We recognize the future of cancer treatment is precision combination therapy, and we built Aiomic to understand the specific drivers of each patient’s disease and design the most effective treatments.
Alongside next-generation oncology drugs, CSTS has established a strong systems biology team to bring together the insights from multiple branches of science and medicine – to explain the molecular characteristics of cancer and decode tumor-host interactions. Since inception, CSTS in-house development of systems biology techniques and custom AI infrastructure has culminated in the Aiomic software platform. Aoimic is the first AI powered oncology platform that automatically identifies and prioritizes therapeutic target combinations using high fidelity biological model of tumor function based on genomic information. The target selection platform powers our research and our vision of genomic driven oncology, and has generated a rich pipeline of therapeutic target combinations priorities for our drug development.
Next Generation Therapy Design
The presence of a DNA gene alteration does not imply pathogenicity. A DNA mutation not reflected in altered RNA is therapeutically less relevant. Similarly, DNA and RNA are alone are insufficient to understand a cancer. We recognize that no single level molecular analysis is sufficient and that the correct identification of clinically actionable gene targets requires cross-referencing the molecular information across the multiple levels.
Aiomic currently incorporates protein, their modifications, and cancer hallmark analysis to analyze the tumor microenvironment. A proprietary thermodynamic framework allows Aiomic to integrate patient diagnostic information using novel systems biology techniques and in-house built AI technologies.
How Aiomic Works
Our algorithms identify the network of molecules most likely driving the cancer by cross referencing RNA information with corresponding DNA alterations to determine the most energetically active sub-network based on entropy. The use of RNA expression data allows analysis of protein-protein interactions through a proprietary thermodynamic calculation which assigns a Gibbs free energy value to each molecule in the network. The process yields an energetically active sub-network of molecules in which tumor-host interactions are explained using the Cancer Hallmarks.
From this energetically active sub-network, Aiomic algorithms rank effective combinations of targets for treatment. Using a patient specific virtual tumor model, Aiomic automatically generates a rationale which references supporting scientific evidence and peer-reviewed publications to explain why the selection of targets is an effective combination.
A New Class of Platelet-Facilitated Drugs (PFDs)
We are enabling better cancer treatments beginning with two ground breaking therapies. CYT005 is a first in class peptide conjugate, while CYT015 is a best in class small molecule.
