IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention

被引：11

作者：

Blasiak, Agata ^{[1
,2
,3
]}

Truong, Anh T. L. ^{[1
,2
,3
]}

Wang, Peter ^{[1
,2
,3
]}

Hooi, Lissa ^{[4
]}

Chye, De Hoe ^{[5
]}

Tan, Shi-Bei ^{[1
,2
,3
]}

You, Kui ^{[1
,2
,3
]}

Remus, Alexandria ^{[1
,2
,3
]}

Allen, David Michael ^{[6
,7
,8
]}

Chai, Louis Yi Ann ^{[7
,8
]}

Chan, Conrad E. Z. ^{[5
,9
]}

Lye, David C. B. ^{[7
,9
,10
,11
]}

Tan, Gek-Yen G. ^{[5
]}

Seah, Shirley G. K. ^{[5
]}

Chow, Edward Kai-Hua ^{[1
,2
,3
,4
]}

Ho, Dean ^{[1
,2
,3
]}

机构：

[1] Natl Univ Singapore, N1 Inst Hlth N1, Singapore 117456, Singapore

[2] Natl Univ Singapore, Inst Digital Med WisDM, Singapore 117456, Singapore

[3] Natl Univ Singapore, Coll Design & Engn, Dept Biomed Engn, Singapore 117583, Singapore

[4] Natl Univ Singapore, Canc Sci Inst Singapore, Singapore 117599, Singapore

[5] Def Med & Environm Res Inst, DSO Natl Labs, Singapore 117510, Singapore

[6] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Microbiol & Immunol, Infect Dis Translat Res Program, Singapore 117545, Singapore

[7] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Med, Singapore 119228, Singapore

[8] Natl Univ Singapore Hosp, Dept Med, Div Infect Dis, Singapore 119074, Singapore

[9] Natl Ctr Infect Dis NCID, Jalan Tan Tock Seng, Singapore 308442, Singapore

[10] Nanyang Technol Univ, Lee Kong Chian Sch Med, Dept Pharmacol, Singapore 308232, Singapore

[11] Tan Tock Seng Hosp, Dept Infect Dis, Singapore 308433, Singapore

来源：

ACS NANO | 2022年 / 16卷 / 09期

基金：

新加坡国家研究基金会; 英国医学研究理事会;

关键词：

Artificial Intelligence; Drug Discovery; Drug Combinations; Combinatorial Therapy; IDentif.AI; SARS-CoV-2; COVID-19; DRUG; NANOMEDICINE; MODEL;

D O I：

10.1021/acsnano.2c06366

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Nanomedicine-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.

引用

页码：15141 / 15154

页数：14

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