Development of quantitative systems pharmacology and toxicology models within consortia: experiences and lessons learned through DILIsym development

2016 ◽  
Vol 22 ◽  
pp. 5-13 ◽  
Author(s):  
Brett A. Howell ◽  
Scott Q. Siler ◽  
Hugh A. Barton ◽  
Elizabeth M. Joshi ◽  
Antonio Cabal ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Pharmacology And Toxicology

scholarly journals Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

2021 ◽  
Author(s):  
Peter Bloomingdale ◽  
Tatiana Karelina ◽  
Murat Cirit ◽  
Sarah F. Muldoon ◽  
Justin Baker ◽  
...  
Keyword(s):  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology

Response to: “The Role of Quantitative Systems Pharmacology in the Design of First-in-Human Trials”

2018 ◽  
Vol 104 (5) ◽  
pp. 798-798
Author(s):  
Kevin Blake ◽  
Milton Bonelli ◽  
Stefano Ponzano ◽  
Harald Enzmann ◽  
Keyword(s):  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Human Trials

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

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3751
Author(s):  
Chang Gong ◽  
Alvaro Ruiz-Martinez ◽  
Holly Kimko ◽  
Aleksander S. Popel
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Immune Cell ◽  
Imaging Techniques ◽  
Tumor Development ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Spatially Resolved ◽  
Biomarker Analysis ◽  
Model Platform

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.

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