Dynamic modeling in ovarian cancer: an original approach linking early changes in modeled longitudinal CA-125 kinetics and survival to help decisions in early drug development.

OBJECTIVE: Early prediction of the expected benefit of treatment in recurrent ovarian cancer (ROC) patients may help in drug development decisions. The actual value of 50% CA-125 decrease is being reconsidered. The main objective of the present study was to quantify the links between longitudinal assessments of CA-125 kinetics and progression-free survival (PFS) in treated recurrent ovarian cancer (ROC) patients. METHODS: The CALYPSO randomized phase III trial database comparing two platinum-based regimens in ROC patients was randomly split into a "learning dataset" and a "validation dataset". A parametric survival model was developed to associate longitudinal modeled CA-125 changes (ΔCA125), predictive factors, and PFS. The predictive performance of the model was evaluated with simulations. RESULTS: The PFS of 534 ROC patients were properly characterized by a parametric mathematical model. The modeled ΔCA125 from baseline to week 6 was a better predictor of PFS than the modeled fractional change in tumor size. Simulations confirmed the model's predictive performance. CONCLUSIONS: We present the first parametric survival model quantifying the relationship between PFS and longitudinal CA-125 kinetics in treated ROC patients. The model enabled calculation of the increase in ΔCA125 required to observe a predetermined benefit in PFS to compare therapeutic strategies in populations. Therefore, ΔCA125 may be a predictive marker of the expected gain in PFS and an early predictive tool in drug development decisions.