Rethinking the seven-day treatment-free interval in T-cell engager therapy using agent-based modeling

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Background

The CD3/CD19 bispecific T cell engager (TCE) blinatumomab has shown efficacy in relapsed/refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL), but response rates are often limited by T cell exhaustion. Recent preclinical studies suggest that incorporating treatment-free intervals (TFIs) into dosing schedules may enhance therapeutic outcomes.

Methods

To systematically evaluate alternative TFI strategies, we developed an agent-based model (ABM) of tumor–T cell interactions under various blinatumomab dosing regimens. The model was calibrated using published in vitro data and incorporated spatial, stochastic, and mechanistic rules governing T cell activation, cytotoxicity, proliferation, and exhaustion.

Results

Our ABM recapitulates experimental observations showing that a 7-day TFI improved T cell function over continuous dosing during the initial 28-day treatment period. However, when simulations were extended to a full 42-day cycle to mimic clinical regimen, this advantage was lost. In contrast, shorter TFIs consistently outperformed both 7-day and continuous schedules, leading to superior tumor control at all timepoints. A translationally oriented Monday-through-Friday (MO_FR) regimen also achieved comparable benefits.

Conclusions

Our results indicate that the empirically tested 7-day TFI schedule may not be optimal. TFI with shorter intervals as well as translationally relevant schedules such as MO_FR, may offer greater therapeutic benefit. This work demonstrates the value of ABM in preclinical immunotherapy design and supports model-guided refinement of TCE dosing strategies prior to clinical translation. Future work will focus on validating these predictions in more complex in vivo models and leveraging patient-derived data to guide personalized TCE treatment design.

Article activity feed