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Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology
Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, cha...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524731/ https://www.ncbi.nlm.nih.gov/pubmed/31134058 http://dx.doi.org/10.3389/fimmu.2019.00924 |
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author | Peskov, Kirill Azarov, Ivan Chu, Lulu Voronova, Veronika Kosinsky, Yuri Helmlinger, Gabriel |
author_facet | Peskov, Kirill Azarov, Ivan Chu, Lulu Voronova, Veronika Kosinsky, Yuri Helmlinger, Gabriel |
author_sort | Peskov, Kirill |
collection | PubMed |
description | Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges. |
format | Online Article Text |
id | pubmed-6524731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65247312019-05-27 Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology Peskov, Kirill Azarov, Ivan Chu, Lulu Voronova, Veronika Kosinsky, Yuri Helmlinger, Gabriel Front Immunol Immunology Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges. Frontiers Media S.A. 2019-04-30 /pmc/articles/PMC6524731/ /pubmed/31134058 http://dx.doi.org/10.3389/fimmu.2019.00924 Text en Copyright © 2019 Peskov, Azarov, Chu, Voronova, Kosinsky and Helmlinger. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Peskov, Kirill Azarov, Ivan Chu, Lulu Voronova, Veronika Kosinsky, Yuri Helmlinger, Gabriel Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title | Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title_full | Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title_fullStr | Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title_full_unstemmed | Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title_short | Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology |
title_sort | quantitative mechanistic modeling in support of pharmacological therapeutics development in immuno-oncology |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524731/ https://www.ncbi.nlm.nih.gov/pubmed/31134058 http://dx.doi.org/10.3389/fimmu.2019.00924 |
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