{"id":51028,"date":"2024-09-06T11:06:46","date_gmt":"2024-09-06T10:06:46","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=51028"},"modified":"2024-09-06T11:06:46","modified_gmt":"2024-09-06T10:06:46","slug":"ai-unlocks-new-path-to-personalised-cancer-treatments","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/ai-unlocks-new-path-to-personalised-cancer-treatments\/51028\/","title":{"rendered":"AI unlocks new path to personalised cancer treatments"},"content":{"rendered":"

Researchers are leveraging AI and molecular simulations to uncover new pathways for personalised cancer treatments, paving the way for more effective therapies.<\/h2>\n

The team, led by Auburn University, in collaboration with scientists from the University of Basel and ETH Zurich, has developed a novel approach integrating AI with molecular dynamics simulations<\/a> and network analysis to enhance the prediction of binding sites on the PD-L1 protein.<\/p>\n

This breakthrough promises to accelerate the development of personalised cancer treatments by identifying critical interaction points in cancer-related proteins.<\/p>\n

\u201cUtilising computational tools to engineer proteins represents the next frontier in cancer therapeutics,\u201d said Dr Rafeal Bernardi, who led the team.<\/p>\n

\u201cOur integrated approach combining AI, molecular dynamics, and network analysis holds immense potential for developing personalised therapies for cancer patients.\u201d<\/p>\n

The use of AI in healthcare<\/a> could be instrumental in improving immunotherapies, such as pembrolizumab (Keytruda), that are already revolutionising cancer treatment.<\/p>\n

Mapping the future of personalised cancer treatments<\/h3>\n

One of the greatest challenges in personalised cancer therapeutics is accurately predicting where a drug can bind to its target protein. In this case, the researchers focused on PD-L1, a checkpoint protein that cancers exploit to suppress the immune system.<\/p>\n

Some modern drugs unleash the immune system to attack tumours by blocking PD-L1. However, understanding where exactly to target PD-L1 with new treatments has been a longstanding problem.<\/p>\n

The team developed a sophisticated method that combines AlphaFold2-based AI tools with molecular dynamics simulations and dynamic network analysis.<\/p>\n

Their approach allowed them to predict and confirm key binding regions in the PD-L1 protein that are critical for drug interaction.<\/p>\n

The computational approach was validated with cutting-edge experimental techniques, including cross-linking mass spectrometry and next-generation sequencing.<\/p>\n

These experiments confirmed the accuracy of the team\u2019s predictions, demonstrating the power of combining computational models with experimental validation to unravel complex protein-protein interactions and create personalised cancer treatments.<\/p>\n

The impact of protein interactions on future drug discovery<\/h3>\n

The implications of this study go far beyond PD-L1. The methods developed can be applied to many other proteins, potentially leading to the discovery of new drug targets for various diseases, including other types of cancer and autoimmune conditions.<\/p>\n

Additionally, this research paves the way for more cost-effective and rapid development of cancer therapeutics, an area where traditional experimental methods can be slow and expensive.<\/p>\n

\u201cThis research stresses the potential of computational tools like NAMD and VMD, combined with cutting-edge hardware such as NVIDIA DGX systems, to advance cancer therapeutics. Our findings mark a significant step toward developing new, targeted treatments for cancer,\u201d explained Dr Diego Gomes, lead author of the work and a researcher at Auburn.<\/p>\n","protected":false},"excerpt":{"rendered":"

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