A research team from the Korea Advanced Institute of Science and Technology has created an AI model to forecast negative interactions between oral anti-COVID-19 medicine and prescription pharmaceuticals.
Ritonavir and nirmatrelvir, two ingredients in Pfizer’s Paxlovid, were tested for potential drug interactions by researchers from KAIST’s Department of Biochemical Engineering using a new iteration of the DeepDDI AI-based drug interaction prediction model.
A press release stated that the new model DeepDDI2 can compute for and process a total of 113 different types of drug-drug interactions.
Paxlovid turned out to interact with about 2,248 prescription medications, including 1,403 drugs that include ritonavir and 673 medicine that contain nirmatrelvir.
The researchers found 239 medications with nirmatrelvir and 124 pharmaceuticals with ritonavir that had the minimal potential for adverse reactions, which they suggested as alternatives for prescription drugs with high adverse reactions to Paxlovid.
The Need for an AI Model
COVID-19 people are likely to receive antiviral medication and other medications if they have comorbid conditions such as diabetes or high blood pressure. Paxlovid’s adverse drug responses and drug-drug interactions “have not been thoroughly analyzed,” according to the KAIST researchers. They then began investigating, using AI technology, how continuous antiviral therapy in combination with other medications can result in detrimental and unintended side effects.
The Future Trend of AI Model
Paxlovid’s full approval by the US Food and Drug Administration is edging closer to Pfizer. An advisory panel last week decided to recommend the approval of the medicine because it believes it to be safe and effective. The regulatory authority authorized the use of Paxlovid’s emergency for the corporation in December 2021. The US FDA is likely to make a final decision on its complete approval by May following the advisers’ vote.
Conclusion
“The findings of this study are significant in cases where we might be forced to use hastily created medications in response to emergencies like the COVID-19 pandemic. With DeepDDI2, it is now able to promptly identify and combat adverse drug reactions brought on by drug-drug interactions, “Sang Yup Lee, a professor at KAIST, said in a statement.
