The IDentif.AI-x pandemic readiness platform: rapid prioritization of optimized COVID-19 combination therapy regimens

• Main Authors: Blasiak, Agata, Truong, Anh T. L., Remus, Alexandria, Hooi, Lissa, Seah, Shirley Gek Kheng, Wang, Peter, Chye, De Hoe, Lim, Angeline Pei Chiew, Ng, Kim Tien, Teo, Swee Teng, Tan, Yee-Joo, Allen, David Michael, Chai, Louis Yi Ann, Chng, Wee Joo, Lin, Raymond T. P., Lye, David C., Wong, John Eu-Li, Tan, Gladys Gek-Yen, Chan, Conrad En Zuo, Chow, Edward Kai-Hua, Ho, Dean
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2023
• Subjects: Science::Medicine; COVID-19; Combination Therapy
• Online Access: https://hdl.handle.net/10356/164337
• Institution: Nanyang Technological University

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.