LONDON:Scientists have developed a newartificial intelligence(AI) model that could help treat cancer more effectively by identifying the best drug combinations to selectively killcancer cellswith specific genetic or functional makeup. The new machine learning method, described in the journal Nature Communications, found associations between drugs and cancer cells that were not observed previously.
The model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells.
“The model learned by the machine is actually a polynomial function familiar from school mathematics, but a very complex one,” said Professor Juho Rousu from Aalto University in Finland.
The researchers noted that combination 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, they said.
“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,” Rousu said.
In experimental measurements, a correlation coefficient of 0.8-0.9 is considered reliable, the researchers said.
The model accurately predicts 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, they said.
“This will help cancer researchers to prioritise which drug combinations to choose from thousands of options for further research,’ said Tero Aittokallio from the University of Helsinki in Finland.
The researchers noted that the same machine learning approach could be used for non-cancerous diseases.
They said, in that 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, according to the researchers.
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