Please use this identifier to cite or link to this item: https://doi.org/10.1021/acsnano.2c06366
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dc.titleIDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention
dc.contributor.authorBlasiak, Agata
dc.contributor.authorTruong, Anh TL
dc.contributor.authorWang, Peter
dc.contributor.authorHooi, Lissa
dc.contributor.authorChye, De Hoe
dc.contributor.authorTan, Shi-Bei
dc.contributor.authorYou, Kui
dc.contributor.authorRemus, Alexandria
dc.contributor.authorAllen, David Michael
dc.contributor.authorChai, Louis Yi Ann
dc.contributor.authorChan, Conrad EZ
dc.contributor.authorLye, David CB
dc.contributor.authorTan, Gek-Yen G
dc.contributor.authorSeah, Shirley GK
dc.contributor.authorChow, Edward Kai-Hua
dc.contributor.authorHo, Dean
dc.date.accessioned2022-11-30T08:29:16Z
dc.date.available2022-11-30T08:29:16Z
dc.date.issued2022-08-17
dc.identifier.citationBlasiak, Agata, Truong, Anh TL, Wang, Peter, Hooi, Lissa, Chye, De Hoe, Tan, Shi-Bei, You, Kui, Remus, Alexandria, Allen, David Michael, Chai, Louis Yi Ann, Chan, Conrad EZ, Lye, David CB, Tan, Gek-Yen G, Seah, Shirley GK, Chow, Edward Kai-Hua, Ho, Dean (2022-08-17). IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention. ACS NANO 16 (9). ScholarBank@NUS Repository. https://doi.org/10.1021/acsnano.2c06366
dc.identifier.issn1936-0851
dc.identifier.issn1936-086X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/234987
dc.description.abstractNanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.
dc.language.isoen
dc.publisherAMER CHEMICAL SOC
dc.sourceElements
dc.subjectArtificial Intelligence
dc.subjectDrug Discovery
dc.subjectDrug Combinations
dc.subjectCombinatorial Therapy
dc.subjectIDentif.AI
dc.subjectSARS-CoV-2
dc.subjectCOVID-19
dc.typeArticle
dc.date.updated2022-11-28T05:08:59Z
dc.contributor.departmentCANCER SCIENCE INSTITUTE OF SINGAPORE
dc.contributor.departmentDEPT OF BIOMEDICAL ENGINEERING
dc.contributor.departmentDEPT OF MEDICINE
dc.contributor.departmentPHARMACOLOGY
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.doi10.1021/acsnano.2c06366
dc.description.sourcetitleACS NANO
dc.description.volume16
dc.description.issue9
dc.published.statePublished
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