Computer program can predict how cancerous tumors will respond to drugs before dosing starts


A new IT tool has been developed for predict how certain cancers may react experimental treatment even before it has been administered to a patient.

First reported by FirstWord Pharmaceuticals, the computer program is able to predict how tumors might become resistant to treatment even before it becomes evident in a clinical trial. The computer model was developed at The Cancer Research Institute. The program, which was described in a new article published in the journal Cellular Chemical Biology, could potentially allow researchers to start “working on second-generation drugs to combat drug resistance before the first-generation drug is given to patients.” FirstWord Pharmaceuticals reported.

Not only that, but the program could also lead to the development of tests that would determine whether or not patients had certain mutations in their cancer that would be resistant to certain potential treatments. This determination would give the first chance to deliver personalized medicines to a patient, FirstWord Pharma noted. The computer program was also able to predict which part of a cancerous tumor might have multiple mutations that would require multiple treatment options. The computer program “prioritized mutations” in these areas based on the likelihood that they formed in the type of cancer being studied, according to the report.

“Our new approach can predict which mutations are likely to arise in response to drug therapy in different types of tumors,” said Teresa Kaserer, chief scientist at the London-based Cancer Institute. FirstWord Pharma. “This will be extremely beneficial for the design of new cancer drugs. Instead of reacting to what we see in the clinic – when it’s too late because patients have stopped responding to treatment – we can use our computer method to predict during the drug design phase how resistance will arise.

Kaserer told the publication that the success of the computer program means researchers can develop tests that will select patients for particular treatments and better monitor them while they take the drug.

“This could be great news for patients, who could switch to a second generation drug as soon as a resistance mutation emerges,” Kaserer said.

As part of the program’s development, researchers tested the computer model on existing anticancer drugs and drug targets. This included 17 different drugs that target cancer-related proteins, such as MAPK1, KIT, EGFR, Abl, and ALK, FirstWord Pharmaceuticals noted. The program was able to “accurately predict many of the mutations doctors see in the clinic,” according to the article. For the MAPK protein, the computer was able to predict many that were generated in the laboratory, FirstWord Pharmaceuticals added.

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Gordon K. Morehouse