# Exploring the preclinical relevance of therapeutic radiolabeled daratumumab (anti-CD38) in combination with anti-CS1 CAR T cells as a novel combinatorial treatment for multiple myeloma

> **NIH NIH R01** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2023 · $394,548

## Abstract

Although novel agents have increased the survival of multiple myeloma (MM) patients, the ability of cancer cells
to develop different mechanisms of resistance to therapeutic treatments has provided the scientific rationale to
use new regimens that overcome these mechanisms. Despite the significant anti-MM activity of daratumumab
(Dara), an increasing number of patients have exhibited relapsing disease with more aggressive features.
Although CAR T cell therapy is now considered one of few therapeutic options for Dara-relapsing patients,
relapse after CAR T cell therapy, as seen in MM and other cancers, is also an unfortunate scenario. Our
preclinical data show that the radioactive antibody lutetium-177-Dara (177Lu-Dara) eliminates MM cells in mice
bearing systemic MM disease, but that the curative doses of radioimmunotherapy (RIT) are toxic and eventually
lethal. Our data also show that, although the anti-CS1 CAR T-treated MM mice have a significantly longer survival
compared to control groups, MM cells are not completely eradicated, and the animals relapse. Thus, CS1
directed CAR-T therapy combined with lower dose CD38-directed RIT may have a beneficial effect in treating
relapsing MM. To test this hypothesis, the team will determine the optimal non-toxic effective RIT dose as a
single agent and the extent to which this dose is more effective when combined with CAR T cell therapy. The
efficacy of treatment depends on a multitude of factors such as the disease burden, bone marrow (BM) toxicity,
dose of RIT, dose of anti-CS1 CAR-T cells, and the scheduling and the frequency of the proposed therapies. To
navigate through these myriad factors and deliver an optimal therapeutic strategy requires a sound
understanding of the dynamics involved in each of the therapeutic options. In Specific Aim 1, the anti-MM dose
of Dara-directed RIT will be optimized to achieve minimal BM associated toxicity. Extensive preclinical studies
using DOTA-Dara labeled with two clinical relevant radionuclides, 177Lu and Actinium-225, will be conducted in
an MM-engrafted mouse model. In Specific Aim 2, the antitumor activity of combining Dara RIT and CS1 CAR
T cells will be evaluated to achieve complete disease eradication with minimal BM toxicity. The team will conduct
in vivo combinatorial studies using radiolabeled Dara and CS1 CAR T cells at different doses and administration
schedules in order to maximize MM killing activity and T cell immune activation. In Specific Aim 3, the group
will develop a mathematical model to predict the efficacy of combined RIT and CS1 CAR-T therapy. Dara-
directed RIT optimization studies and in vivo combinatorial studies will be used to parameterize radiobiological
and ordinary differential equation based models. The developed modeling framework will be use to study and
predict outcomes of different therapeutic combinations. These studies will define the optimum therapeutic dose
of radiolabeled Dara as a single agent and in combination with ...

## Key facts

- **NIH application ID:** 10666472
- **Project number:** 5R01CA238429-05
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** Flavia Pichiorri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $394,548
- **Award type:** 5
- **Project period:** 2019-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10666472

## Citation

> US National Institutes of Health, RePORTER application 10666472, Exploring the preclinical relevance of therapeutic radiolabeled daratumumab (anti-CD38) in combination with anti-CS1 CAR T cells as a novel combinatorial treatment for multiple myeloma (5R01CA238429-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10666472. Licensed CC0.

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