer to as a drug interaction. It is not feasible to evaluate interaction with every potential coadministered drug in clinical trials during drug development. Therefore, evaluation often starts with in vitro experiments to identify mechanisms underlying potential DDIs. Results of in vitro experiments, along with clinical pharmacokinetic (PK) data, provide mechanistic understanding and inform risk-based approaches that can determine the need for clinical DDI studies. FDA issued this guidance to provide best practices for in vitro studies evaluating DDIs mediated by Cytochrome P450 enzymes (called as CYP) and transporters, and to provide recommendations for determining how in vitro results can inform clinical DDI risk evaluation.
For listeners less familiar with this area, can you explain how sponsors can use the information obtained from in vitro drug interaction studies to inform the need for clinical studies and to communicate DDI risk in the labeling?
In vitro studies help identify unknown DDI risks associated with an investigational drug to determine if further clinical studies are needed. DDI may arise from either other medications affecting an investigational drug, as well as from the investigational drug affecting other medications. Before conducting clinical studies, sponsors should conduct in vitro experiments to assess whether a drug is metabolized by enzymes, and if so, which enzymes may be responsible for the drug’s metabolism, often focusing on CYP enzymes. For example, if a drug is found to be primarily metabolized by one type of CYP enzyme in vitro, the drug concentration in humans is anticipated to substantially increase when a patient takes the drug along with another medication that inhibits that CYP enzyme. Taking these two drugs together may lead to more side effects. The drug concentration can also significantly decrease when it is given along with a medication that induces that CYP enzyme, which may result in decrease or loss of efficacy of the drug. Therefore, it is important to plan and conduct further clinical studies to characterize the DDI and inform drug labeling on how to mitigate the DDI. Similarly, in vitro studies may be conducted to identify whether an investigational drug is a substrate of certain drug transporters. To identify the possibility of an investigational drug affecting other medications, in vitro studies are often needed to evaluate whether the drug can inhibit or induce certain CYP enzymes or transporters. These results, along with clinical PK data, are critical to predict the possibility of DDI when the drug is given with other medications that are metabolized or transported by those CYP enzymes or transporters. Such prediction will inform the need, timing, and design for further clinical DDI evaluations.
Prescription drug labeling should include a summary of the drug interaction information that is essential for the safe and effective use of the product. Even in the absence of clinical DDI studies, in vitro study results may still be sufficient to justify clinical intervention to mitigate anticipated DDI risks between the investigational drug and other medications.
What is the role of in silico modeling in the evaluation of drug-drug interactions?
If an in vitro study identifies the potential for a DDI with an investigational drug, the sponsor needs to consider further evaluating the DDI potential by conducting clinical studies or choose to utilize in silico modeling to gain additional information. In this context, in silico modeling refers to simulation studies conducted with computer models adequately validated for the purpose of use. A commonly used in silico modeling approach is called physiologically-based pharmacokinetic modeling or PBPK modeling, which incorporates information generated from in vitro experiments and often also in vivo PK data. Compared to some simple equations that only offer qualitative prediction, meaning the equations show a possible DDI potential or not – a yes or no type question, in silico modeling has the advantage of providing more quantitative prediction for what happens in humans. Thus, an in silico modeling approach can better inform the decision to further evaluate a DDI, for example, by potentially better informing whether a study is needed or how the study should be conducted. In general, in silico modeling expands the utility of in vitro data and possibly reduces the time and resources required for DDI evaluation during drug development. In some situations where we have accumulated lot of experience, in silico models may replace clinical DDI studies. Because this science is evolving, new uses of in silico methods to predict DDIs in place of clinical studies are continuously being considered by the FDA. The Agency encourages sponsors to discuss issues and considerations related to the use of in silico models with the FDA early in development.
Can you give us a brief overview of the evolution of this guidance?
A previous draft guidance was published in February 2012 and contained both clinical and in vitro DDI content. FDA received comments on the guidance that it was long and difficult to follow. Thus, in October 2017, we split the guidance into 2 draft guidances – covering in vitro and clinical DDIs separately, along with some other updates. Since then, we have published revised draft versions of both guidances to address the public comments we received. Now in 2020, we recently published the final version of both of these guidances. For the in vitro DDI final guidance, there are a few changes from the 2017 draft version, related to evaluation of the DDI potential of investigational drugs as CYP inducers or transporter inhibitors, assessment of the DDI potential of drug metabolites as substrates or perpetrators of CYP enzymes, and certain considerations for in vitro experiments.
How do you anticipate this guidance will affect external and internal stakeholders?
FDA received a number of public comments for the 2017 draft in vitro DDI guidance. Revisions were made wherever needed in the final version of the document to address some of these comments and provide more clarity. We anticipate that external stakeholders will appreciate the specific recommendations and best practices provided in the guidance, and the information will help drug developers to utilize the data generated from in vitro studies to guide further DDI evaluation, such as whether in vivo studies are needed, when to conduct such studies if needed, and how the studies should be designed. Similarly, we believe that FDA staff will welcome the guidance because it offers consistent recommendations that FDA reviewers can provide to sponsors when questions or concerns arise regarding DDI evaluation.
This guidance contains an incredible amount of useful and important information. What are a couple of key items that you especially want listeners to remember?
There have been significant advances in science underlying drug metabolism and pharmacokinetics, and this progress has greatly improved our understanding of DDI mechanisms and our ability to identify DDI risk. As a part of this risk evaluation, it is important to conduct in vitro experiments early during drug development. Fully utilizing the information will help derive strategies to evaluate DDI potential of an investigational drug during development. Another point I’d like to bring the audience’s attention to is that the guidance focuses on CYP or transporter-mediated DDIs, as these are the most common DDI mechanisms. There are also other mechanisms of potential interaction, such as protein displacement, gastric pH change, chelation, and modulation of UGT enzymes. Potential for these DDIs needs to be considered when applicable, and FDA will continue working on development of guidance or policy to address some of these types of DDIs to aid drug development.
Dr. Yang, thank you for taking the time to share your thoughts on the in vitro drug interaction studies final guidance. We have learned so much from your experience and insights in this area, and we appreciate all the hard work that you do to ensure the safe and effective use of the drugs and biologics we regulate. We would also like to thank the guidance working group for writing and publishing this final guidance.
To the listeners, we hope you found this podcast useful. We encourage you to take a look at the snapshot and to read the final guidance.