AI Collaboration Targets Chemo Resistance
Evogene and Queensland University of Technology announced a collaboration to advance AI-driven cancer therapeutics. The project will use Evogene's generative AI platform to develop treatments designed to overcome chemotherapy resistance.
- The collaboration is specifically targeting non-small cell lung cancer (NSCLC), which is a common and aggressive form of the disease. The project will focus on overcoming resistance to a cornerstone chemotherapy drug, Cisplatin. Research from Dr. Mark Adams' lab at QUT identified a specific cellular detoxification pathway that drives this resistance, and this is the pathway the collaboration aims to block. - Evogene's AI platform, called ChemPass AI, has been trained on a massive dataset of 38 billion molecules to help it design new, effective drug candidates. It works to optimize multiple factors at once, including a potential drug's potency, toxicity, and how easily it can be synthesized. The platform is also getting an upgrade by integrating AI agents using Google Cloud's Vertex AI to speed up the discovery cycle. - Chemotherapy resistance is a major cause of treatment failure, responsible for over 90% of deaths in cancer patients undergoing chemotherapy. In the case of NSCLC, 60-70% of patients show intrinsic resistance to Cisplatin from the start. - Cancer cells develop resistance through various mechanisms, such as pumping the drug out of the cell, repairing the DNA damage caused by the chemo, or altering the drug's target. This project focuses on designing small-molecule inhibitors to block the specific detoxification pathway, effectively restoring the cancer cells' sensitivity to the treatment. - This partnership exemplifies how different scientific roles collaborate. Dr. Mark Adams' team at QUT provides the deep biological understanding of the cancer and its resistance mechanism. Evogene's team, likely composed of computational biologists and AI engineers, uses that biological insight to guide their AI platform in designing potential drugs. - The process is iterative. Evogene's AI will generate potential drug designs, which QUT's lab will then test experimentally. The results of these real-world tests are then fed back into the AI model to refine and improve the next generation of drug candidates.
