Chemotherapy-induced peripheral neuropathy (henceforth, neuropathy) is often dose limiting and is generally managed by empirical dose modifications. We aimed to (1) identify an early time point that is predictive of future neuropathy using a patient-reported outcome and (2) propose a dose-adjustment algorithm based on simulated data to manage neuropathy. In previous work, a dose-neuropathy model was developed using dosing and patient-reported outcome data from Cancer and Leukemia Group B 40502 (Alliance), a randomized phase III trial of paclitaxel, nanoparticle albumin-bound paclitaxel or ixabepilone as first-line chemotherapy for locally recurrent or metastatic breast cancer. In the current work, an early time point that is predictive of the future severity of neuropathy was identified based on predictive accuracy of the model. Using the early data and model parameters, simulations were conducted to propose a dose-adjustment algorithm for the prospective management of neuropathy in individual patients. The end of the first 3 cycles (12 weeks) was identified as the early time point based on a predictive accuracy of 75% for the neuropathy score after 6 cycles. For paclitaxel, nanoparticle albumin-bound paclitaxel, and ixabepilone, simulations with the proposed dose-adjustment algorithm resulted in 61%, 48%, and 35% fewer patients, respectively, with neuropathy score ≥8 after 6 cycles compared to no dose adjustment. We conclude that early patient-reported outcome data on neuropathy can be used to guide dose adjustments in individual patients that reduce the severity of future neuropathy. Prospective validation of this approach should be undertaken in future studies.
Keywords: breast cancer; chemotherapy; modeling and simulation; neuropathy; personalized.
© 2019, The American College of Clinical Pharmacology.