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The co-administration of drugs is commonplace for therapeutic proteins (TP). Yet, drug developers may face delays or ambiguity in preparing for clinical trials if they don’t plan for preliminary analysis of drug-drug interactions (DDI). Fortunately, the U.S. Food and Drug Administration (FDA) released in August 2020 new draft guidance for DDI assessment of TPs. The resulting shift in DDI experimental design and criteria will require teams to take a risk-based testing approach to accurately predict the effects of DDIs during future in-human trials.
DDI studies provide a preliminary analysis and risk potential for drug developers compiling Investigational New Drug (IND) applications and candidates of Biologics License Applications (BLA), though the new guidance is recommended but not required. By applying the insights provided by this guidance, developers can achieve a more streamlined and accurate evaluation, including the appropriate studies, data analysis and interpretation of data to predict potential DDIs.
Tapping into the decision tree
With so many pathways available in drug discovery and development, it can be difficult to identify which testing methods will be adequate, scientifically sound and justifiable for each molecule. Often, laboratories use in vitro testing as the primary testing method during preclinical DDI studies. Physiologically-Based Pharmacokinetic (PBPK) studies are also an option when dynamic models are required. The latest guidance recognizes the multiple pathways as a challenge for drug developers and provides a decision tree to support study combinations and specific requirements at hand.
This resource is a crucial reference as molecules reach pivotal developmental stages. Testing laboratories and drug developers will need to make sure the study design is modified to meet the recommendations and follow this decision tree. This mainly entails fine-tuning technical aspects to ensure research quality and adjust criteria.
Overall, developers and their testing partners can use the FDA’s decision tree to more easily create a relevant test plan that executes a scientifically-sound program. This addition allows developers to be less concerned about their study director’s ability to respond to unexpected results or make difficult decisions when they arise. On top of simplifying the study design process to identify DDI risks, the pathways available are also more apparent, given the additional options for phenotyping studies, such as the range of enzymes.
Digging into the guidance
DDI testing typically relies on in vitro test methods rather than in vivo methods to provide a more accurate prediction of human risks because alternative test systems may vary in metabolization. In vitro studies often use human-derived tissue, cells or enzymes to garner results and determine a reasonable path forward. Other alternatives for phenotyping studies in the decision tree further enhance the goal to customize DDI studies specific to each molecule’s potential effects and risks.
During DDI testing, scientists assign molecules’ perpetrator’ and ‘victim’ roles to understand their influence on other drugs and vice versa. Scientists conduct such studies to represent human metabolization and achieve a more accurate picture of the interactions.
If traditional cytochrome P450 (CYP) enzymes cannot determine the major metabolic components, in either victim or perpetrator role, testing can still lean on alternative enzymes. This additional breadth of options has opened up testing in new ways and allows laboratories the opportunity to adjust methods as needed.
Testing laboratories’ approaches are constantly evolving, and as such, some molecules with narrow therapeutic windows or associated with polymorphic enzymes could gain additional attention from investigators about DDI risks. Regulators are more familiar with DDI mechanisms in small molecules, but there is still plenty of work to do on large molecules and other novel therapeutics. By investing in accurate inputs and thoughtful design, results can determine the major metabolic players for the test compound and focus on how the drug will perform in subsequent studies.
Achieving the most accurate insights
Developers hoping to gather the most revealing information about their TP before moving their project forward must recognize the value of investing in an adequate study setup. The more relevant information developers share with laboratory testing partners, the better investigators will be able to determine the applicable approaches. Drug developers should prioritize providing ample evidence about the principal routes of elimination, the contribution of enzymes and transporters to the drug disposition, and the drug’s effects on enzymes and transporters.
DDI science and drug co-administration will only continue to advance, and the methods used to understand potential effects must keep pace. For now, this DDI guidance is non-binding, but taking the opportunity to be on the leading edge of these insights will put drug developers in a better position in the long-term, both with their molecule and in the relationship with their laboratory testing partners.
Peter Wang joined WuXi AppTec in 2008 and currently serves as senior director of drug metabolism and niotransformation in the company’s lab testing division. In this role, he provides operational, client, and project management support. Dr. Wang has extensive experience in metabolite identification, having worked in in vitro and in vivo DMPK-related fields for more than 15 years. Dr. Wang received his Ph.D. in Organic Chemistry co-educated by Shanghai Institute of Materia Medica, Chinese Academy of Sciences, and the Institute of Natural Medicine, Japan. From 2001 to 2005, he conducted post-doctoral research in Phase II drug metabolism at the University of Florida. Wang has published over 40 peer-reviewed and review articles.