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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Pharmacol.</journal-id>
<journal-title>Frontiers in Pharmacology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Pharmacol.</abbrev-journal-title>
<issn pub-type="epub">1663-9812</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1486422</article-id>
<article-id pub-id-type="doi">10.3389/fphar.2024.1486422</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Pharmacology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Drug-induced coagulopathies: a real-world pharmacovigilance study using the FDA adverse event reporting system</article-title>
<alt-title alt-title-type="left-running-head">Lu et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphar.2024.1486422">10.3389/fphar.2024.1486422</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lu</surname>
<given-names>Yanjun</given-names>
</name>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2825978/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Qian</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhu</surname>
<given-names>Shita</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
</contrib-group>
<aff>
<institution>Pharmacy Department</institution>, <institution>Xiamen Fifth Hospital</institution>, <addr-line>Xiamen</addr-line>, <addr-line>Fujian</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1758076/overview">Linan Zeng</ext-link>, McMaster University, Canada</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/284329/overview">Mamunur Rashid</ext-link>, University of Nebraska Medical Center, United States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/816664/overview">Anoop Kumar</ext-link>, Delhi Pharmaceutical Sciences and Research University, India</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Yanjun Lu, <email>13792830916@163.com</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>18</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1486422</elocation-id>
<history>
<date date-type="received">
<day>26</day>
<month>08</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>12</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Lu, Xu and Zhu.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Lu, Xu and Zhu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>This study aims to investigate adverse drug reaction signals associated with coagulopathies through data mining using the Adverse Event Reporting System (FAERS) of the US Food and Drug Administration. Prompt identification of high-risk drugs provides a valuable basis for enhancing clinical drug safety.</p>
</sec>
<sec>
<title>Methods</title>
<p>The adverse event reports related to coagulopathies from Q1 2004 to Q2 2024 were extracted from the ASCII data packages in FAERS. The reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to identify adverse drug reaction signals associated with coagulopathies.</p>
</sec>
<sec>
<title>Results</title>
<p>During the reporting period, 40,545 reports were retrieved, with a slightly higher proportion of females than males. Among the top 30 drugs associated with the occurrence of coagulopathies, 24 drugs exhibited positive signals in risk analysis. Based on the individual drug reporting odds ratio (95% confidence interval) as a measure of risk signal strength, the top five drugs are as follows: gemcitabine [ROR (95% CI):16.87 (15.83&#x2013;17.98)], busulfan [ROR (95% CI):15.51 (13.69&#x2013;17.58)], anti-thymocyte globulin [ROR (95% CI):15.49 (13.49&#x2013;17.78)], tacrolimus [ROR (95% CI):12.7 (11.57&#x2013;13.95)], etonogestrel and ethinylestradiol vaginal ring [ROR (95% CI):11.88 (10.95&#x2013;12.89)]. After categorizing the drugs, the strongest risk signal is sex hormones and modulators of the genital system [ROR (95% CI):11.88 (10.95&#x2013;12.89)], followed by analgesics [ROR (95%CI): 6.73 (6.38&#x2013;7.1)], immunosuppressants [ROR (95% CI):3.91 (3.76&#x2013;4.05)], antineoplastic agents [ROR (95% CI):3.33 (3.22&#x2013;3.45)], corticosteroids for systemic use [ROR (95% CI): 2.94 (2.73&#x2013;3.18)], antiepileptics [ROR (95% CI):1.93 (1.71&#x2013;2.18)], drugs used in diabetes [ROR (95% CI):1.5 (1.34&#x2013;1.67)], antibacterials for systemic use [ROR (95% CI):1.46 (1.28&#x2013;1.68)].</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Our findings indicate that multiple drugs are associated with an increased risk of coagulopathies. From the pharmacovigilance perspective, proactive analysis of these drugs aids in clinical monitoring and enhances risk identification of coagulopathies.</p>
</sec>
</abstract>
<kwd-group>
<kwd>coagulopathies</kwd>
<kwd>FAERS database</kwd>
<kwd>adverse event</kwd>
<kwd>data mining</kwd>
<kwd>disproportionality analyses</kwd>
<kwd>pharmacovigilance</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Pharmacoepidemiology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>Coagulopathies are diseases characterized by reduced blood clotting capacity, resulting in a pathological tendency toward bleeding and thrombosis (<xref ref-type="bibr" rid="B31">Iba et al., 2019a</xref>; <xref ref-type="bibr" rid="B30">Iba and Levy, 2020</xref>). Coagulopathies arise from various conditions, such as severe trauma, sepsis, cancer, hematological malignancies, and pregnancy-related complications (<xref ref-type="bibr" rid="B44">Levi, 2014</xref>; <xref ref-type="bibr" rid="B48">Lockhart et al., 2016</xref>; <xref ref-type="bibr" rid="B32">Iba et al., 2019b</xref>; <xref ref-type="bibr" rid="B39">Kleinveld et al., 2022</xref>). In addition, drug-induced coagulopathies are also common in clinical practice (<xref ref-type="bibr" rid="B53">Novak et al., 2012</xref>; <xref ref-type="bibr" rid="B11">Cui et al., 2019</xref>; <xref ref-type="bibr" rid="B41">Kumar et al., 2019</xref>). Coagulopathies can stem from platelet dysfunction, impaired thrombin generation, hypofibrinogenemia, and hyperfibrinolysis (<xref ref-type="bibr" rid="B16">Gando et al., 2016</xref>; <xref ref-type="bibr" rid="B5">Bartoszko and Karkouti, 2021</xref>). Coagulopathies are severe complications in patients, which can lead to multiple organ dysfunction and are linked to poorer patient prognosis (<xref ref-type="bibr" rid="B28">Helms et al., 2023</xref>; <xref ref-type="bibr" rid="B46">Li et al., 2023</xref>). Studies have shown that up to 56% of trauma patients and over 40% of critically-ill patients develop coagulopathies (<xref ref-type="bibr" rid="B72">Stensballe et al., 2017</xref>; <xref ref-type="bibr" rid="B57">Petros, 2019</xref>). Severe coagulopathies are associated with a more than fourfold increase in adverse bleeding events, blood transfusion volume, and mortality, along with prolonged hospital and ICU stays. This heightened risk highlights the importance of early identification and prevention of coagulopathies (<xref ref-type="bibr" rid="B89">Zhao et al., 2021</xref>; <xref ref-type="bibr" rid="B46">Li et al., 2023</xref>).</p>
<p>Clinical manifestations of coagulopathies are diverse, primarily including bleeding tendency (such as skin ecchymosis, joint hematoma, and visceral hemorrhage), thrombosis (e.g., deep vein thrombosis and pulmonary embolism). They are usually accompanied by abnormal laboratory results, such as prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen concentration, platelet count, function testing, and special coagulation factor testing (<xref ref-type="bibr" rid="B51">Moore et al., 2020</xref>; <xref ref-type="bibr" rid="B21">Giustozzi et al., 2021</xref>; <xref ref-type="bibr" rid="B22">G&#xf3;mez-Mesa et al., 2021</xref>). Drugs can affect the coagulation system through multiple biomolecules and signal pathways. For example, some drugs inhibit the activity of catalases, while others cause bleeding by interfering with the interaction between platelets and blood vessel walls. These complex mechanisms of action complicate the diagnosis and treatment of drug-induced coagulopathies (<xref ref-type="bibr" rid="B8">Cassar et al., 2005</xref>; <xref ref-type="bibr" rid="B83">Xiao et al., 2013</xref>; <xref ref-type="bibr" rid="B34">Izzedine and Perazella, 2015</xref>; <xref ref-type="bibr" rid="B6">Bauer et al., 2022</xref>). Due to the complex pathogenesis and diagnostic challenges of coagulopathies, which often require multidisciplinary collaboration, research in this area is limited. Identifying drugs closely linked to coagulopathies is essential, as it enables medical institutions to develop precise monitoring and intervention strategies while providing critical safety information for clinicians and pharmacists to support safer drug use for patients.</p>
<p>Currently, information on adverse reactions related to coagulopathies is mainly recorded in drug labels. Although drug safety is evaluated in clinical trials, these clinical trials may not fully capture the real-world scenario due to sample limits, treatment duration, and co-morbid conditions (<xref ref-type="bibr" rid="B36">Javed and Kumar, 2024</xref>). Therefore, conducting real-world research offers a more comprehensive approach to understanding drug safety. Although identifying drugs related to coagulopathies is crucial, no comprehensive list of these drugs currently exists. While most existing research focuses on evaluating the coagulopathy risks of individual drugs, it is equally important to investigate coagulopathies associated with a broader range of medications (<xref ref-type="bibr" rid="B9">Chai and Babu, 2014</xref>; <xref ref-type="bibr" rid="B11">Cui et al., 2019</xref>; <xref ref-type="bibr" rid="B56">Peralta et al., 2019</xref>; <xref ref-type="bibr" rid="B24">Guo et al., 2022</xref>).</p>
<p>Compared to laboratory and clinical trial data, pharmacovigilance data reflects real-world drug use more accurately and is vital for post-market surveillance (<xref ref-type="bibr" rid="B62">Raschi et al., 2020b</xref>). The FAERS is the largest public drug alert database for spontaneous reports of adverse events, gathering data from medical personnel, consumers, manufacturers, <italic>etc.</italic> It plays an essential role in informing healthcare professionals and the public about the potential risks of drugs (<xref ref-type="bibr" rid="B66">Sakaeda et al., 2013</xref>; <xref ref-type="bibr" rid="B63">Raschi et al., 2019</xref>; <xref ref-type="bibr" rid="B61">Raschi et al., 2020a</xref>).</p>
<p>A signal is new or previously unknown information linking an adverse event to a drug (<xref ref-type="bibr" rid="B36">Javed and Kumar, 2024</xref>). Generating a signal requires more than one high-quality report (<xref ref-type="bibr" rid="B73">Subeesh et al., 2017</xref>; <xref ref-type="bibr" rid="B69">Sharma and Kumar, 2022</xref>). Data mining algorithms (DMAs) such as reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) are common analytical methods for detecting signals in pharmacovigilance databases (<xref ref-type="bibr" rid="B40">Kubota et al., 2004</xref>; <xref ref-type="bibr" rid="B64">Rothman et al., 2004</xref>). These methods identify patterns of associations or unexpected occurrences of events in large databases using statistical analysis.</p>
<p>The purpose of this study is to comprehensively investigate the risks of drug-induced coagulopathies through the FAERS and identify drugs with potential coagulopathy risks that are not listed in the package insert. This research aims to provide an overview of drugs that may induce coagulopathies from a pharmacovigilance perspective, offering valuable insights for clinical practice.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Data sources and processing procedures</title>
<p>The FAERS database is the central system for post-marketing adverse drug reaction monitoring in the United States and is also one of the main approaches for current pharmacovigilance research (<xref ref-type="bibr" rid="B88">Zhang et al., 2024</xref>). In our study, ASCII data packages submitted from the first quarter of 2004 to the second quarter of 2024 were retrieved from the database. Each package contains demographic and administrative information (DEMO), drug information (DRUG), adverse events (REAC), patient outcomes (OUTC), report sources (RPSR), start and end dates for reported drugs (THER), indications for use (INDI) (<xref ref-type="bibr" rid="B84">Yang et al., 2022</xref>). Import all data analysis into R version 4.3.3 and Excel software for data cleaning and analysis. Delete duplicate data according to the FDA&#x2019;s suggestion. If the case has the same case ID, it will retain the latest report with FDA_DT; if the case ID is the same as the FDA_DT, it will retain a large master ID. After the repeated data is eliminated, some primary ID repeated items are still found, so the auxiliary duplicate data is performed (<xref ref-type="bibr" rid="B86">Yu et al., 2021</xref>).</p>
<p>The symptoms of AEs are coded using the Medical Dictionary for Regulatory Activities (MedDRA). MedDRA is an internationally standardized and clinically validated terminology system (<xref ref-type="bibr" rid="B41">Kumar et al., 2019</xref>). In the FAERS database, encode each report using the preferred term (PT) from MedDRA terminology, which is categorized into High-Level Term (HLT), High-Level Group Term (HLGT), and System Organ Class (SOC) in MedDRA (<xref ref-type="bibr" rid="B87">Zhang et al., 2023</xref>; <xref ref-type="bibr" rid="B45">Li et al., 2024</xref>). According to the latest MedDRA 27.0 version, our study searched for &#x201c;coagulopathies (MedDRA 10064477)&#x201d; at the HLT level and identified 26 related PTs, mainly including coagulopathy, disseminated intravascular coagulation, thrombotic microangiopathy, hypercoagulation, antiphospholipid, <italic>etc.</italic> In line with MedDRA 27.0 standards, only &#x201c;primary suspect (PS)&#x201d; drugs coded by PT were included to focus on the highest level of suspicion for drug-related coagulopathies. The Anatomical Therapeutic Chemical (ATC) classification system was used to code the preliminary drug list, the final drug list for analysis was obtained after excluding ambiguous drug names and integrating drugs with the same ingredient (<xref ref-type="bibr" rid="B15">Fan et al., 2024</xref>; <xref ref-type="bibr" rid="B45">Li et al., 2024</xref>).</p>
</sec>
<sec id="s2-2">
<title>2.2 Data analysis</title>
<p>In this study, we employed ROR, PRR, and BCPNN to identify signals for potential increased risk of drug-related coagulopathies (<xref ref-type="bibr" rid="B59">Poluzzi et al., 2009</xref>; <xref ref-type="bibr" rid="B66">Sakaeda et al., 2013</xref>; <xref ref-type="bibr" rid="B69">Sharma and Kumar, 2022</xref>; <xref ref-type="bibr" rid="B91">Zou et al., 2023</xref>; <xref ref-type="bibr" rid="B36">Javed and Kumar, 2024</xref>). The ROR and PRR algorithms are frequentist (non-Bayesian) algorithms, which are simple to calculate and have high sensitivity. The advantage of ROR is that it corrects for bias due to the low number of reports of certain events compared to PRR, while the advantage of PRR is that it is less affected by the omission of adverse events (<xref ref-type="bibr" rid="B14">Evans et al., 2001</xref>; <xref ref-type="bibr" rid="B64">Rothman et al., 2004</xref>). BCPNN, a Bayesian algorithm, effectively integrates data from multiple sources and supports cross-validation (<xref ref-type="bibr" rid="B40">Kubota et al., 2004</xref>). It accounts for uncertainties in the disproportionate rate, especially with smaller adverse event samples, reduces false positives, and is used for pattern recognition in higher dimensions (<xref ref-type="bibr" rid="B75">Tang et al., 2022</xref>). This study combines multiple algorithms to leverage their respective strengths, expanding the detection range and cross-validating results to enhance sensitivity and specificity in signal detection (<xref ref-type="bibr" rid="B52">Noguchi et al., 2018</xref>; <xref ref-type="bibr" rid="B90">Zhou et al., 2023</xref>). Higher values of these parameters indicate stronger signal strength, representing the level of association between drugs and coagulopathies. The formulas and criteria for each algorithm are shown in <xref ref-type="table" rid="T1">Table 1</xref> (<xref ref-type="bibr" rid="B70">Sharma et al., 2023</xref>; <xref ref-type="bibr" rid="B47">Liu et al., 2024</xref>). Positive signals are identified if any of the three methods&#x2019; criteria are met, indicating a possible association between the drugs and the event (<xref ref-type="bibr" rid="B2">Alenzi et al., 2024</xref>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Summary of algorithms used for signal detection.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Measure</th>
<th align="left">Calculation formula</th>
<th align="left">Criteria</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="left">ROR</td>
<td align="left">ROR &#x3d; ad/bc</td>
<td rowspan="2" align="left">a&#x2265;3; lower limit of 95% CI &#x3e; 1</td>
</tr>
<tr>
<td align="left">95%CI &#x3d; e<sup>ln(ROR)&#xb1;1.96(1/a&#x2b;1/b&#x2b;1/c&#x2b;1/d)&#x5e;0.5</sup>
</td>
</tr>
<tr>
<td rowspan="2" align="left">PRR</td>
<td align="left">PRR &#x3d; a (c &#x2b; d)/c (a &#x2b; b)</td>
<td rowspan="2" align="left">a&#x2265;3; PRR&#x2265;2, &#x3c7;<sup>2</sup> &#x2265; 4</td>
</tr>
<tr>
<td align="left">&#x3c7;<sup>2</sup> &#x3d; [(ad-bc)<sup>&#x5e;</sup>2](a&#x2b;b &#x2b; c &#x2b; d)/[(a&#x2b;b) (c &#x2b; d) (a&#x2b;c) (b &#x2b; d)]</td>
</tr>
<tr>
<td rowspan="2" align="left">BCPNN</td>
<td align="left">IC &#x3d; log2a (a&#x2b;b &#x2b; c &#x2b; d)/[(a&#x2b;c) (a&#x2b;b)]</td>
<td rowspan="2" align="left">IC025 &#x3e; 0</td>
</tr>
<tr>
<td align="left">95%CI &#x3d; E (IC)&#xb1;2 [V(IC)]&#x2009;&#x5e;&#x2009;0.5</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>PRR, proportional reporting ratio; ROR, reporting odds ratio; BCPNN, bayesian confidence propagation neural network; a, number of reports with coagulation dysfunction caused by the target drug; b, number of reports with other AEs, caused by the target drug; c, number of reports with coagulation dysfunction caused by other drugs; d,number of reports with other AEs, caused by other drugs; CI, confidence interval; IC, information component; IC025, the lower limit of the 95% CI, of the IC; E (IC), the IC, expectations; V (IC), the variance of IC.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Descriptive analysis</title>
<sec id="s3-1-1">
<title>3.1.1 The basic process for retrieving adverse event reports of target drugs</title>
<p>A total of 21, 433, 114 reports were retrieved from the FAERS database. After data cleaning and analysis, 40,545 reports on coagulopathies were collected. We found that 4,687 drugs are related to coagulopathies. After removing anticoagulant and antiplatelet drugs, we conducted a comprehensive analysis of the top 30 drugs, which is detailed in the flowchart (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Flow chart for identifying suspected coagulopathies reports.</p>
</caption>
<graphic xlink:href="fphar-15-1486422-g001.tif"/>
</fig>
</sec>
<sec id="s3-1-2">
<title>3.1.2 Clinical information on adverse event reports</title>
<p>Detailed information on the adverse event reports of the patient was introduced in <xref ref-type="table" rid="T2">Table 2</xref>. As for patients, males reported 17,893 (44.1%) adverse event reports, while females submitted 18,734 (46.2%), with a slightly higher number of reports from females than males. Regarding age composition, patients aged 18&#x2013;64.9 submitted 16,547 (40.8%) adverse event reports, accounting for the most significant proportion. These data come from submissions from many countries. The United States is the most extensive reporting country, and it submitted 14,951 (36.9%) reports, followed by Japan, which submitted 7,310 (18%) adverse event reports. In terms of the ending of the patient, the number of &#x201c;deaths&#x201d; in patients is the largest, with a total of 13,183 (32.5%) adverse event reports, followed by &#x201c;hospitalization or prolongation of hospitalization&#x201d; with 12,511 (30.9%) adverse event reports. In terms of reporting year, 2014 had the highest reported cases (<xref ref-type="fig" rid="F2">Figure 2</xref>). After searching the PTs contained in &#x201c;coagulopathies&#x201d;, a total of 26 related PTs were obtained. Among them, &#x201c;coagulopathy&#x201d; is the most reported PT (<xref ref-type="table" rid="T3">Table 3</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Clinical characteristics of reported drug-induced coagulopathies.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Characteristics</th>
<th align="left">Reports, n (%)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td colspan="2" align="left">Age</td>
</tr>
<tr>
<td align="left">&#x3c;18</td>
<td align="left">3,675 (9.1)</td>
</tr>
<tr>
<td align="left">&#x3e;85</td>
<td align="left">974 (2.4)</td>
</tr>
<tr>
<td align="left">18&#x2013;64.9</td>
<td align="left">16,547 (40.8)</td>
</tr>
<tr>
<td align="left">65&#x2013;85</td>
<td align="left">10,419 (25.7)</td>
</tr>
<tr>
<td align="left">Unknown</td>
<td align="left">8,930 (22.0)</td>
</tr>
<tr>
<td colspan="2" align="left">Gender</td>
</tr>
<tr>
<td align="left">Male</td>
<td align="left">17,893 (44.1%)</td>
</tr>
<tr>
<td align="left">Female</td>
<td align="left">18,734 (46.2%)</td>
</tr>
<tr>
<td align="left">Unknown</td>
<td align="left">3,918 (9.7%)</td>
</tr>
<tr>
<td colspan="2" align="left">Reporting country</td>
</tr>
<tr>
<td align="left">United States</td>
<td align="left">14,951 (36.9)</td>
</tr>
<tr>
<td align="left">Japan</td>
<td align="left">7,310 (18)</td>
</tr>
<tr>
<td align="left">France</td>
<td align="left">1876 (4.6)</td>
</tr>
<tr>
<td align="left">Germany</td>
<td align="left">1,225 (3.0)</td>
</tr>
<tr>
<td align="left">Spain</td>
<td align="left">1,151 (2.8)</td>
</tr>
<tr>
<td align="left">Others or unknown</td>
<td align="left">14,032 (34.6)</td>
</tr>
<tr>
<td colspan="2" align="left">Outcome</td>
</tr>
<tr>
<td align="left">Death</td>
<td align="left">13,183 (32.5)</td>
</tr>
<tr>
<td align="left">Hospitalization or prolongation of hospitalization</td>
<td align="left">12,511 (30.9)</td>
</tr>
<tr>
<td align="left">Life-threatening</td>
<td align="left">4,558 (11.2)</td>
</tr>
<tr>
<td align="left">Disability</td>
<td align="left">180 (0.4)</td>
</tr>
<tr>
<td align="left">Congenital anomaly</td>
<td align="left">28 (0.1)</td>
</tr>
<tr>
<td align="left">Others or unknown</td>
<td align="left">10,085 (24.9)</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Annual trend in reporting of adverse drug events related to coagulopathies.</p>
</caption>
<graphic xlink:href="fphar-15-1486422-g002.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Number of drugs associated with PT group.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Group</th>
<th align="left">Code</th>
<th align="left">No. (%) of drugs</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Coagulopathy</td>
<td align="left">10,009802</td>
<td align="left">15057 (35.84)</td>
</tr>
<tr>
<td align="left">Disseminated Intravascular Coagulation</td>
<td align="left">10,013442</td>
<td align="left">12954 (30.83)</td>
</tr>
<tr>
<td align="left">Thrombotic Microangiopathy</td>
<td align="left">10079988</td>
<td align="left">8,050 (19.16)</td>
</tr>
<tr>
<td align="left">Hypercoagulation</td>
<td align="left">10020608</td>
<td align="left">1771 (4.22)</td>
</tr>
<tr>
<td align="left">Antiphospholipid Syndrome</td>
<td align="left">10002817</td>
<td align="left">1,395 (3.32)</td>
</tr>
<tr>
<td align="left">Factor Viii Inhibition</td>
<td align="left">10048619</td>
<td align="left">1,007 (2.40)</td>
</tr>
<tr>
<td align="left">Hypocoagulable State</td>
<td align="left">10020973</td>
<td align="left">861 (2.05)</td>
</tr>
<tr>
<td align="left">Abnormal Clotting Factor</td>
<td align="left">10049862</td>
<td align="left">198 (0.47)</td>
</tr>
<tr>
<td align="left">Factor V Inhibition</td>
<td align="left">10056335</td>
<td align="left">180 (0.43)</td>
</tr>
<tr>
<td align="left">Factor Ix Inhibition</td>
<td align="left">1,0051778</td>
<td align="left">127 (0.30)</td>
</tr>
<tr>
<td align="left">Heparin Resistance</td>
<td align="left">10059598</td>
<td align="left">93 (0.22)</td>
</tr>
<tr>
<td align="left">Hyperfibrinogenaemia</td>
<td align="left">10051124</td>
<td align="left">61 (0.15)</td>
</tr>
<tr>
<td align="left">Coagulation Disorder Neonatal</td>
<td align="left">10009732</td>
<td align="left">55 (0.13)</td>
</tr>
<tr>
<td align="left">Hyperfibrinolysis</td>
<td align="left">10074737</td>
<td align="left">52 (0.12)</td>
</tr>
<tr>
<td align="left">Activated Protein C Resistance</td>
<td align="left">10067648</td>
<td align="left">33 (0.08)</td>
</tr>
<tr>
<td align="left">Von Willebrand&#x27;S Factor Inhibition</td>
<td align="left">10070690</td>
<td align="left">30 (0.07)</td>
</tr>
<tr>
<td align="left">Factor Xiii Inhibition</td>
<td align="left">10059608</td>
<td align="left">27 (0.06)</td>
</tr>
<tr>
<td align="left">Disseminated Intravascular Coagulation In Newborn</td>
<td align="left">10013443</td>
<td align="left">25 (0.06)</td>
</tr>
<tr>
<td align="left">Factor Vii Inhibition</td>
<td align="left">10075240</td>
<td align="left">16 (0.04)</td>
</tr>
<tr>
<td align="left">Acquired Dysfibrinogenaemia</td>
<td align="left">10051122</td>
<td align="left">7 (0.02)</td>
</tr>
<tr>
<td align="left">Lupus Anticoagulant Hypoprothrombinaemia Syndrome</td>
<td align="left">10085219</td>
<td align="left">4 (0.01)</td>
</tr>
<tr>
<td align="left">Hyperprothrombinaemia</td>
<td align="left">10067920</td>
<td align="left">3 (0.01)</td>
</tr>
<tr>
<td align="left">Hyperthrombinaemia</td>
<td align="left">10058516</td>
<td align="left">3 (0.01)</td>
</tr>
<tr>
<td align="left">Factor Ii Inhibition</td>
<td align="left">10075242</td>
<td align="left">2 (0.00)</td>
</tr>
<tr>
<td align="left">Factor X Inhibition</td>
<td align="left">10075241</td>
<td align="left">1 (0.00)</td>
</tr>
<tr>
<td align="left">Pseudo-Heparin Resistance</td>
<td align="left">10088924</td>
<td align="left">1 (0.00)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s3-2">
<title>3.2 Drugs that increase the risk of coagulopathies</title>
<p>To evaluate the risk signals associated with various drugs that cause coagulopathies, analyze the top 30 drugs with representative reported quantities. These drugs can be divided into the following categories: antineoplastic agents, immunosuppressants, analgesics, corticosteroids for systemic use, sex hormones and modulators of the genital system, antiepileptics, drugs used in diabetes, antibacterials for systemic use. We analyzed the risk signal intensity of individual drugs, and the specific analysis results are shown in <xref ref-type="fig" rid="F3">Figure 3</xref> and <xref ref-type="sec" rid="s11">Supplementary Table S1</xref>. At the same time, we also evaluated the strength of risk signals after classification, and a comprehensive summary of the detailed analysis is provided in <xref ref-type="fig" rid="F4">Figure 4</xref> and <xref ref-type="sec" rid="s11">Supplementary Table S2</xref>. To provide a more comprehensive profile of these top 30 drugs, we compiled a list of drugs containing indications, dose, mode of administration, and adverse effects based on drug labels, which are summarized in <xref ref-type="sec" rid="s11">Supplementary Table S3</xref>.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>ROR for coagulopathies of single drug.</p>
</caption>
<graphic xlink:href="fphar-15-1486422-g003.tif"/>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>ROR for coagulopathies of each group of drugs.</p>
</caption>
<graphic xlink:href="fphar-15-1486422-g004.tif"/>
</fig>
<sec id="s3-2-1">
<title>3.2.1 Single drug risk signal detection</title>
<p>From <xref ref-type="sec" rid="s11">Supplementary Table S1</xref>, it can be seen that 24 drugs exhibit positive signals. The top 5 drugs with positive signals are gemcitabine [ROR (95% CI):16.87 (15.83&#x2013;17.98)], busulfan [ROR (95% CI):15.51 (13.69&#x2013;17.58)], anti-thymocyte globulin [ROR (95% CI):15.49 (13.49&#x2013;17.78)], tacrolimus [ROR (95% CI):12.7 (11.57&#x2013;13.95)], etonogestrel and ethinylestradiol vaginal ring [ROR (95% CI):11.88 (10.95&#x2013;12.89)]. Other positive signal drugs are arranged in order of risk signal strength: eculizumab, ciclosporin, mycophenolate mofetil, paracetamol, prednisolone, cyclophosphamide, bevacizumab, carboplatin, dexamethasone, sunitinib, cytarabine, nivolumab, lamotrigine, pembrolizumab, ibuprofen, capecitabine, metformin, ciprofloxacin, methotrexate. The larger the value of the ROR, the stronger the risk signal, indicating a greater risk of causing coagulopathies. The higher the ROR value, the greater the possibility of adverse events related to the use of specific drugs.</p>
</sec>
<sec id="s3-2-2">
<title>3.2.2 Risk signals after classification of drugs</title>
<p>After the drug classification, the ranking is based on ROR: sex hormones and modulators of the genital system [ROR (95% CI):11.88 (10.95&#x2013;12.89)], analgesics [ROR (95%CI): 6.73 (6.38&#x2013;7.1)], immunosuppressants [ROR (95% CI):3.91 (3.76&#x2013;4.05)], antineoplastic agents [ROR (95% CI):3.33 (3.22&#x2013;3.45)], corticosteroids for systemic use [ROR (95% CI): 2.94 (2.73&#x2013;3.18)], antiepileptics [ROR (95% CI):1.93 (1.71&#x2013;2.18)], drugs used in diabetes [ROR (95% CI):1.5 (1.34&#x2013;1.67)], antibacterials for systemic use [ROR (95% CI):1.46 (1.28&#x2013;1.68)]. The strongest risk signal is sex hormones and modulators of the genital system, followed by analgesics, immunosuppressants, antineoplastic agents, corticosteroids for systemic use, antiepileptics, drugs used in diabetes, antibacterials for systemic use.</p>
</sec>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>This study provides a comprehensive and systematic investigation of adverse events related to drug-induced coagulopathies using the FAERS database. We identified drugs significantly associated with coagulopathies based on case numbers and signal strength. We observed that some drugs do not list coagulopathy-related adverse reactions in their labeling, highlighting the need to further explore drugs closely associated with coagulopathies.</p>
<p>To minimize bias and reduce the occurrence of false positives and false negatives, we used ROR, PRR, and BCPNN methods for analysis. A total of 24 drugs exhibited positive signals in the risk analysis. In addition, standardized naming was used to ensure precise and reliable analysis of our findings. According to their pharmacological effects, the drugs with positive signals can be divided into the following categories: anti-tumor drugs, immunosuppressants, anti-inflammatory and analgesic drugs, hypoglycemic drugs, antiepileptic drugs, steroid hormone drugs, antibacterial drugs, and contraceptive drugs. Higher ROR values indicate a greater risk of coagulopathy-related adverse reactions. Our research provides a basis for clinicians to make informed prescribing decisions and serves as a reminder for healthcare professionals to be vigilant about potential coagulopathies of these drugs in clinical practice.</p>
<p>Anti-tumor drugs accounted for the largest proportion of coagulopathy cases in our study. The incidence of coagulopathies in cancer patients is approximately 6%&#x2013;15%. With cancer increasingly managed as a chronic disease and the use of new drugs on the rise, their incidence rate is expected to rise (<xref ref-type="bibr" rid="B43">Lechner and Obermeier, 2012</xref>; <xref ref-type="bibr" rid="B3">Al-Nouri et al., 2015</xref>). Anti-tumor drug therapies appear to be more common contributors to coagulopathies than cancer itself, with associated conditions including thrombotic microangiopathy (TMA), thrombocytopenia, intravascular thrombosis, and ischemia-induced terminal organ damage (<xref ref-type="bibr" rid="B34">Izzedine and Perazella, 2015</xref>; <xref ref-type="bibr" rid="B38">Jodele et al., 2015</xref>). The chemotherapeutic drugs most frequently associated with coagulopathies are mitomycin-C and gemcitabine (<xref ref-type="bibr" rid="B1">Aklilu and Shirali, 2023</xref>). In our study, gemcitabine demonstrated the highest ROR value and risk intensity. Gemcitabine is a pyrimidine analog that promotes apoptosis in rapidly dividing cells by disrupting DNA synthesis (<xref ref-type="bibr" rid="B55">Pandit and Royzen, 2022</xref>). Numerous studies have reported gemcitabine-induced coagulopathies, and the use of gemcitabine may increase the incidence of cancer patients (<xref ref-type="bibr" rid="B23">Grall et al., 2021</xref>; <xref ref-type="bibr" rid="B33">Ishikawa et al., 2021</xref>; <xref ref-type="bibr" rid="B79">van der Heijden et al., 2023</xref>). While the mechanism of gemcitabine-induced coagulopathies remains unclear, it is hypothesized to involve microvascular endothelial damage and immune complex-mediated endothelial injury (<xref ref-type="bibr" rid="B50">Mini et al., 2006</xref>; <xref ref-type="bibr" rid="B92">Zupancic et al., 2007</xref>). Carboplatin, a second-generation platinum-based anticancer drug, is cell cycle non-specific and exerts anti-tumor effects by interfering with DNA synthesis and replication (<xref ref-type="bibr" rid="B13">Ettinger et al., 2022</xref>; <xref ref-type="bibr" rid="B60">Porter et al., 2023</xref>). It has been reported that carboplatin alone or in combination with other drugs can induce coagulopathies (<xref ref-type="bibr" rid="B29">Iams et al., 2013</xref>; <xref ref-type="bibr" rid="B67">Salhi et al., 2021</xref>). Given that many cancer patients are treated with multiple chemotherapy drugs, identifying the specific causative agent of coagulopathies can be challenging. Drug-induced coagulopathies may disrupt anti-tumor treatment in patients and increase the risk of cancer progression. Our study aims to discover drugs closely related to coagulopathies, which can help clinicians discover adverse drug reactions during treatment, support preventive measures and promote rational drug use in clinical practice.</p>
<p>Immunosuppressants-induced coagulopathies also accounted for a substantial proportion of cases in our study. Coagulopathies are a severe adverse reaction of immunosuppressants, mainly manifested as systemic platelet aggregation, thrombocytopenia, and mechanical damage to red blood cells (<xref ref-type="bibr" rid="B17">George and Nester, 2014</xref>). The mechanism behind immunosuppressant-induced coagulopathies may involve immune-mediated responses as well as dose- or duration-dependent toxicity (<xref ref-type="bibr" rid="B7">Bhavsar et al., 2016</xref>). Endothelial injury triggers the formation of microthrombi and platelet aggregation in the vascular system (<xref ref-type="bibr" rid="B54">Nwaba et al., 2013</xref>). Tacrolimus is a calcineurin inhibitor commonly used as an immunosuppressant post-solid organ transplants. There have been many reports on coagulopathies caused by tacrolimus (<xref ref-type="bibr" rid="B54">Nwaba et al., 2013</xref>; <xref ref-type="bibr" rid="B10">Cortina et al., 2015</xref>; <xref ref-type="bibr" rid="B49">Markan et al., 2021</xref>), and some have been fatal (<xref ref-type="bibr" rid="B20">Gill and Meghrajani, 2022</xref>). There are also some reports of coagulopathies caused by the combination of tacrolimus and ciclosporin; both drugs showed strong signals in our study (<xref ref-type="bibr" rid="B58">Pham et al., 2000</xref>). Recently, there have been reports that vascular endothelial growth factor (VEGF) inhibitors such as bevacizumab can lead to coagulopathies. VEGF stimulates signaling pathways and transcription by activating its receptor VEG-FR2, which is crucial for angiogenesis. Bevacizumab disrupts this pathway, leading to thrombotic microvascular disease (<xref ref-type="bibr" rid="B4">Apte et al., 2019</xref>; <xref ref-type="bibr" rid="B12">Estrada et al., 2019</xref>).</p>
<p>Our study reveals that sex hormones and reproductive system regulators have the highest risk of inducing coagulopathies. Compelling evidence suggests that female hormone replacement therapies and combination oral contraceptives can induce coagulopathies and increase the risk of venous thromboembolism (<xref ref-type="bibr" rid="B80">Watson, 2007</xref>). The formation of coagulopathies may be related to the increased plasma concentrations of procoagulant proteins, reduced anticoagulant proteins, and altered fibrinolysis (<xref ref-type="bibr" rid="B19">Gialeraki et al., 2017</xref>; <xref ref-type="bibr" rid="B76">Teal and Edelman, 2021</xref>). Studies have shown that patients with oral contraceptives may face a significantly increased risk of developing coagulopathies during the perioperative period (<xref ref-type="bibr" rid="B80">Watson, 2007</xref>). Especially for patients with compound oral contraceptives, the probability of occurrence of coagulopathies is 2&#x2013;4 times higher compared to patients who do not use them (<xref ref-type="bibr" rid="B27">Haverinen et al., 2022</xref>). The risk of coagulopathies is dose-dependent on the dose of sex hormones used (<xref ref-type="bibr" rid="B81">Weill et al., 2016</xref>). Excessive use of antipyretic and analgesic drugs can also lead to coagulopathies, characterized by an increased prothrombin time and international normalized ratio (<xref ref-type="bibr" rid="B42">Larson et al., 2005</xref>; <xref ref-type="bibr" rid="B25">Habib et al., 2013</xref>). Some antibiotics can increase the risk of coagulopathies, which has been confirmed in multiple studies, such as cefoperazone sulbactam, tigecycline, and linezolid (<xref ref-type="bibr" rid="B65">Routsi et al., 2015</xref>; <xref ref-type="bibr" rid="B26">Hakeam et al., 2018</xref>; <xref ref-type="bibr" rid="B11">Cui et al., 2019</xref>; <xref ref-type="bibr" rid="B78">Treml et al., 2021</xref>; <xref ref-type="bibr" rid="B82">Wu et al., 2021</xref>). Our study found that ciprofloxacin is associated with coagulopathies, although few studies have been conducted.</p>
<p>In our study, we identified some (OTC) drugs, such as paracetamol and ibuprofen, as potentially associated with coagulopathies, even though they can be purchased without a prescription. Patients should carefully read instructions before using OTC drugs, strictly follow the recommended dosage and frequency, and avoid altering the dosage or administration method (<xref ref-type="bibr" rid="B68">S&#xe1;nchez-S&#xe1;nchez et al., 2021</xref>). If symptoms of coagulopathies appear, such as severe bleeding or uncontrollable bleeding, patients should discontinue use immediately and seek medical attention (<xref ref-type="bibr" rid="B74">Tan et al., 2020</xref>). Pharmacists should assess patients on their health status, allergy history, medication history, and any concurrent treatments that could influence coagulopathy risk when dispensing OTC drugs. Governments, medical institutions, and pharmaceutical manufacturers can collaborate to raise awareness about the adverse reactions associated with OTC drugs and help to enhance patients&#x2019; understanding of these potential risks (<xref ref-type="bibr" rid="B18">Gheorghe et al., 2019</xref>; <xref ref-type="bibr" rid="B85">Yousaf et al., 2020</xref>). Open communication between patients and healthcare providers is crucial to optimize patient care and ensure safety. Documenting the medical history of patient is a critical step in prevention, especially for those with a history of coagulopathies, who should exercise particular caution. For critically ill patients using these drugs, regular blood coagulation testing is particularly necessary (<xref ref-type="bibr" rid="B71">Song et al., 2020</xref>; <xref ref-type="bibr" rid="B15">Fan et al., 2024</xref>). In our study, monitoring cases of coagulopathies associated with any drug in the FAERS database was critical for helping clinicians properly diagnose and manage this condition. Comprehensive information enables healthcare providers to make informed treatment decisions and apply strategies to reduce the risk of drug-induced coagulopathies.</p>
<p>We found that many drugs with an elevated risk of coagulopathies, such as Mycophenolate Mofetil, Carboplatin, Cytarabine, Nivolumab, Metformin, Rituximab, and Infliximab, were not listed with coagulopathy risks in their instructions. Some drugs, such as Paracetamol, Prednisolone, Dexamethasone, and IbuProfen. In the instructions, only their use will affect the efficacy of oral anticoagulant drugs, but coagulopathies are not mentioned in adverse reactions. Although many drugs have been on the market for a long time, awareness of coagulopathy risks is limited, and data on the prevention and management of drug-induced coagulopathies remain sparse. Monitoring drug-induced coagulopathies is crucial, particularly for cancer and critically ill patients, as it may reduce mortality rates. With the advent of new drugs, especially anti-tumor drugs and immunosuppressants, continuous monitoring of drug-induced coagulopathies will better inform clinicians in making optimal treatment decisions.</p>
<p>This study has several limitations. First, the study established only the association between drugs and adverse events, while lacking definitive proof of the causal relationship between drug exposure and the reported event (<xref ref-type="bibr" rid="B77">Tobaiqy et al., 2010</xref>; <xref ref-type="bibr" rid="B35">Jain et al., 2023</xref>). Second, the FAERS data was based on spontaneous and voluntary reports, which may be influenced by recent research findings or media coverage (<xref ref-type="bibr" rid="B15">Fan et al., 2024</xref>; <xref ref-type="bibr" rid="B37">Jiang et al., 2024</xref>). Third, our study did not account for the impact of concomitant drugs and secondary suspect drugs (SS) on adverse reactions. Finally, this study did not assess whether coagulopathy-related adverse reactions are dose-dependent.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>In summary, coagulopathies represent severe adverse reactions, with some cases leading to fatal outcomes, particularly among patients with severe illness and cancer. Despite the serious nature of coagulopathies, the risks have not been sufficiently assessed. In this study, we conducted a comprehensive evaluation of drugs related to coagulopathies using the FAERS database and systematically analyzed the top 30 drugs with strong risk signals. For clinical use, we recommend enhanced drug safety monitoring to reduce the risk of coagulopathies when administering these medications.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s11">Supplementary Material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>YL: Data curation, Formal Analysis, Investigation, Methodology, Writing&#x2013;original draft, Writing&#x2013;review and editing. QX: Data curation, Investigation, Writing&#x2013;original draft. SZ: Data curation, Investigation, Writing&#x2013;original draft.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fphar.2024.1486422/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fphar.2024.1486422/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table2.xlsx" id="SM1" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table3.pdf" id="SM2" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table1.xlsx" id="SM3" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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