Mechanistic modeling of tumor immune microenvironment reveals strategies to enhance antibody-drug conjugates’ efficacy

Background Antibody-drug conjugates (ADCs) and bispecific antibodies represent a rapidly advancing frontier in oncology, yet the abnormal tumor microenvironment (TME) hinders their delivery and reduces efficacy. Emerging immunomodulatory ADCs (IM-ADCs) demand mechanistic mathematical models that couple drug transport with immune dynamics. Methods Here, we present a mechanistic framework for the delivery of HE-S2 ADC, an anti-programmed cell death ligand 1 (PD-L1) antibody bearing the bifunctional immunomodulator D18. Our model integrates cancer-immune cells interactions, TME properties, such as dysfunctional vessels, elevated interstitial fluid pressure, tissue hydraulic conductivity, and vascular permeability, spatiotemporal distributions across growing tumor and adjacent host tissue, convective-diffusive transport, ADCs binding and internalization kinetics and tumor-draining lymph node biology governing antigen presentation and the generation of effector CD8 + T cells. Parameters were calibrated simultaneously with the murine MC38 and B16 tumor growth data and effector CD8+T cell data following treatment with D18, anti-PD-L1, and ADC. Results Our mechanistic spatiotemporal model captures the superior antitumor efficacy of the HE-S2 ADC relative to its individual components and provides mechanistic predictions for unmeasured variables, such as spatiotemporal dynamics of drug/immune-cell distributions. It explains reduced intratumoral D18 exposure via rapid clearance, while antibody/ADC achieves higher tumor retention through leaky tumor vasculature. The model suggests a reinforcing loop in which improved ADC exposure enhances CD8+T cell infiltration, driving tumor shrinkage that lowers fluid pressure and improves drug delivery. Parametric analyses findings support TME normalization strategies that increase functional vessel density prior to ADC administration; however, such approaches should preserve sufficient vascular permeability by maintaining vessel pore radius >~40 nm, ensuring pores remain large enough for ADC extravasation and effective intratumoral delivery. Conclusion The proposed mechanistic model successfully captures how TME properties regulate the delivery and efficacy of IM-ADCs while suggesting TME normalization as a potential strategy to improve treatment outcomes.

Authors

Institutions

Publication Details

Journal
Journal for ImmunoTherapy of Cancer
Published
2026-09-01
DOI
https://doi.org/10.1136/jitc-2026-015357
Primary Topic
Monoclonal and Polyclonal Antibodies Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Mechanistic modeling of tumor immune microenvironment reveals strategies to enhance antibody-drug conjugates’ efficacy

Lance L. Munn, Rakesh K. Jain, Triantafyllos Stylianopoulos, Constantinos Harkos
Journal for ImmunoTherapy of Cancer
Monoclonal and Polyclonal Antibodies Research
article

Mechanistic modeling of tumor immune microenvironment reveals strategies to enhance antibody-drug conjugates’ efficacy

Lance L. Munn, Rakesh K. Jain, Triantafyllos Stylianopoulos, Constantinos Harkos
article en

Abstract

Background Antibody-drug conjugates (ADCs) and bispecific antibodies represent a rapidly advancing frontier in oncology, yet the abnormal tumor microenvironment (TME) hinders their delivery and reduces efficacy. Emerging immunomodulatory ADCs (IM-ADCs) demand mechanistic mathematical models that couple drug transport with immune dynamics. Methods Here, we present a mechanistic framework for the delivery of HE-S2 ADC, an anti-programmed cell death ligand 1 (PD-L1) antibody bearing the bifunctional immunomodulator D18. Our model integrates cancer-immune cells interactions, TME properties, such as dysfunctional vessels, elevated interstitial fluid pressure, tissue hydraulic conductivity, and vascular permeability, spatiotemporal distributions across growing tumor and adjacent host tissue, convective-diffusive transport, ADCs binding and internalization kinetics and tumor-draining lymph node biology governing antigen presentation and the generation of effector CD8 + T cells. Parameters were calibrated simultaneously with the murine MC38 and B16 tumor growth data and effector CD8+T cell data following treatment with D18, anti-PD-L1, and ADC. Results Our mechanistic spatiotemporal model captures the superior antitumor efficacy of the HE-S2 ADC relative to its individual components and provides mechanistic predictions for unmeasured variables, such as spatiotemporal dynamics of drug/immune-cell distributions. It explains reduced intratumoral D18 exposure via rapid clearance, while antibody/ADC achieves higher tumor retention through leaky tumor vasculature. The model suggests a reinforcing loop in which improved ADC exposure enhances CD8+T cell infiltration, driving tumor shrinkage that lowers fluid pressure and improves drug delivery. Parametric analyses findings support TME normalization strategies that increase functional vessel density prior to ADC administration; however, such approaches should preserve sufficient vascular permeability by maintaining vessel pore radius >~40 nm, ensuring pores remain large enough for ADC extravasation and effective intratumoral delivery. Conclusion The proposed mechanistic model successfully captures how TME properties regulate the delivery and efficacy of IM-ADCs while suggesting TME normalization as a potential strategy to improve treatment outcomes.

Journal for ImmunoTherapy of CancerVol. 14(9)
Harvard University (US), University of Cyprus (CY), Massachusetts General Hospital (US)
National Foundation for Cancer Research, European Commission
Good health and well-being
Openalex Percentile: Top 11%
Monoclonal and Polyclonal Antibodies Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.