{"id":221265,"date":"2025-10-23T22:09:35","date_gmt":"2025-10-23T22:09:35","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/221265\/"},"modified":"2025-10-23T22:09:35","modified_gmt":"2025-10-23T22:09:35","slug":"new-ai-tool-pinpoints-genes-drug-combos-to-restore-health-in-diseased-cells","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/221265\/","title":{"rendered":"New AI Tool Pinpoints Genes, Drug Combos To Restore Health in Diseased Cells"},"content":{"rendered":"

Work described in this story was made possible in part by federal funding supported by taxpayers. At Harvard Medical School, the future of efforts like this \u2014 done in service to humanity \u2014 now hangs in the balance due to the government\u2019s decision to terminate large numbers of federally funded grants and contracts across Harvard University.<\/p>\n

In a move that could reshape drug discovery, researchers at Harvard Medical School have designed an artificial intelligence model capable of identifying treatments that reverse disease states in cells.<\/p>\n

Unlike traditional approaches that typically test one protein target or drug at a time in hopes of identifying an effective treatment, the new model, called PDGrapher and available for free<\/a>, focuses on multiple drivers of disease and identifies the genes most likely to revert diseased cells back to healthy function.<\/p>\n

Get more HMS news<\/a><\/p>\n

The tool also identifies the best single or combined targets for treatments that correct the disease process. The work, described Sept. 9 in Nature Biomedical Engineering<\/a>, was supported in part by federal funding.<\/p>\n

By zeroing in on the targets most likely to reverse disease, the new approach could speed up drug discovery and design and unlock therapies for conditions that have long eluded traditional methods, the researchers noted.<\/p>\n

\u201cTraditional drug discovery resembles tasting hundreds of prepared dishes to find one that happens to taste perfect,\u201d said study senior author Marinka Zitnik<\/a>, associate professor of biomedical informatics in the Blavatnik Institute at HMS and associate faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. \u201cPDGrapher works like a master chef who understands what they want the dish to be and exactly how to combine ingredients to achieve the desired flavor.\u201d<\/p>\n

The traditional drug-discovery approach \u2014 which focuses on activating or inhibiting a single protein \u2014 has succeeded with treatments such as kinase inhibitors, drugs that block certain proteins used by cancer cells to grow and divide. However, Zitnik noted, this discovery paradigm can fall short when diseases are fueled by the interplay of multiple signaling pathways and genes. For example, many breakthrough drugs discovered in recent decades \u2014 think immune checkpoint inhibitors<\/a> and CAR T-cell therapies<\/a> \u2014 work by targeting disease processes in cells.<\/p>\n

The approach enabled by PDGrapher, Zitnik said, looks at the bigger picture to find compounds that can actually reverse signs of disease in cells, even if scientists don\u2019t yet know exactly which molecules those compounds may be acting on.<\/p>\n

How PDGrapher works: Mapping complex linkages and effects<\/p>\n

PDGrapher is a type of artificial intelligence tool called a graph neural network. This tool doesn\u2019t just look at individual data points but at the connections that exist between these data points and the effects they have on one another.<\/p>\n

In the context of biology and drug discovery, this approach is used to map the relationship between various genes, proteins, and signaling pathways inside cells and predict the best combination of therapies that would correct the underlying dysfunction of a cell to restore healthy cell behavior. Instead of exhaustively testing compounds from large drug databases, the new model focuses on drug combinations that are most likely to reverse disease.<\/p>\n

PDGrapher points to parts of the cell that might be driving disease. Next, it simulates what happens if these cellular parts were turned off or dialed down. The AI model then offers an answer as to whether a diseased cell would happen if certain targets were \u201chit.\u201d<\/p>\n

\u201cInstead of testing every possible recipe, PDGrapher asks: \u2018Which mix of ingredients will turn this bland or overly salty dish into a perfectly balanced meal?\u2019\u201d Zitnik said.<\/p>\n

Advantages of the new model<\/p>\n

The researchers trained the tool on a dataset of diseased cells before and after treatment so that it could figure out which genes to target to shift cells from a diseased state to a healthy one.<\/p>\n

Next, they tested it on 19 datasets spanning 11 types of cancer, using both genetic and drug-based experiments, asking the tool to predict various treatment options for cell samples it had not seen before and for cancer types it had not encountered.<\/p>\n

The tool accurately predicted drug targets already known to work but that were deliberately excluded during training to ensure the model did not simply recall the right answers. It also identified additional candidates supported by emerging evidence. The model also highlighted KDR (VEGFR2) as a target for non-small cell lung cancer, aligning with clinical evidence. It also identified TOP2A \u2014 an enzyme already targeted by approved chemotherapies \u2014 as a treatment target in certain tumors, adding to evidence from recent preclinical studies that TOP2A inhibition may be used to curb the spread of metastases in non-small cell lung cancer.<\/p>\n

The model showed superior accuracy and efficiency, compared with other similar tools. In previously unseen datasets, it ranked the correct therapeutic targets up to 35 percent higher than other models did and delivered results up to 25 times faster than comparable AI approaches.<\/p>\n

What this AI advance spells for the future of medicine<\/p>\n

The new approach could optimize the way new drugs are designed, the researchers said. This is because instead of trying to predict how every possible change would affect a cell and then looking for a useful drug, PDGrapher right away seeks which specific targets can reverse a disease trait. This makes it faster to test ideas and lets researchers focus on fewer promising targets.<\/p>\n

This tool could be especially useful for complex diseases fueled by multiple pathways, such as cancer, in which tumors can outsmart drugs that hit just one target. Because PDGrapher identifies multiple targets involved in a disease, it could help circumvent this problem.<\/p>\n

Additionally, the researchers said that after careful testing to validate the model, it could one day be used to analyze a patient\u2019s cellular profile and help design individualized treatment combinations.<\/p>\n

Finally, because PDGrapher identifies cause-effect biological drivers of disease, it could help researchers understand why certain drug combinations work \u2014 offering new biological insights that could propel biomedical discovery even further.<\/p>\n

The team is currently using this model to tackle brain diseases such as Parkinson\u2019s and Alzheimer\u2019s, looking at how cells behave in disease and spotting genes that could help restore them to health. The researchers are also collaborating with colleagues at the Center for XDP<\/a> at Massachusetts General Hospital to identify new drug targets and map which genes or pairs of genes could be affected by treatments for X-linked Dystonia-Parkinsonism, a rare inherited neurodegenerative disorder.<\/p>\n

\u201cOur ultimate goal is to create a clear road map of possible ways to reverse disease at the cellular level,\u201d Zitnik said.<\/p>\n","protected":false},"excerpt":{"rendered":"Work described in this story was made possible in part by federal funding supported by taxpayers. At Harvard…\n","protected":false},"author":2,"featured_media":221266,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[916,90,56,54,55],"class_list":{"0":"post-221265","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-genetics","8":"tag-genetics","9":"tag-science","10":"tag-uk","11":"tag-united-kingdom","12":"tag-unitedkingdom"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/221265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=221265"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/221265\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/221266"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=221265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=221265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=221265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}