Multidisciplinary analysis of evolution based Abiraterone treatment for metastatic castrate resistant prostate cancer
Abstract
Background
We present a multidisciplinary approach to clinical trial design and analysis in a pilot study (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="clintrialgov" xlink:href="NCT02415621">NCT02415621</ext-link>) in which evolution-based mathematical models guide patient-specific dosing for Abiraterone treatment in men with castrate resistant metastatic prostate cancer.
Methods
Abiraterone plus prednisone were administered intermittently based on an evolutionary mathematical model. Outcomes are compared to historical controls and a matched contemporaneous cohort who met trial eligibility but received SOC dosing. Longitudinal cohort data allowed modification of pre-trial model parameter estimates. Model simulations of each patient using updated parameters critically evaluated trial design.
Results
Trial patients, on average, received no abiraterone during 59% of time on treatment. Median Time to Radiographic Progression (TTP) was 30.4 months compared to 14.3 months in the contemporaneous SOC group (p<0.001). All patients in the SOC group have progressed but 4 in the adaptive cohort remain on treatment at >1800 days. Longitudinal trial data found the competition coefficient ratio (αRS/αSR) of sensitive and resistant populations, a critical factor in intratumoral evolution, was 2 to 3-fold higher than pre-trial estimates. Computer simulations using the corrected parameter unexpectedly demonstrated optimal cycling can reduce the resistant cells. Longitudinal data from 4 trial patients who remain on treatment are consistent with model predictions. Modeling results predict protocol changes that will allow similar outcomes in most patients.
Conclusions
Administration of abiraterone using evolution-based mathematical models decreased drug dosing and increased radiographic TTP. Integration of mathematical models into trial design identifies novel insights into key treatment parameters and provides optimization strategies for follow-up investigations.
Article Summary
supplemental sections outline the methodology for parameter estimates from trial data, computer simulations, and comparison of simulation results and actual clinical data in every patient in both cohorts.
Statement of Translational Relevance
Integration of evolution-based mathematical models significantly increased TTP in abiraterone therapy for mCRPC. This multidisciplinary approach represents a novel clinical trial strategy in which the treatment protocol is framed mathematically, clinical data then refines model parameterization, and simulations using the updated model predict alternative strategies to improve outcomes. Here we demonstrate the mathematical models used to design the trial can also produce novel analytic approaches. By using longitudinal trial data, key model parameters can be refined. Simulations using the updated model can then be applied to every patient in the trial. Finally, additional simulations demonstrate alternative protocols that could improve results. These analyses demonstrate evolution-based approaches may allow consistent long-term control in patients with metastatic prostate cancer.
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