Evaluating drug-drug interactions (DDI) of investigational drugs is an important part of the regulatory drug development process. Current guidance on the conduct of DDI studies has been issued by the US FDA, EMA and Japanese PMDA, and future harmonised guidance (ICH M12) is expected to be adopted in April 2024. The risk of clinical DDI can be assessed using in vitro assays combined with models of varying complexity to analyse and contextualise the data. The main purpose of these in vitro DDI studies is to identify potential safety issues. As a result, many of the models have been developed to over-estimate the risk of DDI in order to reduce the chance of false negatives. However, this needs to be balanced with the risk of false positives and the possibility of unnecessary clinical DDI studies which are costly and time consuming to perform. Therefore, choosing the most accurate model when analysing the data from in vitro DDI studies is an important consideration.
At the 2022 Joint DMDG/GMP/SPS conference in Amsterdam, Cyprotex presented research in the area of cytochrome P450 (CYP) induction and compared various models for predicting clinical CYP3A4 induction risk. Six compounds were selected which were classified in vivo as non-inducers (clinical AUCR >0.8) or moderate inducers (clinical AUCR =0.2-0.5). The research compared basic R3 values (with and without a scaling factor) with correlations methods (relative induction score and Imax,u/EC50 values) using both mRNA and activity endpoints.
We found that both the R3 calculations (using a 10 x safety factor) and the correlation methods showed no false negatives. The models, however, differed in terms of their false positive rate. The calculation of d values for R3 calculations was found to significantly improve the prediction of AUCR compared to assuming a d value of 1. For the correlation methods, relative induction score (RIS) provided a more accurate DDI prediction relative to the Imax,u/EC50 method. In addition, it was found that restricting fu to 0.01 can contribute to a significant over prediction of clinical induction risk for highly bound compounds.
To learn more, read our poster, or contact us to discuss your DDI study.