# Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $476,442

## Abstract

Project Summary
The principal goal of this proposal is to combine multiscale mathematical modeling with novel computational
model-driven quantitative experimental platforms to develop a comprehensive and predictive 3D computational
framework. Bladder cancer is one of the 10 most common cancers in the United States and in its advanced
stages the 5-year survival rates are below 35%. Given the poor outcomes with chemotherapy in advanced cases,
immunotherapy has emerged as an exciting domain for exploration. Monoclonal antibodies targeting the PD-
1/PD-L1 “immune checkpoint” pathway have resulted in favorable outcomes in advanced bladder cancer, and 5
drugs targeting this pathway have been approved in the past two years. Unfortunately, the objective response
rates of current FDA approved immunotherapy drugs remain less than 25%. An alternative treatment strategy
for bladder cancer is small molecule inhibitors (SMIs) of fibroblast growth factor receptor (FGFR3), and early
clinical studies using these molecular-targeted agents have shown promise. Recently published data supporting
the co-acting combination of potent immune checkpoint inhibitors and specific FGFR3 inhibitors potentially offer
an advance in targeted therapeutics for cancer. A powerful and practical way to optimize novel drug combinations
for clinical cancer treatment is to use sophisticated, data-driven computational models. Our proposed agent-
based model platform will both aid in the characterization of tumor-immune dynamics and also suggest the best
strategies for administering therapeutic combinations of immune-checkpoint and receptor kinase inhibitors. The
model will be parameterized at the molecular and cellular scales by an innovative high-throughput image
quantification pipeline that allows T-cell or cancer cell behaviors and interactions to be observed, tracked, and
quantified. Importantly, this model system pipeline can measure the antigen burden on tumor cells and the
proportion of the two types of T-cell cytotoxicity (Fas-ligand vs. granule-based). Our experimentally-driven
multiscale approach is posed to (1) significantly enhance the current understanding of the impact of differential
cell-kill mechanisms on tumor-immune outcomes; (2) optimize the administration of combination therapy and
maximize tumor response; and (3) to improve the ability to select the most promising drugs for the clinical trials.
While based on tumors of the bladder, the platform that we are developing is easily adaptable for the study of
any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type. The true
significance of our work lies in its translational value: our experimental and theoretical studies will be able to test
clinically relevant hypotheses regarding the prospect of receptor tyrosine kinase inhibitors and immune
checkpoint inhibitors to impact the mechanism of tumor cell kill by immune cells in distinct ways. Cancer is one
of the leading causes of death for ...

## Key facts

- **NIH application ID:** 10144947
- **Project number:** 5U01CA243075-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Trachette Jackson
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $476,442
- **Award type:** 5
- **Project period:** 2020-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10144947

## Citation

> US National Institutes of Health, RePORTER application 10144947, Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules (5U01CA243075-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10144947. Licensed CC0.

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