In Bioengineering & translational medicine
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.
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
COVID‐19, SARS‐CoV‐2, artificial intelligence, combinatory treatment, digital medicine, drug development, drug interactions