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Title: IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development
Authors: Blasiak, Agata 
Lim, Jhin Jieh 
Seah, Shirley Gek Kheng
Kee, Theodore 
Remus, Alexandria 
Chye, De Hoe
Wong, Pui San
Hooi, Lissa 
Truong, Anh T. L. 
Le, Nguyen 
Chan, Conrad E. Z.
Desai, Rishi
Ding, Xianting
Hanson, Brendon J.
Chow, Edward Kai-Hua 
Ho, Dean 
Keywords: artificial intelligence
combinatory treatment
digital medicine
drug development
drug interactions
Issue Date: 1-Dec-2020
Publisher: Blackwell Publishing Ltd
Citation: Blasiak, Agata, Lim, Jhin Jieh, Seah, Shirley Gek Kheng, Kee, Theodore, Remus, Alexandria, Chye, De Hoe, Wong, Pui San, Hooi, Lissa, Truong, Anh T. L., Le, Nguyen, Chan, Conrad E. Z., Desai, Rishi, Ding, Xianting, Hanson, Brendon J., Chow, Edward Kai-Hua, Ho, Dean (2020-12-01). IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development. Bioengineering and Translational Medicine 6 (1) : e10196. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization suggests that IDentif.AI may be clinically actionable toward current and future outbreaks. © 2020 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of The American Institute of Chemical Engineers.
Source Title: Bioengineering and Translational Medicine
ISSN: 2380-6761
DOI: 10.1002/btm2.10196
Rights: Attribution 4.0 International
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