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06 03, 2020;10 (1): 9063.

A QSP model of prostate cancer immunotherapy to identify effective combination therapies.

Roberta Coletti, Lorena Leonardelli, Silvia Parolo, Luca Marchetti
Author Information
  1. Roberta Coletti: University of Trento, Department of mathematics, Trento, 38123, Italy.
  2. Lorena Leonardelli: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy.
  3. Silvia Parolo: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy. ORCID
  4. Luca Marchetti: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy. marchetti@cosbi.eu. ORCID
PMID: 32493951 DOI: 10.1038/s41598-020-65590-0

Abstract

Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.

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MeSH Term

Animals
Cancer Vaccines
Cell Line, Tumor
Combined Modality Therapy
Humans
Immunologic Factors
Immunotherapy
Male
Mice
Models, Biological
Prostate
Prostatic Neoplasms, Castration-Resistant

Chemicals

Cancer Vaccines
Immunologic Factors
Journal Article
Article has an altmetric score of 1

Word Cloud

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