Networx.bio provides a computational biology platform that maps and scores all tumor-immune receptor-ligand inhibitory interactions per patient, aiming to predict immunotherapy response with minimal samples. The company’s NetwoRx Score quantifies specific molecular “brakes” like PD-1/PD-L1, offering mechanistic insights beyond standard biomarkers. This enables precise patient enrichment and more efficient clinical trials across cancer indications.
Funding
Funding not disclosed
Founders
Product
Problem
Current immune checkpoint inhibitors (ICIs) are approved in about 20 cancers but suffer from inefficient biomarkers, making it hard to match drugs to patients. 70-80% of patients do not respond to ICIs, and 96% of clinical trials fail, largely because tumor complexity exceeds what current technologies can analyze.
Solution
Networx.bio maps and scores all receptor-ligand inhibitory interactions—the “brakes” on the immune system—for each patient across 30+ cell types, including immune subsets, stroma, and tumor cells. Using proprietary computational tools, the company integrates deep transcriptomic data from physically sorted cells to generate a per-patient NetwoRx Score for each molecular interaction (e.g., PD-1/PD-L1). Initial data show strong correlation between these scores and ICI drug effectiveness, suggesting the platform can predict responses to specific drugs and combinations. This mechanistic approach aims to replace inaccurate scRNA-Seq methods, enabling successful trials with minimal patient cohorts and faster wins in immuno-oncology.
Target Audience
Primary customers are biopharmaceutical companies and clinical trial sponsors developing immunotherapies, especially those seeking to enrich patient cohorts, de-risk trials, and select optimal drug combinations in immuno-oncology.
Features
- Mechanistic tumor microenvironment mapping that quantifies and scores all receptor-ligand inhibitory pairs, not just PD-1/PD-L1
- Integration of exceptionally reproducible data covering 30+ immune subsets, stroma, and tumor cells with deep transcriptomes from physically sorted cells
- Proprietary computational tools that convert correlation into causality, predicting responses to ICIs and targeted therapies across tumor types and drug classes
- NetwoRx Score per patient per molecular interaction, providing unprecedented depth into tumor-immune communication
- Predictive power with very few patients or samples, enabling minimal patient cohorts and maximal signal