Since 2006, when the U.S. FDA published a draft guidance on drug interaction studies, it has expected new drug applications to include in vitro data on the interactions of new drug candidates with drug transporters. The rationale is that if a new drug is a substrate of a transporter (e.g., an efflux transporter such as P-glycoprotein [P gp/MDR1]), its absorption, metabolism, distribution, or elimination could be affected by a co-medication that is a substrate or inhibitor of the same transporter.
Conversely, if a new drug is an inhibitor of a transporter (e.g., an uptake transporter such as OATP1B1), it could alter the disposition of a co-dosed drug (e.g., a statin) that is a substrate of the same transporter.
In either case, the consequences could be loss of efficacy and/or compromised safety of the victim drug. Not only is in vitro drug-transporter interaction data “expected” (in fact, review of some NDAs has been delayed until such data is included), but an implicit assumption is that they should be performed in such a way that the results predict what will happen in patients. Surprisingly, this is not always as easy as it sounds.
This article will cover some of the common mistakes to avoid in order to improve the translation of in vitro transporter interaction data to the clinic.
Drug developers are certainly complying with the FDA’s expectations; of the 24 oral or IV small molecule drugs approved by the agency in 2012, 18 have labels that reference transporters. Not only that, fewer and fewer labels reference clinical transporter data because the data is being generated in vitro instead. It seems clear that the FDA is pushing so hard to get in vitro transporter data in order to fill in the current gap in our knowledge about the clinical importance of transporter-mediated drug-drug interactions (DDIs) and the translatability of in vitro test systems.
Within a few years, one hopes that they will have accumulated sufficient data to determine whether the intelligent use of vitro transporter assays can make drug development both faster and safer when it comes to investigating the potential for clinical DDIs.
In the meantime, it is imperative to take into account a few important considerations that can mean the difference between a true result and a false negative (drug regulators have extremely low tolerance for these) or false positive (which will eventually be sorted out later, in clinical trials if necessary). These include:
- Properties of the test compound
- The optimal test system (depending on, e.g., the properties of the test compound)
- Test conditions