Abstract

Physiologically- based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–druginteractions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made withsufficient confidence. In the current work, the ability to predict the effect of rifampin, a well- characterized strongCYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow withoptimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4 ,and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate forcompounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies forthree challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parametersensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures shouldbe considered, for example, a mechanistic absorption model instead of a first- order absorption model might bemore appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate thatrifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model.PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates withlimited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused onrifampin, the learnings may apply to other inducers.

By Micaela B. Reddy , Tamara D. Cabalu, Loeckie de Zwart, Diane Ramsden ,Martin E. Dowty , Kunal S. Taskar, Justine Badée, Jayaprakasam Bolleddula , Laurent Boulu, Qiang Fu , Masakatsu Kotsuma, Alix F. Leblanc , Gareth Lewis, Guiqing Liang, Neil Parrott, Venkatesh Pilla Reddy, Chandra Prakash, Kushal Shah, Kenichi Umehara,Dwaipayan Mukherjee , Jessica Rehmel and Niresh Hariparsad