Researchers at King's College Hospital and Queen Mary University of London have developed an AI algorithm which can prescribe the most effective treatment plan for patients diagnosed with primary liver cancer. The computer-based algorithm, named Drug Ranking Using Machine Learning (DRUML), classifies drugs used to treat bile duct cancer (a type of primary liver cancer), based on their efficacy in reducing cancer cell growth. The research into DRUML was recently published in Cancer Research, an American Association of Cancer Research journal. Researchers say that the software could be used in the future to predict individual patient responses to therapies to enable them to select the most effective treatment plan. Professor Pedro Cutillas, researcher at Queen Mary University of London, said: "Patients who are diagnosed with primary liver cancer often have a very poor prognosis. Hence why a one-size-fits-all approach to treatment is not the most effective way to reduce cancer cell growth and why we applied DRUML to this type of cancer."

The key metric used by DRUML is drug response distance (D), which is computed using empirical markers of drug responses identified in the training set. The D metric is the difference in overall expression of markers increased in drug sensitive cells relative to markers increased in drug resistant cells within a sample. Because D is an internally normalized metric, obtained by subtracting averaged signals from two sets of phosphosites, proteins, or transcripts within a given sample, DRUML can use D to predict drug responses in a new cancer-derived sample without comparing it against a control or reference sample set.

Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalised therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."

A big part of personalized medicine in cancer is knowing ahead of time if a drug is likely to be effective or not. That's usually done by identifying actionable genetic mutations. But a team of researchers recently developed a potentially quicker and more consistent tool based on omics data: a machine learning algorithm that ranks drugs based on their anti-proliferative efficacy in cancer cells. Known as Drug Ranking Using Machine Learning (DRUML), the method was developed at Queen Mary University in London and is based on machine learning analysis of protein omics data in cancer cells. DRUML was created based on training responses of cancer cells to 412 cancer drugs to predict the most appropriate one to treat a particular cancer.

The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. By training the models using the responses of these cells to 412 cancer drugs listed in drug response repositories, DRUML was able to produce ordered lists based on the effectiveness of the drugs to reduce cancer cell growth. This study represents a significant advancement in artificial intelligence in biomedical research, and demonstrates that machine learning using proteomics and phosphoproteomics data may be an effective way of selecting the best drug to treat different cancer models. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset.