ACMD Seminar: Towards Optimization of Monoclonal Therapeutic Drug Delivery Using a 3D Model of Cancer Immune Interaction

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Abstract: T cell exhaustion is a dysfunctional state that develops after prolonged antigen exposure, in which T cells progressively lose their ability to proliferate, secrete cytokines, and eliminate malignant cells. A hallmark of exhaustion is the sustained expression of inhibitory receptors, including Programmed Cell Death protein 1 (PD-1) and Cytotoxic T-Lymphocyte Associated protein 4 (CTLA-4), which transmit suppressive signals that limit immune activity. Cancer cells exploit these regulatory pathways by upregulating inhibitory receptor ligands on their surface, further driving the exhausted phenotype. Monoclonal therapeutic antibodies (mAbs) that target inhibitory receptors boost immune function by disrupting suppressive signaling pathways, and have demonstrated efficacy in several cancers. However, clinical outcomes remain variable for both checkpoint blockade and standard of care methods, motivating alternative treatment strategies. Towards the goal of identifying optimal dosing regimens of immune blockade drugs, we construct a multiscale, off-lattice 3D model of the tumor microenvironment (TME). This framework couples an agent-based model of cell–cell interactions with a reaction–diffusion model for antibody transport and infiltration. Cell motility is modeled through intermolecular forces based on the Lennard-Jones potential, and T cell chemotaxis is characterized by a prescribed cytokine signaling function. In the current model iteration, we consider 𝛼PD-1 mAbs, which bind to PD-1 on T cells and block the PD-1/PD-L1 pathway, thereby limiting exhaustion. Our model tracks both exhausting interactions between cancer and effector T cells and the infiltration capacity of T cells into the TME. We assess parameter sensitivity by computing first-order and total-order Sobol indices. We highlight key factors that influence T cell migration and progression into an exhaustive state. Our analysis provides insights into how microenvironmental and therapeutic parameters influence T cell function and may inform future modeling strategies for immunotherapy optimization, including combination therapies.

 

Bio: Jordana is a first year post doctoral researcher in the Talkington Lab at the University of Buffalo. She received her PhD in mathematical modeling from the Rochester Institute of Technology, where she conducted her dissertation on the modeling of pulmonary drug transport and delivery. She dabbled in PKPD modeling as a summer intern at Merck, and she worked for three years as a student researcher at Los Alamos National Lab while completing her degree. In her down time, she enjoys kitchen dance parties, creating art, planning her next trip, and playing Minecraft with her nine year old daughter.

 

Host: Zach Grey/Anne Talkington

 

Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)