IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development.
IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development.
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DOI：
10.1002/btm2.10196
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发表时间：
2021-01
影响因子：
7.4
通讯作者：
Ho D
中科院分区：
文献类型：
--
作者：
Blasiak A;Lim JJ;Seah SGK;Kee T;Remus A;Chye H;Wong PS;Hooi L;Truong ATL;Le N;Chan CEZ;Desai R;Ding X;Hanson BJ;Chow EK;Ho D
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的出现导致了多项药物再利用临床试验，这些试验产生了很大程度上不确定的结果。为了克服这一挑战，我们使用了IDentif.AI，这是一个将实验验证与人工智能（AI）和数字药物开发相结合的平台，可以快速确定不可预测的药物相互作用，并使用临床相关剂量优化传染病联合治疗设计。IDentif.AI与12种候选药物治疗组配对，代表了超过530，000种针对从患者样本中收集的SARS-CoV-2活病毒的药物组合。IDentif.AI确定了最佳组合为remdesivir，ritonavir和lopinavir，实验验证其介导的疗效是remdesivir单独治疗的6.5倍。此外，它表明羟氯喹和阿奇霉素是相对无效的。该研究在2周内完成，所需的测试数量减少了三个数量级。IDentif.AI独立反映了迄今为止的临床试验结果，而没有来自这些试验的任何数据。这种数字药物开发方法的稳健性与体外实验和AI驱动的优化相结合，表明IDentif.AI可能对当前和未来的疫情具有临床可操作性。
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.
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DOI：
10.1056/nejmoa2022926
发表时间：
2020-11-19
期刊：
The New England journal of medicine
影响因子：
--
作者：
RECOVERY Collaborative Group;Horby P;Mafham M;Linsell L;Bell JL;Staplin N;Emberson JR;Wiselka M;Ustianowski A;Elmahi E;Prudon B;Whitehouse T;Felton T;Williams J;Faccenda J;Underwood J;Baillie JK;Chappell LC;Faust SN;Jaki T;Jeffery K;Lim WS;Montgomery A;Rowan K;Tarning J;Watson JA;White NJ;Juszczak E;Haynes R;Landray MJ
通讯作者：
Landray MJ
影响因子：
--
作者：
Al-Shyoukh I;Yu F;Feng J;Yan K;Dubinett S;Ho CM;Shamma JS;Sun R
通讯作者：
Sun R
影响因子：
158.5
作者：
Grein, J.;Ohmagari, N.;Flanigan, T.
通讯作者：
Flanigan, T.
影响因子：
158.5
作者：
Ackermann, Maximilian;Verleden, Stijn E.;Jonigk, Danny
通讯作者：
Jonigk, Danny
影响因子：
7.6
作者：
Breilh, D.;Foucher, J.;De Ledinghen, V.
通讯作者：
De Ledinghen, V.