Xellarbio integrates machine learning and AI with large-scale wet-lab experiments to address complexity in drug discovery. The company utilizes pharma-scale organ-on-a-chip technology combined with image-based AI for pre-clinical testing. This approach aims to accelerate the development pipeline by better modeling human biology.
Funding
$24M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
LCTIYCZIFounders
Product
Problem
Traditional drug discovery methods rely on simplified models that fail to capture the complexity of human biology, leading to inaccurate preclinical data and hindering the development of effective therapeutics. These limitations result in increased costs and prolonged timelines for bringing new drugs to market.
Solution
Xellar Biosystems offers a pharma-scale organ-on-chip platform that replicates human biological systems for more accurate drug testing. By combining large-scale wet-lab experiments with image-based AI and machine learning, the platform provides a more holistic and interconnected representation of human biology. This approach enables researchers to generate preclinical data that better reflects human responses to drugs, accelerating the identification and development of promising therapeutic candidates. The platform integrates engineering, pre-clinical development, imaging, and computational modules to provide a comprehensive solution for drug discovery.
Target Audience
The primary customers are pharmaceutical companies and research institutions seeking to improve the accuracy and efficiency of their drug discovery process.
Features
- Micro-organ matrix engineering to create realistic in-vitro models
- Preclinical development platform for compound testing and validation
- High-content imaging platform for detailed data acquisition
- AI/ML system for data analysis and predictive modeling
- Scalable platform suitable for pharma-scale drug discovery
- Integration of wet-lab experiments with AI-driven computation