• Tue. Mar 21st, 2023

AI-primarily based drug interaction prediction technologies analyzes the interaction in between Paxlovid components


Mar 16, 2023

Mar 16 2023

KAIST (President Kwang Hyung Lee) announced on the 16th that an sophisticated AI-primarily based drug interaction prediction technologies created by the Distinguished Professor Sang Yup Lee’s study group in the Division of Biochemical Engineering that analyzed the interaction in between the PaxlovidTM components that are utilized as COVID-19 therapy and other prescription drugs was published as a thesis. This paper was published in the on the web edition of 「Proceedings of the National Academy of Sciences of America」(PNAS), an internationally renowned academic journal, on the 13th of March.

In this study, the study group created DeepDDI2, an sophisticated version of DeepDDI, an AI-primarily based drug interaction prediction model they created in 2018. DeepDDI2 is capable to compute for and method a total of 113 drug-drug interaction (DDI) forms, much more than the 86 DDI forms covered by the current DeepDDI.

The study group utilized DeepDDI2 to predict attainable interactions in between the components (ritonavir, nirmatrelvir) of Paxlovid, a COVID-19 therapy, and other prescription drugs. The study group mentioned that when amongst COVID-19 sufferers, higher-threat sufferers with chronic illnesses such as higher blood stress and diabetes are most likely to be taking other drugs, drug-drug interactions and adverse drug reactions for Paxlovid have not been sufficiently analyzed, but. This study was pursued in light of seeing how continued usage of the drug may possibly lead to really serious and undesirable complications.

The study group utilized DeepDDI2 to predict how Paxrovid’s elements, ritonavir and nirmatrelvir, would interact with two,248 prescription drugs. As a outcome of the prediction, ritonavir was predicted to interact with 1,403 prescription drugs and nirmatrelvir with 673 drugs.

Making use of the prediction final results, the study group proposed option drugs with the identical mechanism but low drug interaction possible for prescription drugs with higher adverse drug events (ADEs). Accordingly, 124 option drugs that could minimize the attainable adverse DDI with ritonavir and 239 option drugs for nirmatrelvir had been identified.

By way of this study achievement, it became attainable to use an deep understanding technologies to accurately predict drug-drug interactions (DDIs), and this is anticipated to play an vital part in the digital healthcare, precision medicine and pharmaceutical industries by delivering valuable facts in the method of creating new drugs and creating prescriptions.

Distinguished Professor Sang Yup Lee mentioned, “The final results of this study are meaningful at occasions like when we would have to resort to utilizing drugs that are created in a hurry in the face of an urgent circumstances like the COVID-19 pandemic, that it is now attainable to recognize and take vital actions against adverse drug reactions brought on by drug-drug interactions incredibly swiftly.”

This study was carried out with the help of the KAIST New-Deal Project for COVID-19 Science and Technologies and the Bio·Medical Technologies Improvement Project supported by the Ministry of Science and ICT.


KAIST (Korea Sophisticated Institute of Science and Technologies)

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