Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Abstract

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling

“…Nonetheless, the strong inter-patient, inter-tumoral, and intra-tumoral heterogeneities in cancer require large clinical datasets to determine the physiological plausibility of randomly generated virtual patients. This challenge may be resolved by emerging multi-omics data 13 , 14 , which involve a large number of molecular data that characterize the tumor microenvironment in individual patients. In parallel with the effort to generate virtual patients that resemble real patients’ characteristics, digital twins are being developed in precision oncology with a goal to monitor and optimize treatment for individual patients through personalized models 15 .…”