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

Abstract

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. © 2014 Elsevier Inc.