American Association for Cancer Research Annual Meeting

American Association for Cancer Research Annual Meeting

Source:

Kucukosmanoglu A, et al. Abstract 1908. Presented at: American Association for Cancer Research Annual Meeting (hybrid meeting); April 8-13, 2022.

Disclosures: Brain Tumor Charity, Health~Holland and Innovation Exchange Amsterdam supported the study. Westerman reports a public-private partnership grant from Health~Holland. Please see the abstract for all other researchers’ relevant financial disclosures.
April 13, 2022
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AI model may predict adverse events of combination therapies for various cancer types

Source:

Kucukosmanoglu A, et al. Abstract 1908. Presented at: American Association for Cancer Research Annual Meeting (hybrid meeting); April 8-13, 2022.

Disclosures: Brain Tumor Charity, Health~Holland and Innovation Exchange Amsterdam supported the study. Westerman reports a public-private partnership grant from Health~Holland. Please see the abstract for all other researchers’ relevant financial disclosures.
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An artificial intelligence model appeared to predict adverse events associated with novel combination therapies for various cancer types, according to study results presented at American Association for Cancer Research Annual Meeting.

Rationale and methods

“In our lab, we aim to identify effective multitargeted therapies against cancer and translate the findings to the clinic,” Bart Westerman, PhD, associate professor in the department of neurosurgery at Brain Tumor Center Amsterdam, told Healio. “We found that many obstacles prohibit this translation, one of them being adverse events, which are caused by desired but also undesired targets of drugs. To predict the feasibility of approach in a real-life setting, we created a model to predict the adverse events of drug combinations.”

Quote from Bart Westerman, PhD.

Westerman and colleagues pooled data from the FDA Adverse Event Reporting System (FAERS) database, which includes 15 million records of adverse events, and developed a method to predict the adverse events of drug combinations for the purpose of selecting those with mild adverse event profiles. They then fed the adverse event profiles into a convolutional neural network algorithm — machine learning that mimics the way human brains make associations between data.

Researchers provided unseen adverse event profiles of drug combinations to the model, which they dubbed “adverse events atlas,” to assess whether the model could recognize and decode them using the latent space descriptors.

Key findings

Results showed the model recognized the new patterns, indicating that measured combined profiles may be converted back into those of each agent included in the drug combination.

Moreover, the model accurately reviewed adverse event profiles for some of the most used combination therapies based on data from FAERS and the U.S. clinical trials database.

Researchers reported limitations of the study, including the potential difficulties in comparing the data with more sparse data and the limited application of the models to clinical practice until further validation.

Looking ahead

“We are currently extracting adverse event data from clinical records in our hospital. This adverse event data is hidden in unstructured text — yet another obstacle that prohibits our understanding of drug combinations in a real-life setting,” Westerman said. “We are using natural language processing and parsing to extract relevant information and, in the future, we aim to link these data to our adverse event prediction model that could have value in the clinic, when proven to do so.”

Westerman added that the researchers are validating the value of their findings by using other unseen data.

“We additionally want to link our model to data from the clinic, which is currently ongoing,” he said. “In parallel, we are building a prediction model to predict the effects of multitarget combinations based on clinical data and will use the adverse event data to better understand on and off targets of drugs in the context of therapy efficacy.”