Purpose: Anticancer drugs often show a narrow therapeutic index and high inter-patient variability, which can lead to the need to adjust doses individually during the treatment. One approach to doing this is to use individual model predictions. Such methods have been proposed to target-specific drug concentrations or blood cell count, both of which are continuous variables. However, many toxic effects are evaluated on a categorical scale. This article presents a novel approach to dose adjustments for reducing a graded toxicity while maintaining efficacy, applied to hand-and-foot syndrome (HFS) induced by capecitabine.
Methods: A mixed-effects proportional odds Markov model relating capecitabine doses to HFS grades was individually adjusted at the end of each treatment cycle (3 weeks) by estimating subject-specific parameters by Bayesian MAP technique. It was then used to predict the risk of intolerable (grade ≥ 2) toxicity over the next treatment cycle and determine the next dose accordingly, targeting a predefined tolerable risk. Proof of concept was given by simulating virtual clinical trials, where the standard dose reductions and the prediction-based adaptations were compared, and where the therapeutic effect was simulated using a colorectal tumor inhibition model. A sensitivity analysis was carried out to test various specifications of prediction-based adaptation.
Results: Individualized dose adaptation might reduce the average duration of intolerable HFS by 10 days as compared to the standard reductions (3.8 weeks vs. 5.2 weeks; 27% relative reduction) without compromising antitumor efficacy (both responder rates were 49%). A clinical trial comparing the two methods should include 350 patients per arm to achieve at least 90% power to show a difference in grade ≥2 HFS duration at an alpha level of 0.05.
Conclusions: These results indicate that individual prediction-based dose adaptation based on ordinal data may be feasible and beneficial.