Researchers have developed a machine learning model to accurately predict how combinations of different cancer drugs kill various types of cancer cells. The new AI model was edified with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells.
A team of researchers from Aalto University, the University of Helsinki, and the University of Turku in Finland have published the findings in the journal Nature Communications
Professor Juho Rousu from Aalto University said, "the model learned by the machine is actually a polynomial function familiar from school mathematics, but a very complex one'.
When healthcare professionals treat patients suffering from advanced cancers, they usually need to use a combination of different therapies. In addition to cancer surgery, the patients are often treated with radiation therapy, medication, or both.
According to the study, medication can be combined, with different drugs acting on different cancer cells. Combinatorial drug therapies often improve the effectiveness of the treatment and can reduce the harmful side-effects if the dosage of individual drugs can be reduced.
However, experimental screening of drug combinations is very slow and expensive, and therefore, often fails to discover the full benefits of combination therapy. With the help of a new machine learning method, one could identify the best combinations to selectively kill cancer cells with specific genetic or functional makeup.
The research results demonstrated that the model found associations between drugs and cancer cells that were not observed previously. 'The model gives very accurate results. For example, the values of the so-called correlation coefficient were more than 0.9 in our experiments, which points to excellent reliability,' said Professor Rousu.
In experimental measurements, a correlation coefficient of 0.8-0.9 was considered reliable.
The model accurately predicted how a drug combination selectively inhibits particular cancer cells when the effect of the drug combination on that type of cancer has not been previously tested.Researcher Tero Aittokallio from the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki told that this would be helpful for cancer researchers to prioritize which drug combinations to choose from thousands of options for further research.
"The same machine learning approach could be used for non-cancerous diseases. In this case, the model would have to be re-taught with data related to that disease. For example, the model could be used to study how different combinations of antibiotics affect bacterial infections or how effectively different combinations of drugs kill cells that have been infected by the SARS-Cov-2 coronavirus," said Aittokallio.
( With inputs from ANI )
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