Paul van den Berg (1), Martijn Ruppert (1), Emir Mesic (1), Nelleke Snelder (1), Andreas Seelmann (2), Roland Heinig (2), Amer Joseph (2), Dirk Garmann (2), Joerg Lippert (2), Thomas Eissing (2)
(1) LAP&P Consultants, The Netherlands, (2) Pharmaceuticals R&D, Bayer AG, Germany
Objectives: Chronic kidney disease (CKD) in patients with type 2 diabetes (T2D) is associated with an accelerated progression towards kidney failure [1]. Phase III study FIDELIO-DKD investigated the efficacy and safety of the nonsteroidal, selective mineralocorticoid receptor antagonist finerenone compared to placebo on top of standard-of-care in patients with CKD and T2D [2]. Patients received either finerenone (starting dose depended on eGFR at screening) or placebo p.o. once daily and could be up- or down-titrated according to changes in serum potassium and eGFR and at the discretion of the investigator [2, 3]. The primary endpoint of FIDELIO-DKD was a composite of (1) time to the first occurrence of kidney failure, (2) a sustained decrease of eGFR ≥ 40% from baseline over at least 4 weeks, or (3) renal death.
The objectives of this analysis were to:
- characterize the PK of finerenone in patients from FIDELIO-DKD, including identification of covariate effects to support the finerenone submission and labeling.
- provide posthoc estimates for exposure-response analyses.
- characterize the relationship between finerenone exposure and the time to reach the primary kidney endpoint, including investigation of selected prognostic factors (PFs).
Methods: A total of 5057 finerenone plasma concentrations from 2284 subjects were used to develop the finerenone PK model. These were collected at month 4 (trough sample) and at each of the yearly visits (post-dose at any time on the visit day). An existing model based on sparsely sampled data from Phase 2b studies was used as the starting point for the PK analysis [4]. Stepwise covariate modelling was done to identify covariates on finerenone PK. To support labelling two types of model-based simulations were performed, namely: 1) simulations to assess the magnitude and uncertainty of a single covariate effect, and 2) simulations to compare the finerenone exposure at steady-state in subgroups of interest taking combined individual covariate effects into account.
During a median follow-up of 2.6 years, a kidney event contributing to the composite endpoint occurred in 504 of 2833 patients (17.8%) in the finerenone group and in 600 of 2841 patients (21.1%) in the placebo group [5]. First, a placebo time-to-event (TTE) model was developed for the composite kidney endpoint. Candidate PFs based on non-parametric Cox Proportional Hazard analysis were concurrently included in the model. Next, the optimal parametric hazard model was selected and non-significant PFs were removed via a backward deletion procedure. Subsequently, the active treatment data was included and the ER investigated using individual post-hoc predictions from the PK model as input. In a final step PFs removed during placebo TTE model development were re-evaluated.
NONMEM was used to develop the population PK and finerenone ER models.
Results: A two-compartmental population PK model with volumes set equal and absorption through a series of transit compartments and first-order elimination adequately captured the finerenone concentration–time data in FIDELIO-DKD. Generally, covariate effects or multivariate forward-simulations in subgroups of interest were contained within the equivalence range of 80–125% around typical exposure. The kidney composite TTE data were best described with a Weibull hazard model. The ER relationship was implemented as an Emax model with finerenone concentration in the central compartment driving the response. Half-maximal effect concentration was estimated at 0.166 µg/L and the maximal hazard decrease at -36.1%. PFs for the treatment-independent chronic kidney disease progression risk included a low estimated glomerular filtration rate and a high urine-to-creatinine ratio increasing the risk, while concomitant sodium-glucose transport protein 2 inhibitor (SGLT2i) use decreased the risk. Importantly, no SGLT2i co-medication-related modification of the finerenone treatment effect per se could be identified.
Conclusions: None of the tested pharmacokinetic covariates had clinical relevance in FIDELIO-DKD. Finerenone effects on kidney outcomes approached saturation towards 20 mg once daily and SGLT2i use provided additive benefits [6]. These findings are further strengthened by current exposure-response analyses for the biomarkers/surrogates UACR and eGFR [7] that may be further linked to kidney outcome.
References:
[1] Agarwal R, et al. Investigating new treatment opportunities for patients with chronic kidney disease in type 2 diabetes: the role of finerenone. Nephrol Dial Transplant. 2020.
[2] Bakris GL, et al. Design and baseline characteristics of the finerenone in reducing kidney failure and disease progression in diabetic kidney disease trial. Am J Nephrol. 2019;50(5):333–44.
[3] Goulooze B, et al. Finerenone dose-exposure-serum potassium response analysis of FIDELIO-DKD phase 3: the role of dosing, titration, and inclusion criteria. Clin Pharmacokinet. 2022;61(3):451-462.
[4] Snelder N, et al. Population pharmacokinetic and exposure-response analysis of finerenone: insights based on phase IIb data and simulations to support dose selection for pivotal trials in type 2 diabetes with chronic kidney disease. Clin Pharmacokinet. 2020;59(3):359–70.
[5] Bakris GL, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. N Engl J Med 2020;383:2219-29.
[6] Van den Berg P, et al. Finerenone Dose-Exposure-Response for the Primary Kidney Outcome in FIDELIO-DKD Phase III: Population Pharmacokinetic and Time-to-Event Analysis. Clin Pharmacokinet. 2022;61(3):439-450.
[7] Goulooze SC, et al. Dose–exposure–response analysis of the nonsteroidal mineralocorticoid receptor antagonist finerenone on UACR and eGFR: An analysis from FIDELIO-DKD. Submitted.
Reference: PAGE 30 (2022) Abstr 9976 [www.page-meeting.org/?abstract=9976]
Poster: Drug/Disease Modelling - Other Topics