TME Modeling and Drug Response Research

Built on the CellOS virtual cell model and integrating multi-omics aligned data from real-world clinical samples, we decode cellular heterogeneity and key regulatory mechanisms in the tumor microenvironment and predict likely cellular responses to candidate gene and drug perturbations—helping pharma companies with target research, mechanism-of-action studies, and clinical translational research.

Project Background

In clinical research on oncology drugs, tumor microenvironment heterogeneity, variable treatment responses, and the mechanisms underlying clinical translation are top priorities. Traditional computational models struggle to capture the complexity of patient-derived tumor microenvironments. This project combines real-world data with AIVC technology in a joint study structured around staged R&D milestones, supporting drug development decisions and the exploration of candidate biomarkers.

The Challenge

1	Complex tumor microenvironment heterogeneity that traditional models fail to reflect across patients
2	Complex drug response and resistance mechanisms with hard-to-identify key regulators
3	Candidate targets lacking functional evidence, creating high uncertainty in clinical translation
4	Underused clinical multi-omics data, limiting support for precision R&D decisions

Solution

1	Integrate clinical multi-omics data with CellOS to build a tumor microenvironment model
2	Use AuraPert to predict gene perturbations and potential drug responses
3	Combine with clinical phenotypes to screen candidate targets and patient stratification biomarkers
4	Set joint R&D milestones to advance model predictions and experimental validation