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Kidney Week

Abstract: TH-PO415

Development of a Clinical Trial Enrichment (CTE) Tool for Autosomal Dominant Polycystic Kidney Disease

Session Information

Category: Genetic Diseases of the Kidneys

  • 1201 Genetic Diseases of the Kidneys: Cystic

Authors

  • Aggarwal, Varun, Critical Path Institute, Tucson, Arizona, United States
  • Zaph, Susana, Sanofi, Boston, Massachusetts, United States
  • Leiser, Randolph J., Sanofi, Boston, Massachusetts, United States
  • Quinlan, Lauren E., Critical Path Institute, Tucson, Arizona, United States
  • Cui, Zihan, Critical Path Institute, Tucson, Arizona, United States
  • Morales, Juan F., Critical Path Institute, Tucson, Arizona, United States
  • Miller, Christine E., Critical Path Institute, Tucson, Arizona, United States
  • Dasmahapatra, Pronabesh, Sanofi, Boston, Massachusetts, United States
  • Vanasco, Wendy, Critical Path Institute, Tucson, Arizona, United States
  • Romero, Klaus, Critical Path Institute, Tucson, Arizona, United States
  • Perrone, Ronald D., Tufts Medical Center, Boston, Massachusetts, United States
  • Fedeles, Sorin V., Critical Path Institute, Tucson, Arizona, United States
Background

Despite the regulatory advances in ADPKD including the qualification of total kidney volume (TKV) as a prognostic enrichment biomarker and its designation by the FDA as a reasonably likely surrogate endpoint, the clinical development paradigms for PKD remain challenging.

Methods

Registry (Mayo, Emory, Colorado), longitudinal (CRISP), and RCT data (HALT) were curated and mapped to Clinical Data Interchange Standards Consortium Study Data Tabulation Model (CDISC SDTM) standards. Model-Informed Drug Development (MIDD) approaches to predict time to end-stage renal disease (ESRD) were employed with estimated glomerular filtration rate (eGFR) decline and total kidney volume (TKV) as longitudinal markers. The data was divided with 80% for training and 20% for validation. To build the joint model, we identified the appropriate longitudinal progression model, the appropriate time to event ESRD model, and incorporated the impact of longitudinal markers on event hazard.

Results

Two base models that captured the overall trend of TKV and eGFR were examined and the one with a lower Akaike Information Criteria (AIC) score was chosen. Covariates were:

Age of diagnosis, baseline age, baseline eGFR, and baseline log(TKV) as continuous covariates
Race, sex, and presence of hypertension at baseline as categorical covariates
Age of diagnosis, baseline age, baseline TKV, and sex as covariates for the TKV longitudinal model
Baseline age, baseline eGFR, race and baseline TKV as covariates for the eGFR longitudinal model

For the time-to-event model, the Weibull distribution was selected based on the lowest AIC (best fit) before incorporating longitudinal markers.

A graphical user interface (GUI) for the CTE tool was constructed for the creation of virtual simulations of disease progression. The tool models different trial durations, population sizes, and hypothetical magnitude of drug effects on TKV/eGFR progression and predict their impact on ESRD.

Conclusion

A quantitative tool can be utilized to model disease progression trajectories in defined ADPKD subpopulations and potentially simulate impact on ESRD based on theoretical drug effects on TKV/eGFR progression. The tool may be beneficial in clinical trial design to ultimately benefit ADPKD patients.

Funding

  • Other U.S. Government Support – Sanofi