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Article

A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy

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Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
2
Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA
3
Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Mohammad Kohandel and Michelle Przedborski
Cancers 2021, 13(15), 3751; https://doi.org/10.3390/cancers13153751
Received: 30 April 2021 / Revised: 9 July 2021 / Accepted: 20 July 2021 / Published: 26 July 2021
(This article belongs to the Special Issue Quantitative Approaches to Cancer Immunotherapy)
Mathematical and computational models, such as quantitative systems pharmacology (QSP) models, are becoming a popular tool in drug discovery. The advancement of imaging techniques creates a unique opportunity to further expand these models and enhance their predictive power by incorporating characteristics of individual patients embedded in the image data obtained from tissue samples. The aim of this study is to develop a platform which combines the strength of QSP models and spatially resolved agent-based models (ABM), and create a model of cancer development in the context of anti-cancer immunity and immune checkpoint inhibition therapy. The model can be applied in virtual clinical trials and biomarker discovery to help refine trial design.
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials. View Full-Text
Keywords: immuno-oncology; systems biology; agent-based model; computational model; mathematical model; intratumoral heterogeneity; digital pathology immuno-oncology; systems biology; agent-based model; computational model; mathematical model; intratumoral heterogeneity; digital pathology
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MDPI and ACS Style

Gong, C.; Ruiz-Martinez, A.; Kimko, H.; Popel, A.S. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy. Cancers 2021, 13, 3751. https://doi.org/10.3390/cancers13153751

AMA Style

Gong C, Ruiz-Martinez A, Kimko H, Popel AS. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy. Cancers. 2021; 13(15):3751. https://doi.org/10.3390/cancers13153751

Chicago/Turabian Style

Gong, Chang, Alvaro Ruiz-Martinez, Holly Kimko, and Aleksander S. Popel. 2021. "A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy" Cancers 13, no. 15: 3751. https://doi.org/10.3390/cancers13153751

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