| Autor: |
Edward Kai-Hua Chow, Theodore Kee, De Hoe Chye, Lissa Hooi, Dean Ho, Nguyen Le, Shirley Gek Kheng Seah, Xianting Ding, Rishi J. Desai, Pui San Wong, Alexandria Remus, Brendon J. Hanson, Jhin Jieh Lim, Agata Blasiak, Anh T. L. Truong, Conrad E.Z. Chan |
| Rok vydání: |
2020 |
| Předmět: |
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| Popis: |
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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