Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically Based Pharmacokinetic Modeling.

Abstract

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HV), depending on the interplay between drug-drug and disease (drug-drug-disease interaction, DDDI). Physiologically based pharmacokinetic (PBPK) modelling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modelling in severe CKD population is still low when non-renal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: 1) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) PK and DDI; and 2) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and non-renal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PK in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HV within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD disease effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in severe CKD patients was predicted to be similar to that in HV. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.