Prediction of the optimal dosing regimen using a mathematical model of tumour uptake for immunocytokine-based cancer immunotherapy

PURPOSE: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities. EXPERIMENTAL DESIGN: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL-2 receptor-positive cell populations (i.e. CD8+, CD4+, NK and B cells) which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues. RESULTS: We created a PKPD mathematical model that incorporates the expansion of IL-2R-positive target cells at multiple doses levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL-2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL-2R-positive cells. CONCLUSIONS: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anti-cancer immunotherapy.

http://ift.tt/2CCf0eY