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Deciding on Which Drug-drug Interactions to Evaluate in the Clinic

Drug-drug interactions are a critical research area in pharmaceutical drug development. One of the most tragic examples of drug-drug interactions was the antihistamine terfenadine. Terfenadine (also known as Seldane) was a common antihistamine intended to block the effects of an allergic rhinitis. Upon administration terfenadine is metabolized to fexofenadine by the cytochrome P450 3A4 isoform. Unfortunately several people who took terfenadine concomitantly with ketoconazole, an antifungal, suffered cardiac problems including torsades de pointes which often leads to death. Thus in November 1999, the FDA released a guidance on recommendations for evaluating drug-drug interactions during drug development. A second guidance was released in draft form in September 2006.

Metabolic drug interactions generally fall into two groups: inhibition and induction. Inhibition results when one drug inhibits the metabolism of another. In the previous example, ketaconazole inhibited the metabolism of terfenadine. Thus, inhibition is a reduction in the metabolic capacity of the body. In contrast, induction results when one drug increases the metabolism of another. For example, the antibiotic rifampin induces CYP3A4. This means that higher CYP3A4 activity levels are present when rifampin is administered. That will result in increased metabolism of any drugs that are susceptible to CYP3A4.

The testing paradigm suggested by the FDA involves in vitro testing followed by selective clinical evaluations. NME stands for new molecular entity, or the drug that is being developed.

Drug-drug interaction decision tree
Drug-drug interaction decision tree

The simplified steps for the testing paradigm are:

  1. Perform in vitro inhibition testing at therapeutic concentrations.
  2. Perform in vitro induction testing a therapeutic concentrations.
  3. If signals are detected in either #1 or #2, conduct appropriate clinical tests.

Clinical studies are indicated when therapeutic levels of the investigational drug result in inhibition or induction. These clinical studies should be designed with simplicity in mind. The key to effective clinical drug-drug interaction studies is to focus on the primary objective of evaluating whether a clinical drug interaction exists. In general, these studies involve a comparison of total exposure (AUC) as a surrogate for changes in clearance. These studies also include 2 key arms, one with the investigational drug alone, and a second with both the investigational drug and another drug that acts as an inhibitor, substrate, or inducer. Then the AUC from the combination treatment is compared to the AUC from the single treatment and the differences are quantified.

Changes greater than 5-fold are considered strong interactions. Changes between 2 and 5-fold are considered moderate interactions. And changes less than 2 fold are considered weak interactions. Based on these general categories, drug interaction information can be incorporated into the drug label.

Cancer treatments often use regimens of multiple drugs being administered simultaneously, thus raising their potential for DDIs. Indeed, DDIs that cause unmanageable, severe adverse effects have led to restrictions in clinical use and even drug withdrawals from the market. Read this case study to learn how pharmacometrics was used to assess the risk of a DDI for a new combination cancer treatment.


Nathan Teuscher
By: Nathan Teuscher
Dr. Teuscher has been involved in clinical pharmacology and pharmacometrics work since 2002. He holds a PhD in Pharmaceutical Sciences from the University of Michigan and has held leadership roles at biotechnology companies, contract research organizations, and mid-sized pharmaceutical companies. Prior to joining Certara, Dr. Teuscher was an active consultant for companies and authored the Learn PKPD blog for many years. At Certara, Dr. Teuscher developed the software training department, led the software development of Phoenix, and now works as a pharmacometrics consultant. He specializes in developing fit-for-purpose models to support drug development efforts at all stages of clinical development. He has worked in multiple therapeutic areas including immunology, oncology, metabolic disorders, neurology, pulmonary, and more. Dr. Teuscher is passionate about helping scientists leverage data to aid in establishing the safety and efficacy of therapeutics.

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