Matterworks AI-Powered
AI-Powered Tools for the Future of Biological Engineering

The contributions of metabolomics to systems biology, biomarker discovery, disease metabolism, and the development of biologics using analytical tools like LC-MS have garnered attention in this rapidly developing omics field.

Its successful applications in human health research have propelled the development of new technologies, promising richer and more complete analysis capabilities of small biomolecules versus existing market solutions.

Matterworks, an early-stage biotech research firm, is developing a fundamentally enabling platform technology that harnesses dramatic advances in machine learning (ML) and artificial intelligence (AI) to extract new biological insights from raw omic data. This firm’s first product, Pyxis, applies advanced ML to raw metabolomic data, rapidly generating absolute concentrations for a broad set of key metabolites that enable real-time biological insights.

“Pyxis reinvents the metabolomic analysis processes using ML, making it faster, quantitative, and scalable. This allows us to apply metabolomic insights in crucial areas like generating new hypotheses for markers and metabolomic interpretation of underlying cellular and biological data,” says Jack Geremia, CEO of Matterworks.

Currently in the beta testing stage, the firm is demonstrating the efficiency of Pyxis to top pharmaceutical firms worldwide.

Matterworks will offer cloud-based software that enables companies to automatically transform raw MS data into metabolite concentrations without peak picking or curve integration. To simplify the adoption of Pyxis, Matterworks provides a sample prep kit with universal calibrators and runs on a single LC-MS method on users’ existing LC-MS equipment. This function will empower users to utilize metabolomic insights for cancer immunotherapy research, vaccine development for deadly diseases, and microbiome therapeutics.

Pyxis has gained immense attention through its successful trials by demonstrating speed to result and breadth of data versus what can be generated now. This speed and breadth of data collection promises biopharma a tool to improve bioprodution efficiency, clone picking, and to detect process excursions.

Pharmaceutical companies can also use the platform to distinguish various classes of phenotypes compared to conventional metabolomic analysis technologies.

Employing Pyxis will minimize the LC-MS acquisition time to about five minutes per sample. It calculates the absolute metabolite concentration rapidly within the timeframe of the batch production of biologics like therapeutic proteins. This rapid result generation will also allow researchers to draw the necessary intracellular data within the timeframe of the batch.

Matterworks is currently reaching out and demonstrating Pyxis to potential clients who require absolute quantitation of biologics in a rapid and scalable manner. The firm initially runs the platform technology on top of its existing LC-MS platform to ensure a seamless performance. In addition, Matterworks plans to launch Pyxis commercially within the next 12 months.

Revolutionary breakthroughs in biology are only possible with technologies that draw insights from massive amounts of complex biological data. However, the technologies available for extracting metabolomic insights are far behind compared to platforms generating data. Matterwork’s efforts in developing AI-based tools to engineer biology foster innovations in medicine, sustainability, and bio-economy for the benefit of humanity.