Kidney Week

Abstract: TH-PO469

Predicting the Relationship between Treatment Effects on Total Kidney Volume and eGFR in ADPKD Trials Using a Novel Modeling Approach

Session Information

• Cystic Kidney Diseases: Clinical Assessment and Therapeutic Directions
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Genetic Diseases of the Kidneys

• 1201 Genetic Diseases of the Kidneys: Cystic

Authors

• Yu, Alan S.L., The University of Kansas Medical Center, Kansas City, Kansas, United States

Alan S.L. Yu, MBChB

• Parker, Chelsie, Indiana University, Bloomington, Indiana, United States

Chelsie Parker, MS

• Golzarri-Arroyo, Lilian, Indiana University, Bloomington, Indiana, United States

Lilian Golzarri-Arroyo, MSc

• Landsittel, Doug, University of Buffalo, Buffalo, New York, United States

Doug Landsittel, PhD

• Garg, Rekha, Regulus Therapeutics Inc, San Diego, California, United States

Rekha Garg, MD

• Lee, Edmund Chun Yu, Regulus Therapeutics Inc, San Diego, California, United States

Edmund Chun Yu Lee, PhD

Group or Team Name

• CRISP Consortium.

Background

TKV is accepted by the FDA as a surrogate endpoint that is reasonably likely to predict clinical benefit in ADPKD and the most commonly used response biomarker for proof-of-concept intervention trials. However, the magnitude of treatment effect on TKV that would be predictive of a meaningful improvement in a clinical outcome, such as eGFR, is unknown. Inference of this from observational studies has previously been approached by examining inter-individual variance in the relationship between TKV and GFR slopes over time.

Methods

We developed a novel approach to modeling the intra-individual relationship between TKV and eGFR. Patients from the CRISP IV dataset were stratified by Mayo Imaging Class (MIC). Linear mixed models were fitted to eGFR with a fixed effect of log(TKV) and random intercepts, and the average slope within each MIC was estimated.

Results

We find that within each MIC there is a consistent, linear relationship between log(TKV) and eGFR (Fig. 1). The model predicts that within classes 1C–1E, for each 1% point per year reduction in TKV growth rate, the rate of eGFR decline would be reduced by approximately 0.4 to 0.5 mL/min/1.73 m² per year.

Conclusion

We have developed a new model that provides a framework for defining the magnitude of treatment effect on TKV that would support accelerated approval of a drug for ADPKD.

Fig. 1. Relationship between log_e(TKV) and eGFR, subdivided by Mayo Imaging Class. Individual patient trajectories are shown in grey (N=487). Colored lines and shading show the modeled linear relationship and 95% confidence intervals from linear mixed models.

Funding

• NIDDK Support