Abstract 7374: Mathematical modeling of targeted radionuclide therapy and CAR-T cell immunotherapy for maximizing therapeutic efficacy in multiple myeloma
Authors: Vikram Adhikarla, Dennis Awuah, Enrico Caserta, Megan Minnix, Maxim Kuznetsov, Amrita Krishnan, Jeffrey Y. Wong, John E. Shively, Xiuli Wang, Flavia Pichiorri, Russell C. Rockne
Journal: Cancer Research (2024)

Abstract Background: Resistance of cancer cells to monotherapies has led to the development of sequential or combination therapy regimens. However, dosing and scheduling of these therapies is challenging due to the numerous dosing and scheduling combinations that can be given. Mathematical models are thus critical tools for addressing this challenge as multiple therapy combinations can be tested in silico to finalize a patient-specific therapeutic regimen in vivo. Here we develop a mathematical framework for combining targeted radiation therapy (TRT) with Chimeric Antigen Receptor (CAR)-T cell immunotherapy and demonstrate the use of in silico techniques to schedule these therapies for maximizing survival. Methods: In the mathematical framework tumor growth is assumed to be exponential and the effect of radiation on both tumor cells and CAR-T cells is modeled using the linear-quadratic model of cell survival to radiation. A predator-prey model is used to characterize the dynamics of tumor and CAR-T cells. Using a preclinical disseminated mouse model of multiple myeloma (MM1S), we evaluate tumor response to 200 nCi of 225Ac-DOTA-Daratumumab (TRT) and 1 million cells of CS1 CAR-T cell therapy both as monotherapies as well as in combination. Tumor burden tracked using bioluminescence imaging from six groups of mice is used to calibrate model parameters: (a) No treatment. (b) Day 7 TRT. (c) Day 7 CAR-T cell therapy. (d) Day 7 TRT + Day 18 CAR-T cell therapy. (e) Day 7 TRT + Day 25 CAR-T cell therapy. (f) Day 7 TRT + Day 32 CAR-T cell therapy. Response to therapy is evaluated using progression-free survival (PFS), overall survival (OS) and time to minimum tumor burden (tmin), all of which are calculated using the predicted in silico tumor burden dynamics and the tumor burden at the start of first therapy. We evaluate these response metrics for various dosing and scheduling regimens. Results: Therapy intervals that were too short or too long are shown to be detrimental for therapeutic efficacy. TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing, while the interval being too long results in tumor regrowth, negatively impacting tumor control and survival. If a single dose is split into multiple doses, the splitting is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. Based on the model parameters we estimate the minimum required TRT activity and CAR-T cell dose to demonstrate an improvement in PFS. Conclusions: The proposed model demonstrates the impact of different dosing and scheduling regimens of TRT and CAR-T therapy on survival metrics. It is a potent tool for translating preclinical results to the clinic and eventually tailor therapy regimens for patients. Citation Format: Vikram Adhikarla, Dennis Awuah, Enrico Caserta, Megan Minnix, Maxim Kuznetsov, Amrita Krishnan, Jeffrey Y. Wong, John E. Shively, Xiuli Wang, Flavia Pichiorri, Russell C. Rockne. Mathematical modeling of targeted radionuclide therapy and CAR-T cell immunotherapy for maximizing therapeutic efficacy in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7374.