Imagine a world where you could. . .
• Match patients with ideal therapeutics
• Identify robust therapeutic signatures
• Discover resistance mechanisms during a clinical trial
• Tailor tools to predict actual patient outcome
• Discover curative therapies rapidly and efficiently
Revolutionizing Therapeutic Discovery
Yatiri Bio was created to address the inefficiencies of cancer drug discovery and development, focusing on the translational gap between discovery and clinical success. The inherent heterogeneity in cancer patients requires both a new methodology and a systemwide approach. Past experience and data have shown that simple models often lead to expensive failures. We can do better.
At Yatiri Bio we use unbiased proteomics, deep learning and patient derived cellular systems to create a molecularly defined patient landscape with empirically testable cellular systems embedded within it to identify the ideal patient subsets for novel therapeutics
Exploring the Depths
Proteomics is the large-scale study of the proteome, which consists of all the proteins produced in an organism, cell, or biological system. Using high resolution mass spectrometry, data from the proteome can provide insight into proteins and their signaling pathways as the drivers of human physiology and disease. Yatiri Bio explicitly prioritizes this data as the key to understanding how and why particular drugs successfully eradicate disease. Recent advances in this technology allow for the identification and quantification of thousands ( 8,000 + ) of proteins per sample. In addition, the signaling pathways can be accurately monitored via analysis of post translational modifications (PTM) that steer biochemical responses. Consequently, these analyses address the relevant disease on a cellular level including direct observation of the effects of drug perturbation. The advantage of studying the proteome (versus the current standard of identifying only a selective few biomarkers) is that we are able to identify more robust biomarker signatures, improve patient selection, and drive pathway-based therapies for personalized medicine.
Bioinformatics
Yatiri Bio develops proprietary computational tools that employ machine learning and artificial intelligence techniques to explore proteomics data in the context of human biology. A fully interactive data portal, ProteoBrowserTM, enables classical analyses such as differential expression, pathway enrichment, sample outlier tests, and metadata associations, as well as providing a backbone for more complex pipelines. Yatiri Bio has developed a novel deep learning architecture that extends beyond traditional statistical modeling by integrating proteomics and drug sensitivity assays for actionable knowledge. By employing our vast experience in drug development and deep understanding of experimental design we build datasets that are optimal for driving pre-clinical success through capturing the diversity in heterogeneous patient populations.
Pre-Clinical Models
Yatiri Bio has developed a portfolio of cellular models backed by clinical proteome data that are tailored to match molecularly defined cancer subtypes. Our ProteoModelsTM can be used for optimized patient selection, rational exploration of combinational therapies, drug repurposing, identifying resistance mechanisms, and drug discovery with robust and translatable biomarkers from the outset.
From Data To Knowledge
To help our customers succeed in bringing novel and effective individualized oncology treatments to patients, Yatiri Bio uses cutting edge artificial intelligence and machine learning to extract the maximum benefit from our proprietary patient proteomics datasets. Yatiri Bio works with our partners toward a better understanding of both the mechanisms by which the drug functions as well as the patient subsets in whom these particular mechanisms will result in positive clinical outcomes. Together, we can capitalize on these previously undervalued and underutilized opportunities.