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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Abstract

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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  1. SciScore for 10.1101/2020.05.04.20088104: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The optimized drug combinations were ranked according to corresponding %Inhibition from the correlated second-order quadratic series with the %Cytotoxicity of the cell-lines (Vero E6, AC16, and THLE-2) serving as qualitative indicators for consideration.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Cell ATP activity for each well was processed using custom written Python 3.7.7 script (Python Software Foundation).
    Python
    suggested: (IPython, RRID:SCR_001658)
    This second-order quadratic analysis and parabolic response surface plot analysis were conducted using the built-in “stepwiselm” function in Matlab R2020a (Mathworks, Inc.) with custom-written code.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04310228RecruitingFavipiravir Combined With Tocilizumab in the Treatment of Co…
    NCT04312009CompletedLosartan for Patients With COVID-19 Requiring Hospitalizatio…
    NCT04325061TerminatedEfficacy of Dexamethasone Treatment for Patients With ARDS C…
    NCT04362332TerminatedChloroquine, Hydroxychloroquine or Only Supportive Care in P…
    NCT04303299RecruitingFight COVID-19 Trial
    NCT04257656TerminatedA Trial of Remdesivir in Adults With Severe COVID-19


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.