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Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients

Title: Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients
Authors: Stebbing, J
Krishnan, V
Bono, SD
Ottaviani, S
Casalini, G
Richardson, PJ
Monteil, V
Lauschke, VM
Mirazimi, A
Terres, JAR
Nickoloff, BJ
Higgs, RE
Rocha, G
Byers, NL
Schlichting, DE
Cardoso, A
Corbellino, M
Item Type: Working Paper
Abstract: <jats:title>Abstract</jats:title> <jats:p>Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently hypothesized, using artificial intelligence (AI)-algorithms, to be useful for the treatment of COVID-19 infection via a proposed anti-cytokine effects and as an inhibitor of host cell viral propagation<jats:sup>1,2</jats:sup>. We validated the AI-predicted biochemical inhibitory effects of baricitinib on human numb-associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids, which express hAAK1 and hGAK. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID-19 infection. In a case series of patients with bilateral COVID-19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS-CoV-2 viral load, inflammatory markers, and IL-6 levels. This represents an important example of an AI-predicted treatment showing scientific and clinical promise during a global health crisis. Collectively, these data support further evaluation of the AI-derived hypothesis on anti-cytokine and anti-viral activity and supports its assessment in randomized trials in hospitalized COVID-19 patients.</jats:p>
Issue Date: 15-Apr-2020
URI: http://hdl.handle.net/10044/1/78734
DOI: 10.21203/rs.3.rs-23195/v1
Publisher: Research Square
Copyright Statement: © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: National Institute for Health Research
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: NIHR-RP-011-053
RDB04 79560
Publication Status: Published
Appears in Collections:Department of Surgery and Cancer
Department of Surgery and Cancer
Imperial College London COVID-19