Cargando…

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining po...

Descripción completa

Detalles Bibliográficos
Autores principales: Anbari, Samira, Wang, Hanwen, Zhang, Yu, Wang, Jun, Pilvankar, Minu, Nickaeen, Masoud, Hansel, Steven, Popel, Aleksander S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318535/
https://www.ncbi.nlm.nih.gov/pubmed/37408756
http://dx.doi.org/10.3389/fphar.2023.1163432
_version_ 1785068058912489472
author Anbari, Samira
Wang, Hanwen
Zhang, Yu
Wang, Jun
Pilvankar, Minu
Nickaeen, Masoud
Hansel, Steven
Popel, Aleksander S.
author_facet Anbari, Samira
Wang, Hanwen
Zhang, Yu
Wang, Jun
Pilvankar, Minu
Nickaeen, Masoud
Hansel, Steven
Popel, Aleksander S.
author_sort Anbari, Samira
collection PubMed
description Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients’ immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.
format Online
Article
Text
id pubmed-10318535
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-103185352023-07-05 Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager Anbari, Samira Wang, Hanwen Zhang, Yu Wang, Jun Pilvankar, Minu Nickaeen, Masoud Hansel, Steven Popel, Aleksander S. Front Pharmacol Pharmacology Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients’ immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10318535/ /pubmed/37408756 http://dx.doi.org/10.3389/fphar.2023.1163432 Text en Copyright © 2023 Anbari, Wang, Zhang, Wang, Pilvankar, Nickaeen, Hansel and Popel. https://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 Pharmacology
Anbari, Samira
Wang, Hanwen
Zhang, Yu
Wang, Jun
Pilvankar, Minu
Nickaeen, Masoud
Hansel, Steven
Popel, Aleksander S.
Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title_full Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title_fullStr Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title_full_unstemmed Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title_short Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
title_sort using quantitative systems pharmacology modeling to optimize combination therapy of anti-pd-l1 checkpoint inhibitor and t cell engager
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318535/
https://www.ncbi.nlm.nih.gov/pubmed/37408756
http://dx.doi.org/10.3389/fphar.2023.1163432
work_keys_str_mv AT anbarisamira usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT wanghanwen usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT zhangyu usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT wangjun usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT pilvankarminu usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT nickaeenmasoud usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT hanselsteven usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager
AT popelaleksanders usingquantitativesystemspharmacologymodelingtooptimizecombinationtherapyofantipdl1checkpointinhibitorandtcellengager