Ai-based virtual staining platform for wound healing screening
An AI-powered virtual staining platform using a vascularized skin-on-chip for high-content wound healing screening. It enables low-cost, multi-parametric monitoring of wound healing without traditional staining methods.
Overview
Xellar Biosystems introduces an innovative AI-based virtual staining platform designed to revolutionize wound healing compound screening. By utilizing a vascularized skin-on-chip model integrated with keratinocytes, fibroblasts, endothelial, and immune cells, the platform simulates a realistic wound healing environment. This advanced system leverages a U-Net AI model to virtually stain label-free brightfield images, facilitating the prediction of mechanism-relevant markers. This enables continuous, low-cost, and multi-parametric monitoring of wound healing processes, transforming traditional organ-chip assays into high-throughput, mechanism-informed screening tools for pharmaceutical and consumer health research and development.
Technical specifications
Key features:
- Vascularized skin-on-chip model replicates all four phases of wound healing: hemostasis, inflammation, proliferation, and remodeling.
- U-Net AI model trained to translate brightfield images into five key readouts: proliferation, migration, fibroblast activation, vascular remodeling, and collagen organization.
- Enables non-invasive, high-resolution kinetic data capture, reducing reliance on traditional staining methods.
- Produces a Regeneration Score for quantitative, unbiased prioritization of bioactive modulators.
Technology readiness level
This solution is at Technology Readiness Level 4, indicating a technology validated in a laboratory environment. Future plans include standardization, AI development, and qualification phases, aiming to deliver a screening-ready service within 12 months.
About Xellar Biosystems
Xellar Biosystems is a biotechnology company that integrates AI-driven data analytics with a proprietary organ-on-chip platform to advance drug discovery and preclinical research. Their technology platform, which incorporates multicellular 3D disease models, simulates human physiological environments by replicating vascularization, biomechanical forces, and paracrine signaling. By utilizing pharmaceutical-scale engineering and high-content imaging, the system generates complex phenotypic and multi-omics data, which is subsequently analyzed by machine learning pipelines to identify promising drug candidates and evaluate therapeutic responses with higher predictive accuracy than traditional 2D culture or animal models.
This approach addresses critical limitations in the drug development process, such as high failure rates, significant costs, and long development timelines. By providing physiologically relevant human models that are compatible with high-throughput screening and laboratory automation, Xellar Biosystems assists pharmaceutical researchers in making more informed decisions earlier in the discovery pipeline. Their technology is designed to reduce the reliance on animal testing, facilitate superior risk assessment, and accelerate the transition of drug candidates into clinical evaluation through robust mechanistic insights.
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