Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins.

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Efficacy and safety of zibotentan and dapagliflozin in patients with chronic kidney disease: study design and baseline characteristics of the ZENITH-CKD trial.

BACKGROUND: Sodium-glucose co-transporter 2 inhibitors (SGLT2is) are part of the standard of care for patients with chronic kidney disease (CKD), both with and without type 2 diabetes. Endothelin A (ETA) receptor antagonists have also been shown to slow progression of CKD. Differing mechanisms of action of SGLT2 and ETA receptor antagonists may enhance efficacy. We outline a study to evaluate the effect of combination zibotentan/dapagliflozin versus dapagliflozin alone on albuminuria and estimated glomerular filtration rate (eGFR). METHODS: We are conducting a double-blind, active-controlled, Phase 2b study to evaluate the efficacy and safety of ETA receptor antagonist zibotentan and SGLT2i dapagliflozin in a planned 415 adults with CKD (Zibotentan and Dapagliflozin for the Treatment of CKD; ZENITH-CKD). Participants are being randomized (1:2:2) to zibotentan 0.25 mg/dapagliflozin 10 mg once daily (QD), zibotentan 1.5 mg/dapagliflozin 10 mg QD and dapagliflozin 10 mg QD alone, for 12 weeks followed by a 2-week off-treatment wash-out period. The primary endpoint is the change in log-transformed urinary albumin-to-creatinine ratio (UACR) from baseline to Week 12. Other outcomes include change in blood pressure from baseline to Week 12 and change in eGFR the study. The incidence of adverse events will be monitored. Study protocol-defined events of special interest include changes in fluid-related measures (weight gain or B-type natriuretic peptide). RESULTS: A total of 447 patients were randomized and received treatment in placebo/dapagliflozin (n = 177), zibotentan 0.25 mg/dapagliflozin (n = 91) and zibotentan 1.5 mg/dapagliflozin (n =  179). The mean age was 62.8 years, 30.9% were female and 68.2% were white. At baseline, the mean eGFR of the enrolled population was 46.7 mL/min/1.73 m2 and the geometric mean UACR was 538.3 mg/g. CONCLUSION: This study evaluates the UACR-lowering efficacy and safety of zibotentan with dapagliflozin as a potential new treatment for CKD. The study will provide information about an effective and safe zibotentan dose to be further investigated in a Phase 3 clinical outcome trial. CLINICAL TRIAL REGISTRATION NUMBER: NCT04724837.

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

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

Does the choice of applied physiologically-based pharmacokinetics platform matter? A case study on simvastatin disposition and drug-drug interaction.

Physiologically-based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user-friendly graphical interface, such as Simcyp and PK-Sim. However, evaluations of platform differences and the potential implications for disposition-related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK-Sim and Simcyp as representatives of established whole-body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK-Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20-80 mg), BCRP and OATP1B1 drug-gene interactions (DGIs), and drug-drug interactions (DDIs) when co-administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in-depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK-Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism- and transporter-mediated DGIs and DDIs.