ARTI-MPC – Activity, Risk & Therapy-Intensity in Metastatic Prostate Cancer
openNIA - National Institute on Aging
PROJECT SUMMARY
Metastatic prostate cancer (MPC) disproportionately affects older adults (OAs), significantly impairing
their functional independence due to severe toxicities associated with androgen receptor signaling inhibitors
(ARSIs). Despite guidelines recommending geriatric assessments (GAs), clinical dosing of ARSIs remains
largely subjective, neglecting critical variations in frailty and physical function. Consequently, many OAs
experience preventable adverse outcomes, underscoring an urgent need for individualized, evidence-based
dosing strategies. My long-term goal is to transform MPC treatment for older men by developing precision dosing
models that mitigate toxicity while preserving functional independence. My overall objectives include validating
ARSI dose intensity and wearable sensor-derived metrics, particularly daily step counts, and using these metrics
to inform predictive toxicity modeling. These objectives are designed to yield practical dosing guidelines tailored
to individual frailty levels and functional trajectories. The rationale is that real-time, objective biomarkers like
wearable-derived step counts combined with ARSI dose intensity can significantly enhance the predictive
accuracy of ARSI-related toxicity, overcoming limitations of traditional, static assessments.
Leveraging detailed data from two ongoing prospective studies, ProsGATE and DaroStep, my approach
integrates serial geriatric assessments, wearable-derived activity metrics, and precise ARSI exposure records.
Specifically, I will first determine if higher monthly ARSI doses predict an increased 12-month risk of severe
toxicity and functional decline. Next, I will validate whether baseline step counts and significant early declines in
activity independently predict adverse treatment outcomes. Finally, I will apply advanced statistical methods,
including penalized spline-based and Bayesian additive regression tree models, to define personalized safe-
dose windows for ARSIs utilizing GA and/or step defined strata. The significance of this research is profound,
offering a scalable model to guide ARSI dosing, substantially reducing severe toxicities among over 120,000
older Americans with MPC. By pioneering the integration of wearable technology with rigorous statistical
modeling, this proposal sets a benchmark for innovation in geriatric oncology. This K23 award will provide
essential training through structured mentorship by experts in geriatrics (Dr. Huisingh-Scheetz), prostate
oncology (Dr. Szmulewitz), Bayesian biostatistics (Dr. Ji), longitudinal modeling (Yan Che), and clinical
pharmacology (Dr. Gobburu). This mentorship, combined with advanced coursework, will equip me with skills
crucial for digital biomarker development, nonlinear dose-toxicity modeling, and precision clinical trial design.
Ultimately, this award will facilitate my transition into an independent physician-scientist role, laying the
foundation for future R01-level trials aimed at optimizing cancer therapy for older adults.
Up to $158K
health research