Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone

Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO-DKD and FIDELIO-DKD

AIMS

To perform dose-exposure-response analyses to determine the effects of finerenone doses.

MATERIALS AND METHODS

Two randomized, double-blind, placebo-controlled phase 3 trials enrolling 13 026 randomized participants with type 2 diabetes (T2D) from global sites, each with an estimated glomerular filtration rate (eGFR) of 25 to 90 mL/min/1.73 m2 , a urine albumin-creatinine ratio (UACR) of 30 to 5000 mg/g, and serum potassium ≤ 4.8 mmol/L were included. Interventions were titrated doses of finerenone 10 or 20 mg versus placebo on top of standard of care. The outcomes were trajectories of plasma finerenone and serum potassium concentrations, UACR, eGFR and kidney composite outcomes, assessed using nonlinear mixed-effects population pharmacokinetic (PK)/pharmacodynamic (PD) and parametric time-to-event models.

RESULTS

For potassium, lower serum levels and lower rates of hyperkalaemia were associated with higher doses of finerenone 20 mg compared to 10 mg (p < 0.001). The PK/PD model analysis linked this observed inverse association to potassium-guided dose titration. Simulations of a hypothetical trial with constant finerenone doses revealed a shallow but increasing exposure-potassium response relationship. Similarly, increasing finerenone exposures led to less than dose-proportional increasing reductions in modelled UACR. Modelled UACR explained 95% of finerenone's treatment effect in slowing chronic eGFR decline. No UACR-independent finerenone effects were identified. Neither sodium-glucose cotransporter-2 (SGLT2) inhibitor nor glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment significantly modified the effects of finerenone in reducing UACR and eGFR decline. Modelled eGFR explained 87% of finerenone's treatment effect on kidney outcomes. No eGFR-independent effects were identified.

CONCLUSIONS

The analyses provide strong evidence for the effectiveness of finerenone dose titration in controlling serum potassium elevations. UACR and eGFR are predictive of kidney outcomes during finerenone treatment. Finerenone's kidney efficacy is independent of concomitant use of SGLT2 inhibitors and GLP-1RAs.

The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone

Finerenone, a selective and nonsteroidal antagonist of the mineralocorticoid receptor, has received regulatory approval with the indication of cardiorenal protection in patients with chronic kidney disease associated with type 2 diabetes. It is rapidly and completely absorbed and undergoes first-pass metabolism in the gut wall and liver resulting in a bioavailability of 43.5%. Finerenone can be taken with or without food. The pharmacokinetics of finerenone are linear and its half-life is 2 to 3 h in the dose range of up to 20 mg. Cytochrome P450 (CYP) 3A4 (90%) and CYP2C8 (10%) are involved in the extensive biotransformation of finerenone to pharmacologically inactive metabolites, which are excreted via both renal (80%) and biliary (20%) routes. Moderate or severe renal impairment, or moderate hepatic impairment result in area-under-the-curve increases of finerenone (< 40%), which do not require a dose adjustment per se, as the starting dose is based on estimated glomerular filtration rate (eGFR) and titrated according to serum potassium levels and eGFR decline. No relevant effects of age, sex, body size or ethnicity on systemic finerenone exposure were identified. Modulators of CYP3A4 activity were found to affect finerenone exposure, consistent with its classification as a sensitive CYP3A4 substrate. Serum potassium should be monitored during drug initiation or dosage adjustment of either a moderate or weak CYP3A4 inhibitor or finerenone, and the dose of finerenone should be adjusted as appropriate. Its use with strong inhibitors is contraindicated and strong or moderate inducers of CYP3A4 should be avoided. Finerenone has no potential to affect relevant CYP enzymes and drug transporters.

Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib

INTRODUCTION

Pyrotinib is a newly developed tyrosine kinase inhibitor whose in vivo clearance relies heavily on cytochrome P450 3A4 (CYP3A4) activity. Clinical trials are ongoing to explore the effects of coadministration with CYP3A4 perpetrators on pyrotinib exposure. The present study aims to utilize physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A4-based drug interactions of pyrotinib.

METHODS

Pyrotinib PBPK model was developed in the PK-Sim® multicompartmental physiology structure. Physiochemical parameters were obtained from the literature, and clearance-related parameters were optimized by fitting clinical single-dose pharmacokinetic data. Pharmacokinetic parameters from the model output were compared with the observed data to validate the model predictive performance. Using validated CYP3A4 perpetrator models, we conducted PBPK simulations for drug interactions in a virtual population to explore the impacts of comedication with these perpetrators.

RESULTS

The PBPK model accurately describes pyrotinib single- and multi-dose pharmacokinetics. The model also predicts dramatic exposure change of pyrotinib in the presence of itraconazole and rifampicin, though the impact of rifampicin is somewhat underestimated. According to model predictions, coadministration with typical potent or moderate CYP3A4 perpetrators increases pyrotinib concentration by over sixfold, extinguishing the possibility of dose adjustment for pyrotinib. A weak CYP3A4 inhibitor has minimal influence on pyrotinib pharmacokinetics.

CONCLUSION

PBPK modeling provides valuable information to avoid irrational medication when receiving pyrotinib chemotherapy.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Hyperkalemia by eplerenone or esaxerenone in the presence or absence of clarithromycin in hypertensive patients: a retrospective observational cohort study

OBJECTIVE

Mineralocorticoid receptor antagonists (MRAs), eplerenone and esaxerenone, cause hyperkalemia dose-dependently. We investigated the cytochrome P450 3A4-mediated drug-drug interaction between the MRAs and clarithromycin.

METHODS

This retrospective observational study included adult hypertensive patients with MRA plus clarithromycin or MRA alone with a propensity score matching (1:1). The difference in serum potassium level (ΔK, maximum level - baseline level) between groups was compared using the Mann-Whitney U -test. Linear regression analysis was used to detect variables that correlated with ΔK in patients with MRA plus clarithromycin.

RESULTS

After propensity score matching (each nine patients), serum potassium level was elevated after treatment with MRA plus clarithromycin [4.3 (3.5 to 5.1) meq/l to 4.9 (4.0 to 5.5) meq/l, P  = 0.0234] and MRA alone [4.3 (4.0 to 4.7) meq/l to 4.6 (4.4 to 5.2) meq/l, P  = 0.0469]. Although there was no significant difference in ΔK between groups [MRA plus clarithromycin: 0.5 (0.1 to 1.1) meq/l vs. MRA alone: 0.3 (0.1 to 1.2) meq/l, P  = 0.7231], ΔK was significantly higher in esaxerenone plus clarithromycin than in esaxerenone alone [0.6 (0.5 to 1.1) meq/l vs. 0.1 (0.1 to 0.2) meq/l, P  = 0.0495]. Conversely, clarithromycin did not show a significant effect on ΔK in patients with eplerenone [0.4 (-0.2 to 1.2) meq/l vs. 0.8 (0.1 to 1.3) meq/l, P  = 0.5745]. A positive correlation was found between ΔK and age in patients with MRA plus clarithromycin ( y  = 0.03 ×  x - 1.38, r  = 0.71, P  = 0.0336).

CONCLUSION

The drug-drug interaction between MRAs and clarithromycin was evident, particularly in esaxerenone. Serum potassium levels should be closely monitored in older patients.

Molecular Insights into the Heterotropic Allosteric Mechanism in Cytochrome P450 3A4-Mediated Midazolam Metabolism

Cytochrome P450 3A4 (CYP3A4) is the main P450 enzyme for drug metabolism and drug-drug interactions (DDIs), as it is involved in the metabolic process of approximately 50% of drugs. A detailed mechanistic elucidation of DDIs mediated by CYP3A4 is commonly believed to be critical for drug optimization and rational use. Here, two typical probes, midazolam (MDZ, substrate) and testosterone (TST, allosteric effector), are used to investigate the molecular mechanism of CYP3A4-mediated heterotropic allosteric interactions, through conventional molecular dynamics (cMD) and well-tempered metadynamics (WT-MTD) simulations. Distance monitoring shows that TST can stably bind in two potential peripheral sites (Site 1 and Site 2) of CYP3A4. The binding of TST at these two sites can induce conformational changes in CYP3A4 flexible loops on the basis of conformational analysis, thereby promoting the transition of the MDZ binding mode and affecting the ratio of MDZ metabolites. According to the results of the residue interaction network, multiple allosteric communication pathways are identified that can provide vivid and applicable insights into the heterotropic allostery of TST on MDZ metabolism. Comparing the regulatory effects and the communication pathways, the allosteric effect caused by TST binding in Site 2 seems to be more pronounced than in Site 1. Our findings could provide a deeper understanding of CYP3A4-mediated heterotropic allostery at the atomic level and would be helpful for rational drug use as well as the design of new allosteric modulators.

Effects of Finerenone, a Novel Nonsteroidal Mineralocorticoid Receptor Antagonist, on Cardiovascular Disease, Chronic Kidney Disease, and Blood Pressure

PURPOSE OF REVIEW

Finerenone, an FDA-approved nonsteroidal mineralocorticoid receptor (MR) antagonist, has been evaluated in context of chronic kidney disease (CKD) and associated cardiovascular disease (CVD). In this review, we summarize pre-clinical and clinical studies focused on the impact of finerenone on these disease processes.

RECENT FINDINGS

Activation of the MR upregulates genes encoding for facilitators of tissue damage. Finerenone binding to a helix domain in this receptor inhibits receptor function. Studies in murine models of kidney disease, heart failure, hypertension, and vascular injury demonstrate significant protective effects of finerenone against further disease progression, as well as association with reduced oxidative stress, inflammation, and fibrosis. Phase 1-3 clinical trials with finerenone show safety and efficacy in improving renal and cardiovascular outcomes in patients with CKD. Research thus far encourages the addition of finerenone to the standard of care for certain CKD patients, especially those especially at risk for or with pre-existing cardiovascular disease. Continued study of the effect of finerenone in diverse patient populations and different disease states is needed.

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone

Finerenone is a nonsteroidal, selective mineralocorticoid receptor antagonist that recently demonstrated its efficacy to delay chronic kidney disease (CKD) progression and reduce cardiovascular events in patients with CKD and type 2 diabetes. Here, we report the development of a physiologically‐based pharmacokinetic (PBPK) model for finerenone and its application as a victim drug of cytochrome P450 3A4 (CYP3A4)‐mediated drug‐drug interactions (DDIs) using the open‐source PBPK platform PK‐Sim, which has recently been qualified for this application purpose. First, the PBPK model for finerenone was developed using physicochemical, in vitro, and clinical (including mass balance) data. Subsequently, the finerenone model was validated regarding the contribution of CYP3A4 metabolism to total clearance by comparing to observed data from dedicated clinical interaction studies with erythromycin (simulated geometric mean ratios of the area under the plasma concentration‐time curve [AUCR] of 3.46 and geometric mean peak plasma concentration ratios [C_maxRs] of 2.00 vs. observed of 3.48 and 1.88, respectively) and verapamil (simulated AUCR of 2.91 and C_maxR of 1.86 vs. observed of 2.70 and 2.22, respectively). Finally, the finerenone model was applied to predict clinically untested DDI studies with various CYP3A4 modulators. An AUCR of 6.31 and a C_maxR of 2.37 was predicted with itraconazole, of 5.28 and 2.25 with clarithromycin, 1.59 and 1.40 with cimetidine, 1.57 and 1.38 with fluvoxamine, 0.19 and 0.32 with efavirenz, and 0.07 and 0.14 with rifampicin. This PBPK analysis provides a quantitative basis to guide the label and clinical use of finerenone with concomitant CYP3A4 modulators.