InterVenn’s unique, proprietary workflow integrates advanced liquid chromatography and mass spectrometry (LC-MS) with artificial intelligence-based software, to create a novel platform identifying glycoproteins as clinical biomarkers. These assays overcome many of the traditional barriers that have prevented researchers to unlock the power of glycoproteomics, allowing physicians and research partners to answer questions that current technologies struggle to address and opening new avenues for discovery and advancement.
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Intervenn’s platform integrates machine learning with high performance liquid chromatography and mass spectrometry instruments (LC-MS) to develop clinically relevant glycopeptide panels to be used for pre-clinical research, clinical development, and diagnostics. We have configured these array instruments to meet our specific needs, delivering accurate and reproducible glycopeptide analyte identification and quantification.
MS can detect minute quantities of multiple molecules in complex biological matrices simultaneously and can accurately identify and quantify a wide range of biomolecules, including proteins, peptides, lipids and small molecules, etc. Historically, the challenge in glycoproteomic studies has been reliably identifying the pattern of glycosylation and the glycopeptide abundance. By taking advantage of optimized separations on the Liquid Chromatography system and the power of tandem mass spectrometry, InterVenn has developed a suite of assays to identify and quantify a wide range of glycopeptides and their glycan structures.
In summary, MS offers a powerful combination of sensitivity, specificity, and versatility that makes it a valuable tool for biomarker discovery, validation and clinical applications.
InterVenn’s approach to employing AI solutions is predominantly focused on signal processing and expanding the capabilities of mass spectrometry, to determine the abundance of glycopeptides in a quantitative manner. Specifically, we have built neural networks that eliminate the need for manual assessment within targeted MS experiments and deconvolute the enormous number of spectral signals present in glycoprotein-based discovery experiments.
Downstream of our AI-based signal processing method, InterVenn utilizes both statistical and machine learning methods to translate biomarker expression into precision medicine applications. This has the dual benefits of robustness and interpretability associated with historical biomarker assays, and contrasts with the risks around employing black box, AI-based clinical predictions.
Peak Integration Platform (PIP) is a proprietary software that translates the complex output of the LC-MS instrument into easily interpretable, quantitative biomarker data. It is powered by artificial intelligence and recurrent neural networks and was trained on a large collection of expert-annotated chromatographic peaks.
It reduces the time required to interpret LC-MS data from months to minutes, and, for the first time, has allowed glycoproteomic research to scale to meet the requirements of large clinical studies.
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