Cytotoxicity Evaluation of Chloroquine and Hydroxychloroquine in Multiple Cell Lines and Tissues by Dynamic Imaging System and Physiologically Based Pharmacokinetic Model

  1. Jianling Yang
  2. Zhengyang Guo
  3. Xu Liu
  4. Qi Liu
  5. Meng Wu
  6. Xueting Yao
  7. Yang Liu
  8. Cheng Cui
  9. Haiyan Li
  10. Chunli Song
  11. Dongyang Liu
  12. Lixiang Xue

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Abstract

Chloroquine (CQ) and hydroxychloroquine (HCQ) have been challenged in treating COVID-19 patients and still under debate due to the uncertainty regarding the effectiveness and safety, and there is still lack of the systematic study on the toxicity of these two drugs. To further uncover the toxicity profile of CQ and HCQ in different tissues, we evaluated the cytotoxicity of them in eight cell lines and further adopted the physiologically based pharmacokinetic models to predict the tissue risk, respectively. Retina, myocardium, lung, liver, kidney, vascular endothelium, and intestinal epithelium originated cells were included in the toxicity evaluation of CQ and HCQ, respectively. The proliferation pattern was monitored in 0–72 h by IncuCyte S3. CC50 and the ratio of tissue trough concentrations to CC50 (R TTCC ) were brought into predicted toxicity profiles. Compared to CQ, HCQ was found to be less toxic in six cell types except Hep3B and Vero cells. In addition, R TTCC was significantly higher in CQ treatment group compared to HCQ group, which indicates relative safety of HCQ. To further simulate the situation of the COVID-19 patients who suffered the dyspnea and hypoxemia, we also tested the cytotoxicity upon hypoxia and normoxia (1, 5 vs. 21% O 2 ). It was found that the cytotoxicity of CQ was more sensitive to hypoxia compared with that of HCQ, particularly in liver originated cells. Both CQ and HCQ showed cytotoxicity in time-dependent manner which indicates the necessity of short period administration clinically.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.22.056762: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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

    Results from rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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.

  2. Version published to 10.3389/fphar.2020.574720
  3. ScreenIT

    SciScore for 10.1101/2020.04.22.056762: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Among these 8 cell lines , Hep3B , HEK-293 , IMR-90 , and IEC-6 are more sensitive to CQ .
    Hep3B
    suggested: None
    HCQ inhibited the viability of Vero cells , IMR90 , A549 , H9C2 , HEK293 , Hep3b and ARPE19 cells in a dose-and time-dependent manner .
    H9C2
    suggested: None
          <div style="margin-bottom:8px">
        <div><b>HEK293</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>ARPE19</b></div>
        <div>suggested: BCRJ Cat# 0041, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0145">CVCL_0145</a></div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CC50 of CQ and HCQ Cytotoxicity tests were carried out in 8 types of cell lines respectively , which is IMR-90 , A549 , ARPE-19 , Hep3B , Vero , HEK-293 , H9C2 , and IEC-6 cells and the results are summarized in Table 1 and Figure 3 .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Vero</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>IEC-6</b></div>
        <div>suggested: BCRJ Cat# 0117, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0343">CVCL_0343</a></div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HCQ exhibits weak cytotoxic activity on Vero and ARPE-19 cell lines with CC50 values of 56.19 μM , and 72.87 μM at 72h , respectively .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>ARPE-19</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Considering that the anti-SARS-CoV activity EC50 of HCQ ( EC50 = 0.72 μM ) is lower than that of CQ ( EC50 = 5.47 μM) , and the CC50 of HCQ is lower than that of CQ in most kinds of cell lines ( such as Hep3B , A549 , IMR-90 , HEK-293 and IEC-6 shown in Table 1 ) ( 9) .</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>A549</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>IMR-90</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>HEK-293</b></div>
        <div>suggested: CLS Cat# 300192/p777_HEK293, <a href="https://scicrunch.org/resources/Any/search?q=CVCL_0045">CVCL_0045</a></div>
      </div>
    </td></tr></table>

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

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  4. Version published to 10.1101/2020.04.22.056762v1 on bioRxiv