Drug-drug interaction (DDI) studies are an important part of the regulatory drug development process. The US FDA, EMA and Japanese PMDA have issued guidance on the conduct of these DDI studies, however, a harmonised guidance is currently under review (ICH M12) and is expected to be adopted in April 2024. The risk of DDI can be assessed in vitro and data analysed using models of varying complexity. As identifying potential safety issues is the main purpose of these studies, many of the models have been developed to over-estimate the risk of DDI .This, however, needs to be balanced with the risk of false positives and the potential of unnecessary clinical DDI studies which are expensive and time consuming to perform. It is essential, therefore, to choose the most accurate model when analysing data from in vitro DDI studies.
In this poster, we focus on:
- an evaluation of the various models for predicting clinical CYP3A4 induction risk from in vitro data
- a comparison of basic R3 values (with and without a scaling factor) with correlation methods (relative induction score (RIS) and Imax/EC50 values)
Read our poster to learn more about our research!