Evaluation of tumor kinetics metrics as early endpoint to support decision making in early drug development
Sandrine Micallef(1) and Francois Mercier
(1) Clinical Pharmacology, Roche Pharma Research and Early Development, Roche Innovation Center Basel, Switzerland
Objectives: In the process of drug development in oncology, moving from early to late phase is a critical and complex step. Usually, decisions are based on the overall response rate (ORR) derived from RECIST[1] criterion, and assessed on a limited number of patients (often 20-40 with more or less similar tumors). Because of variability and the limited amount of information obtained, good decision making can be difficult. We explored the benefits of metrics based on longitudinal tumor kinetic modeling to inform decision making in early drug development.
Methods: We used a bi-exponential tumor kinetics model previously proposed by Stein[2] and implemented as a nonlinear mixed effect model by Claret[3] to fit longitudinal tumor size data simulated from a real study in NSCLC patients treated with an immunotherapy. Predicted Tumor Kinetic Metric (pTKM) were derived from the model, including maximum tumor shrinkage, time to growth and time to progression. We defined decision criteria based on these pTKM to determine trial outcome (success/failure), mimicking the decision process based on ORR. The TKM based criteria were evaluated for consistency with truth and ORR using a simulation study. We compared TKM for decision making in simulated clinical trials of different scenarios (different number of patients, or different tumor assessment periods of time).
Results: Model based TKM required a minimum amount of data to allow model fitting while observed TKM can be derived from any dataset. Globally, predicted TKM had performed at least as well as observed TKM. However, when data were limited (low number of subjects, for example), decision criteria based on observed TKM had high risk of producing incorrect decisions. In this case, predicted TKMs were more likely to give correct decision.
Conclusions: At the end of a phase I clinical trial, when there is enough tumor kinetic data to allow population tumor kinetic model fitting, decision criteria based on predicted TKM support better decisions than observed TKM. Predicted tumor kinetic metric should be further explored as a quantitative support for decision making in early drug development.
References:
[1] Eisenhauer, EA1, et al. "New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)." European journal of cancer 45.2 (2009): 228-247.
[2] Stein, Wilfred D., et al. "Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy." Clinical Cancer Research 17.4 (2011): 907-917.
[3] Claret, Laurent, et al. "Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer." Journal of Clinical Oncology 31.17 (2013): 2110-2114.
