<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3-mathml3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.3" xml:lang="EN">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2025.1665425</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Hepatotoxicity associated with anti-neoplastic agents: a pharmacovigilance analysis of the Food and Drug Administration Adverse Event Reporting System database</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Shi</surname><given-names>Lei</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1912329/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Wang</surname><given-names>Yan</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Qu</surname><given-names>Ying</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Chen</surname><given-names>Rong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Funding acquisition" vocab-term-identifier="https://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="software" vocab-term-identifier="https://credit.niso.org/contributor-roles/software/">Software</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="supervision" vocab-term-identifier="https://credit.niso.org/contributor-roles/supervision/">Supervision</role>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label><institution>Department of Pharmacy, The Third Affiliated Hospital of Soochow University/The First People&#x2019;s Hospital of Changzhou</institution>, <city>Changzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Department of Pharmacy, Kangda College of Nanjing Medical University</institution>, <city>Lianyungang</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Rong Chen, <email xlink:href="mailto:pharmacistchencz01@gmail.com">pharmacistchencz01@gmail.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-12-19">
<day>19</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1665425</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>03</day>
<month>12</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Shi, Wang, Qu and Chen.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Shi, Wang, Qu and Chen</copyright-holder>
<license>
<ali:license_ref start_date="2025-12-19">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Hepatotoxicity is commonly observed in patients undergoing chemotherapy. However, the clinical features and outcomes of hepatotoxicity associated with anti-neoplastic agents remain unclear. In this study, we investigated the characteristics and risk factors of hepatotoxicity associatedwith anti-neoplastic agents.</p>
</sec>
<sec>
<title>Methods</title>
<p>We conducted a retrospective pharmacovigilance analysis using data acquired from the FDA Adverse Event Reporting System (FAERS) database (Q1&#x2013;2004 to Q3 2024). Hepatotoxicity risk was assessed by disproportionality analysis, while LASSO and multivariate logistic regression were applied to control for potential confounders. Finally, we analyzed the time duration to the onset of hepatotoxicity.</p>
</sec>
<sec>
<title>Results</title>
<p>A total of 56 anti-neoplastic agents exhibited positive signals for hepatotoxicity, involving 4,195 reports. Female patients (46.50%) were more frequently affected than males (26.70%), with a median age of 56 years. 627 patients (14.95%) experienced fatal or life-threatening outcomes. The top three drugs with the highest Reporting Odds Ratio (ROR) values were mercaptopurine (ROR = 26.57), pegaspargase (ROR = 13.67) and blinatumomab (ROR = 11.93). Most events occurred within the first month (44.18%) and the median TTO value in the Fatal group (22.5 days) was shorter than that in the non-fatal group (42 days) (p &lt; 0.05). Furthermore, Weibull shape parameter (WSP) analysis indicated that 20 of the top 30 drugs were random failure models.</p>
</sec>
<sec>
<title>Discussion</title>
<p>This analysis profiles hepatotoxicity signals for anti-neoplastic agents but reveals a major methodological gap: without using a validated causality tool like the updated RUCAM, FAERS data cannot confirm druginduced liver injury (DILI). Future studies should integrate RUCAM to improvespecificity and clinical relevance.</p>
</sec>
</abstract>
<abstract abstract-type="graphical">
<title>Graphical Abstract</title>
<p>
<fig>
<graphic xlink:href="fimmu-16-1665425-g000.tif" position="anchor">
<alt-text content-type="machine-generated">Pharmacovigilance analysis of hepatotoxicity associated with anti-neoplastic agents using the FAERS database from 2004 Q1 to 2025 Q1. Methods include descriptive analysis, disproportionality analysis, time to onset, and multivariate logistic regression. Core findings highlight 11 of 56 agents lacking warnings, fatal outcomes in 14.95% of patients, and most adverse events occurring within the first treatment month. Top ROR drugs are mercaptopurine, pegaspargase, and blinatumomab. Recommendations suggest monitoring liver function during chemotherapy.</alt-text>
</graphic>
</fig>
</p>
</abstract>
<kwd-group>
<kwd>anti-neoplastic agents</kwd>
<kwd>disproportionality analysis</kwd>
<kwd>FAERS</kwd>
<kwd>hepatotoxicity</kwd>
<kwd>pharmacovigilance</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This research was funded by The Clinical Comprehensive Drugs Evaluation in Jiangsu Province and Changzhou Science and Technology Program (grant number CM20223005).</funding-statement>
</funding-group>
<counts>
<fig-count count="7"/>
<table-count count="5"/>
<equation-count count="0"/>
<ref-count count="38"/>
<page-count count="14"/>
<word-count count="6069"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cancer Immunity and Immunotherapy</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>According to 2019 estimates from the World Health Organization (WHO), cancer was the first or second leading cause of death prior to the age of 70 years in 112 out of 183 countries (<xref ref-type="bibr" rid="B1">1</xref>). Currently, chemotherapy remains one of the cornerstone modalities for the treatment of malignant tumors, playing a vital role in inhibiting tumor progression and prolonging patient survival (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>). The liver, as the central organ for drug metabolism in humans, plays a critical role in the metabolic processing of anti-neoplastic agents. However, this physiological function inevitably induces hepatotoxicity during the administration of anti-neoplastic agents (<xref ref-type="bibr" rid="B4">4</xref>); this is commonly known as drug-induced liver injury (DILI) (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>). DILI not only diminishes therapeutic efficacy but may also progress to acute liver failure, posing severe threats to patient survival and quality-of-life (<xref ref-type="bibr" rid="B7">7</xref>).</p>
<p>From a pathophysiological perspective, DILI is conventionally categorized into three phenotypic patterns based on the relative elevations of specific serum biomarkers. This classification is typically determined using the R value, a diagnostic ratio calculated as R = (Alanine Aminotransferase [ALT]/Upper Limit of Normal [ULN])/(Alkaline Phosphatase [ALP]/ULN). The R value defines the specific phenotypic pattern, as follows: [I] hepatocellular injury, characterized by the predominant elevation of ALT and defined by an R value &#x2265; 5; [II] cholestatic injury, characterized by the predominant elevation of ALP, often accompanied by levels of elevated &#x3b3;-glutamyl transferase (GGT), and defined by an R value &#x2264; 2; and [III] mixed liver injury, which exhibits features of both I and II and is defined by an R value between 2 and 5 (<xref ref-type="bibr" rid="B8">8</xref>). The current laboratory diagnosis of DILI primarily relies on the dynamic monitoring of serum biochemical markers, including ALT, aspartate aminotransferase (AST), GGT, ALP, and total bilirubin (TBIL) levels (<xref ref-type="bibr" rid="B9">9</xref>). However, because such biomarkers are nonspecific, the definitive diagnosis of DILI still relies on the Roussel Uclaf Causality Assessment Method (RUCAM) as the gold standard (<xref ref-type="bibr" rid="B10">10</xref>, <xref ref-type="bibr" rid="B11">11</xref>). Given the severity of hepatotoxicity associated with chemotherapy, establishing systematic protocols to evaluate liver function and identifying the potential hepatotoxic risks of anti-neoplastic agents have become crucial clinical imperatives. Consequently, the comprehensive analysis of hepatotoxicity associated with anti-neoplastic agents is of significant importance for optimizing the therapeutic management of oncology.</p>
<p>With the increasing utilization of anti-neoplastic agents, research relating to the hepatotoxic effects of these agents has garnered substantial research attention. For instance, Lai et&#xa0;al. (<xref ref-type="bibr" rid="B12">12</xref>) conducted a retrospective study investigating the clinical characteristics, causative agents and outcomes of DILI in Chinese pediatric populations. These authors found that adolescents exhibited a higher prevalence of moderate-to-severe DILI and greater risks of critical hepatic impairment than younger children; the primary causative agents were anti-neoplastic agents (25.9%), antibiotics (21.5%), and traditional Chinese medicines (13.7%). Another retrospective analysis of hepatotoxicity-associated adverse drug reactions in China, carried out between 2012 and 2016, demonstrated a consistent upwards trend in reported cases, particularly among males and elderly populations, with the group featuring patients over 80 years-of-age exhibiting a significantly higher incidence of DILI than the general population (<xref ref-type="bibr" rid="B13">13</xref>). Nevertheless, previous studies predominantly focused on single dimensions with limited sample sizes and failed to comprehensively elucidate the association between anti-neoplastic agents and hepatotoxicity. Therefore, multi-dimensional evaluation of this relationship through large-scale databases carries paramount scientific significance.</p>
<p>The U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), a publicly accessible database featuring adverse event reports and medication error submissions, has been extensively utilized for pharmacovigilance research (<xref ref-type="bibr" rid="B14">14</xref>). In this study, we employed FAERS-based pharmacovigilance signal detection techniques to systematically evaluate anti-neoplastic agents-related to hepatotoxicity, providing critical evidence-based medical insights with substantial clinical applicability.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Data sources</title>
<p>This study mined and analyzed data from the FAERS database (<ext-link ext-link-type="uri" xlink:href="https://www.fda.gov/drugs/drug-approvals-and-databases/fda-adverse-event-reporting-system-faers-database">https://www.fda.gov/drugs/drug-approvals-and-databases/fda-adverse-event-reporting-system-faers-database</ext-link>) spanning the first quarter of 2004 to the third quarter of 2024 for mining and analysis. The American Standard Code for Information Interchange (ASCII) data files derived from the FAERS database feature seven subsets: Patient Demographic and Administrative Information (DEMO), Drug Administration Information (DRUG), Report Source Documentation (RPRS), Adverse Event Coding (REAC), Drug Therapy Records (THER), Outcome Data (OUTC) and Drug Indication Records (INDI).</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Data processing</title>
<p>Duplicate reports were removed according to FDA de-duplication guidelines, retaining the latest version for cases with identical medical record numbers. Associations between files were established using the PRIMARY ID identifier. Primary Suspect (PS) drugs were identified by searching for the Preferred Term (PT) &#x201c;hepatotoxicity&#x201d; standardized via the Medical Dictionary for Regulatory Activities (MedDRA). Drug names were further standardized using the Anatomical Therapeutic Chemical (ATC) system (<ext-link ext-link-type="uri" xlink:href="https://atcddd.fhi.no/atc_ddd_index/">https://atcddd.fhi.no/atc_ddd_index/</ext-link>). The workflow for extracting and cleansing adverse events associated with hepatotoxicity and anti-neoplastic agents is illustrated in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flow diagram depicting data extraction and cleaning.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g001.tif">
<alt-text content-type="machine-generated">Flowchart detailing data analysis for hepatotoxicity. It begins with datasets: DRUG (n=66,422,789), DEMO (n=21,838,627), and REAC (n=53,463,446). After removing duplicates from DEMO (n=18,289,374), adverse events related to hepatotoxicity (n=18,995) are identified. Focus narrows to top 56 antineoplastic agent-related events (n=4,195). Analyzed using Reporting Odds Ratio, Proportional Reporting Ratio, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker methods with associated analyses.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Regression analysis</title>
<p>Univariate analysis was performed on suspected drugs with the inclusion criteria of a 95% confidence interval (CI) lower limit for the reporting odds ratio (ROR) &gt;1, event count &gt;100, and adjusted p-value &lt;0.01. Drugs achieving statistical significance (p &lt; 0.01) in univariate analysis were further subjected to least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed using drug variables identified by LASSO combined with baseline patient characteristics as independent variables to identify specific risk factors associated with hepatoxicity and anti-neoplastic agents.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Time to onset analysis</title>
<p>Time-to-onset (TTO) was defined as the interval between the drug initiation date (START_DT in the THER file) and the adverse event occurrence date (EVENT_DT in the DEMO file). Furthermore, inaccurate, missing, or erroneously entered dates were excluded from the analysis. In addition, TTO data were analyzed using the median, interquartile range (IQR), and Weibull shape parameter (WSP). The Weibull distribution is characterized by two parameters: a scale parameter (&#x3b1;) and a shape parameter (&#x3b2;). Three distinct failure patterns were identified based on &#x3b2; values and their 95% CIs: the early failure type: associated with a decreasing adverse drug event (ADE) risk over time, defined as &#x3b2; &lt; 1 (95% CI &lt; 1), the random failure type, representing a constant ADE risk over time, defined as &#x3b2; approaching 1 (95% CI encompassing 1), and the wear-out failure type, indicating an increasing ADE risk over time and defined as &#x3b2; &gt; 1 (95% CI &gt; 1).</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Statistical analysis</title>
<p>Adverse event signals were analyzed using four pharmacovigilance algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS). The ROR method served as the primary statistical measure, with higher values indicating stronger associations between anti-neoplastic agents and the risk of hepatotoxicity. Detailed computational formulas for these methods are provided in <xref ref-type="table" rid="T1"><bold>Tables&#xa0;1</bold></xref> and <xref ref-type="table" rid="T2"><bold>2</bold></xref>.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Two-by-two contingency table for disproportionality analyses.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Type of drug</th>
<th valign="middle" align="center"><italic>N</italic> of target adverse events</th>
<th valign="middle" align="center"><italic>N</italic> of other adverse events</th>
<th valign="middle" align="center">Total</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Target drug</td>
<td valign="middle" align="center"><italic>a</italic></td>
<td valign="middle" align="center"><italic>b</italic></td>
<td valign="middle" align="center"><italic>a+b</italic></td>
</tr>
<tr>
<td valign="middle" align="center">All other drugs</td>
<td valign="middle" align="center"><italic>c</italic></td>
<td valign="middle" align="center"><italic>d</italic></td>
<td valign="middle" align="center"><italic>c+d</italic></td>
</tr>
<tr>
<td valign="middle" align="center">Total</td>
<td valign="middle" align="center"><italic>a+c</italic></td>
<td valign="middle" align="center"><italic>b+d</italic></td>
<td valign="middle" align="center"><italic>a+b+c+d</italic></td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Overview of the main algorithms used for signal detection.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Algorithm</th>
<th valign="middle" align="center">Publicity</th>
<th valign="middle" align="center">Standard for generating signals</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">ROR</td>
<td valign="middle" align="center"><inline-formula>
<mml:math display="inline" id="im1"><mml:mrow><mml:mi>R</mml:mi><mml:mi>O</mml:mi><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>a</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>d</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="center">Lower 95% CI&gt;1, <italic>N</italic>&#x2265;3</td>
</tr>
<tr>
<td valign="middle" rowspan="2" align="center">PRR</td>
<td valign="middle" align="center"><inline-formula>
<mml:math display="inline" id="im2"><mml:mrow><mml:mi>P</mml:mi><mml:mi>R</mml:mi><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>a</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>c</mml:mi><mml:mo>+</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" rowspan="2" align="center">&#x3c7;<sup>2</sup>&#x2265;4, PRR&#x2265;2, <italic>N</italic>&#x2265;3</td>
</tr>
<tr>
<td valign="middle" align="center"><inline-formula>
<mml:math display="inline" id="im3"><mml:mrow><mml:msup><mml:mi>x</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mo>&#x2211;</mml:mo><mml:mo stretchy="false">[</mml:mo><mml:msup><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>O</mml:mi><mml:mo>&#x2212;</mml:mo><mml:mi>E</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy="false">/</mml:mo><mml:mi>E</mml:mi><mml:mo stretchy="false">]</mml:mo><mml:mo>;</mml:mo><mml:mi>O</mml:mi><mml:mo>=</mml:mo><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">/</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo>+</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math></inline-formula></td>
</tr>
<tr>
<td valign="middle" align="center">MGPS</td>
<td valign="middle" align="center"><inline-formula>
<mml:math display="inline" id="im4"><mml:mrow><mml:mi>E</mml:mi><mml:mi>B</mml:mi><mml:mi>G</mml:mi><mml:mi>M</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>a</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo>+</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="center">Lower 95% CI&gt;2</td>
</tr>
<tr>
<td valign="middle" align="center">BCPNN</td>
<td valign="middle" align="center"><inline-formula>
<mml:math display="inline" id="im5"><mml:mrow><mml:mi>I</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mtext>l</mml:mtext><mml:mi>o</mml:mi><mml:msub><mml:mi>g</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mfrac><mml:mrow><mml:mi>a</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo>+</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo>+</mml:mo><mml:mi>c</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></inline-formula></td>
<td valign="middle" align="center">Lower 95% CI&gt;0, <italic>N</italic>&gt;0</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Descriptive analysis</title>
<p>Following data mining, 56 anti-neoplastic agents were found to be positively correlated with hepatotoxicity, as evidenced by 4,195 reports (<xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>). Most hepatotoxic adverse events (AEs) related to anti-neoplastic agents originated from the United States (18.30%) and Canada (15.30%). After excluding patients with missing age data (n = 1,716, 40.90%), the median age was 56 years, with the largest proportion of participants (36.00%) concentrated in the 18&#x2013;64.9 years age group. Body weight was mostly concentrated in the 50&#x2013;100 kg range (n=558, 13.3%). Following the exclusion of cases with undocumented gender (n = 1,125, 26.80%), female patients (n = 1,949, 46.50%) predominated over males (n = 1,121, 26.70%). A total of 627 patients (14.95%) experienced fatal or life-threatening outcomes. As illustrated in <xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>, the annual number of reported hepatotoxic AEs associated with anti-neoplastic agents exhibited an overall upwards trend from 2004, peaking in 2021 with 757 reported cases.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Clinical characteristics of reports with hepatotoxicity.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Characteristics</th>
<th valign="middle" align="center">Case number (n)</th>
<th valign="middle" align="center">Case proportion (%)</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="3" align="left" style="background-color:#e7e6e6">Gender</th>
</tr>
<tr>
<td valign="middle" align="center">Female</td>
<td valign="middle" align="center">1949</td>
<td valign="middle" align="center">46.46%</td>
</tr>
<tr>
<td valign="middle" align="center">Male</td>
<td valign="middle" align="center">1121</td>
<td valign="middle" align="center">26.72%</td>
</tr>
<tr>
<td valign="middle" align="center">Missing</td>
<td valign="middle" align="center">1125</td>
<td valign="middle" align="center">26.82%</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="left" style="background-color:#e7e6e6">Age</th>
</tr>
<tr>
<td valign="middle" align="center">&lt;18</td>
<td valign="middle" align="center">237</td>
<td valign="middle" align="center">5.65%</td>
</tr>
<tr>
<td valign="middle" align="center">&gt;85</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">0.50%</td>
</tr>
<tr>
<td valign="middle" align="center">18&#x223c;64.9</td>
<td valign="middle" align="center">1512</td>
<td valign="middle" align="center">36.04%</td>
</tr>
<tr>
<td valign="middle" align="center">65&#x223c;85</td>
<td valign="middle" align="center">709</td>
<td valign="middle" align="center">16.90%</td>
</tr>
<tr>
<td valign="middle" align="center">Missing</td>
<td valign="middle" align="center">1716</td>
<td valign="middle" align="center">40.91%</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="left" style="background-color:#e7e6e6">Weight</th>
</tr>
<tr>
<td valign="middle" align="center">&lt;50 kg</td>
<td valign="middle" align="center">97</td>
<td valign="middle" align="center">2.31%</td>
</tr>
<tr>
<td valign="middle" align="center">&gt;100 kg</td>
<td valign="middle" align="center">64</td>
<td valign="middle" align="center">1.53%</td>
</tr>
<tr>
<td valign="middle" align="center">50&#x223c;100 kg</td>
<td valign="middle" align="center">558</td>
<td valign="middle" align="center">13.30%</td>
</tr>
<tr>
<td valign="middle" align="center">Missing</td>
<td valign="middle" align="center">3476</td>
<td valign="middle" align="center">82.86%</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="left" style="background-color:#e7e6e6">Reporting region</th>
</tr>
<tr>
<td valign="middle" align="center">United States</td>
<td valign="middle" align="center">766</td>
<td valign="middle" align="center">18.26%</td>
</tr>
<tr>
<td valign="middle" align="center">Canada</td>
<td valign="middle" align="center">568</td>
<td valign="middle" align="center">13.54%</td>
</tr>
<tr>
<td valign="middle" align="center">Italy</td>
<td valign="middle" align="center">348</td>
<td valign="middle" align="center">8.30%</td>
</tr>
<tr>
<td valign="middle" align="center">Spain</td>
<td valign="middle" align="center">316</td>
<td valign="middle" align="center">7.53%</td>
</tr>
<tr>
<td valign="middle" align="center">Germany</td>
<td valign="middle" align="center">252</td>
<td valign="middle" align="center">6.01%</td>
</tr>
<tr>
<th valign="middle" colspan="3" align="left" style="background-color:#e7e6e6">Outcomes</th>
</tr>
<tr>
<td valign="middle" align="center">Death</td>
<td valign="middle" align="center">439</td>
<td valign="middle" align="center">10.46%</td>
</tr>
<tr>
<td valign="middle" align="center">Disability</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">0.50%</td>
</tr>
<tr>
<td valign="middle" align="center">Hospitalization</td>
<td valign="middle" align="center">725</td>
<td valign="middle" align="center">17.28%</td>
</tr>
<tr>
<td valign="middle" align="center">Life-Threatening</td>
<td valign="middle" align="center">188</td>
<td valign="middle" align="center">4.48%</td>
</tr>
<tr>
<td valign="middle" align="center">Other and Unknown</td>
<td valign="middle" align="center">2822</td>
<td valign="middle" align="center">67.27%</td>
</tr>
<tr>
<td valign="middle" align="center">Totality</td>
<td valign="middle" align="center">4195</td>
<td valign="middle" align="center">100.00%</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Number of annual reports.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g002.tif">
<alt-text content-type="machine-generated">Line graph showing report cases from 2004 to 2024. Cases start at 6 in 2004, gradually increasing to 757 in 2020, then slightly decreasing to 426 by 2024.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Disproportionality analysis</title>
<p>Following data cleaning and analysis, a total of 56 anti-neoplastic agents were identified with positive hepatotoxicity signals. The top 30 anti-neoplastic agents ranked by ROR values are presented in <xref ref-type="table" rid="T4"><bold>Table&#xa0;4</bold></xref>. The five highest-ranking agents were: mercaptopurine (ROR&#xa0;=&#xa0;26.57, 95% CI: 20.84&#x2013;33.89), pegaspargase (ROR&#xa0;=&#xa0;13.67, 95% CI: 10.59&#x2013;17.64), blinatumomab (ROR&#xa0;=&#xa0;11.93, 95% CI: 9.63&#x2013;14.78), letrozole (ROR&#xa0;=&#xa0;11.01, 95% CI: 9.63&#x2013;12.58) and dasatinib (ROR&#xa0;=&#xa0;10.25, 95% CI: 9.14&#x2013;11.50). Subsequent classification of the top 30 anti-neoplastic agents revealed that small molecule kinase inhibitors accounted for the highest number of hepatotoxicity cases and enzymes had the highest ROR values (<xref ref-type="fig" rid="f3"><bold>Figure 3</bold></xref>). Furthermore, among these 56 anti-neoplastic agents, the package inserts of 45 drugs contain warnings regarding hepatotoxicity risks, while the remaining 11 drug&#x2019;s package inserts do not include hepatotoxicity risk alerts. These eleven drugs without hepatotoxicity warnings are: letrozole, doxorubicin, carboplatin, trastuzumab, blinatumomab, cytarabine, dabrafenib, pemetrexed, melphalan, vismodegib and midostaurin.</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The top 30 anti-neoplastic agents associated with ADE of hepatotoxicity.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Ranking</th>
<th valign="middle" align="center">ATC code</th>
<th valign="middle" align="center">Drug</th>
<th valign="middle" align="center">Cases</th>
<th valign="middle" align="center">ROR  (95% CI)</th>
<th valign="middle" align="center">PRR  (&#x3c7;;<sup>2</sup>)</th>
<th valign="middle" align="center">EBGM  (EBGM05)</th>
<th valign="middle" align="center">IC  (IC025)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">L01BB02</td>
<td valign="middle" align="center">Mercaptopurine</td>
<td valign="middle" align="center">67</td>
<td valign="middle" align="center">26.57 (20.84-33.89)</td>
<td valign="middle" align="center">25.89 (1599.01)</td>
<td valign="middle" align="center">25.8 (21.05)</td>
<td valign="middle" align="center">4.69 (4.33)</td>
</tr>
<tr>
<td valign="middle" align="center">2</td>
<td valign="middle" align="center">L01XX24</td>
<td valign="middle" align="center">Pegaspargase</td>
<td valign="middle" align="center">60</td>
<td valign="middle" align="center">13.67 (10.59-17.64)</td>
<td valign="middle" align="center">13.49 (692.46)</td>
<td valign="middle" align="center">13.45 (10.87)</td>
<td valign="middle" align="center">3.75 (3.38)</td>
</tr>
<tr>
<td valign="middle" align="center">3</td>
<td valign="middle" align="center">L01FX07</td>
<td valign="middle" align="center">Blinatumomab</td>
<td valign="middle" align="center">85</td>
<td valign="middle" align="center">11.93 (9.63-14.78)</td>
<td valign="middle" align="center">11.8 (837.18)</td>
<td valign="middle" align="center">11.75 (9.82)</td>
<td valign="middle" align="center">3.55 (3.24)</td>
</tr>
<tr>
<td valign="middle" align="center">4</td>
<td valign="middle" align="center">L02BG04</td>
<td valign="middle" align="center">Letrozole</td>
<td valign="middle" align="center">221</td>
<td valign="middle" align="center">11.01 (9.63-12.58)</td>
<td valign="middle" align="center">10.9 (1965.08)</td>
<td valign="middle" align="center">10.78 (9.64)</td>
<td valign="middle" align="center">3.43 (3.23)</td>
</tr>
<tr>
<td valign="middle" align="center">5</td>
<td valign="middle" align="center">L01EA02</td>
<td valign="middle" align="center">Dasatinib</td>
<td valign="middle" align="center">299</td>
<td valign="middle" align="center">10.25 (9.14-11.5)</td>
<td valign="middle" align="center">10.16 (2431.52)</td>
<td valign="middle" align="center">10.01 (9.09)</td>
<td valign="middle" align="center">3.32 (3.15)</td>
</tr>
<tr>
<td valign="middle" align="center">6</td>
<td valign="middle" align="center">L01EF02</td>
<td valign="middle" align="center">Ribociclib</td>
<td valign="middle" align="center">141</td>
<td valign="middle" align="center">8.42 (7.13-9.94)</td>
<td valign="middle" align="center">8.36 (907.11)</td>
<td valign="middle" align="center">8.3 (7.22)</td>
<td valign="middle" align="center">3.05 (2.81)</td>
</tr>
<tr>
<td valign="middle" align="center">7</td>
<td valign="middle" align="center">L01EF03</td>
<td valign="middle" align="center">Abemaciclib</td>
<td valign="middle" align="center">86</td>
<td valign="middle" align="center">7.3 (5.9-9.03)</td>
<td valign="middle" align="center">7.25 (461.7)</td>
<td valign="middle" align="center">7.22 (6.04)</td>
<td valign="middle" align="center">2.85 (2.54)</td>
</tr>
<tr>
<td valign="middle" align="center">8</td>
<td valign="middle" align="center">L01EX03</td>
<td valign="middle" align="center">Pazopanib</td>
<td valign="middle" align="center">174</td>
<td valign="middle" align="center">7.05 (6.07-8.19)</td>
<td valign="middle" align="center">7.01 (888.74)</td>
<td valign="middle" align="center">6.95 (6.13)</td>
<td valign="middle" align="center">2.8 (2.58)</td>
</tr>
<tr>
<td valign="middle" align="center">9</td>
<td valign="middle" align="center">L01EC01</td>
<td valign="middle" align="center">Vemurafenib</td>
<td valign="middle" align="center">65</td>
<td valign="middle" align="center">6.87 (5.38-8.77)</td>
<td valign="middle" align="center">6.82 (322.34)</td>
<td valign="middle" align="center">6.8 (5.55)</td>
<td valign="middle" align="center">2.77 (2.41)</td>
</tr>
<tr>
<td valign="middle" align="center">10</td>
<td valign="middle" align="center">L01BC01</td>
<td valign="middle" align="center">Cytarabine</td>
<td valign="middle" align="center">74</td>
<td valign="middle" align="center">6.13 (4.88-7.71)</td>
<td valign="middle" align="center">6.1 (314.5)</td>
<td valign="middle" align="center">6.08 (5.02)</td>
<td valign="middle" align="center">2.6 (2.27)</td>
</tr>
<tr>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">L01DB01</td>
<td valign="middle" align="center">Doxorubicin</td>
<td valign="middle" align="center">198</td>
<td valign="middle" align="center">6.06 (5.27-6.98)</td>
<td valign="middle" align="center">6.03 (823.4)</td>
<td valign="middle" align="center">5.98 (5.32)</td>
<td valign="middle" align="center">2.58 (2.37)</td>
</tr>
<tr>
<td valign="middle" align="center">12</td>
<td valign="middle" align="center">L01BA01</td>
<td valign="middle" align="center">Methotrexate</td>
<td valign="middle" align="center">766</td>
<td valign="middle" align="center">6.01 (5.59-6.46)</td>
<td valign="middle" align="center">5.98 (3052.02)</td>
<td valign="middle" align="center">5.78 (5.44)</td>
<td valign="middle" align="center">2.53 (2.42)</td>
</tr>
<tr>
<td valign="middle" align="center">13</td>
<td valign="middle" align="center">L01EC02</td>
<td valign="middle" align="center">Dabrafenib</td>
<td valign="middle" align="center">72</td>
<td valign="middle" align="center">5.49 (4.35-6.92)</td>
<td valign="middle" align="center">5.46 (261.75)</td>
<td valign="middle" align="center">5.45 (4.48)</td>
<td valign="middle" align="center">2.45 (2.11)</td>
</tr>
<tr>
<td valign="middle" align="center">14</td>
<td valign="middle" align="center">L01FF02</td>
<td valign="middle" align="center">Pembrolizumab</td>
<td valign="middle" align="center">245</td>
<td valign="middle" align="center">5.23 (4.61-5.93)</td>
<td valign="middle" align="center">5.2 (822.33)</td>
<td valign="middle" align="center">5.15 (4.63)</td>
<td valign="middle" align="center">2.36 (2.18)</td>
</tr>
<tr>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">L01FX04</td>
<td valign="middle" align="center">Ipilimumab</td>
<td valign="middle" align="center">91</td>
<td valign="middle" align="center">5.15 (4.19-6.34)</td>
<td valign="middle" align="center">5.13 (301.66)</td>
<td valign="middle" align="center">5.11 (4.3)</td>
<td valign="middle" align="center">2.35 (2.05)</td>
</tr>
<tr>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">L01EH01</td>
<td valign="middle" align="center">Lapatinib</td>
<td valign="middle" align="center">62</td>
<td valign="middle" align="center">5.04 (3.92-6.47)</td>
<td valign="middle" align="center">5.01 (198.84)</td>
<td valign="middle" align="center">5 (4.06)</td>
<td valign="middle" align="center">2.32 (1.96)</td>
</tr>
<tr>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">L01FF05</td>
<td valign="middle" align="center">Atezolizumab</td>
<td valign="middle" align="center">106</td>
<td valign="middle" align="center">4.87 (4.02-5.9)</td>
<td valign="middle" align="center">4.85 (322.47)</td>
<td valign="middle" align="center">4.83 (4.11)</td>
<td valign="middle" align="center">2.27 (1.99)</td>
</tr>
<tr>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">L01BA04</td>
<td valign="middle" align="center">Pemetrexed</td>
<td valign="middle" align="center">61</td>
<td valign="middle" align="center">4.81 (3.74-6.19)</td>
<td valign="middle" align="center">4.79 (182.52)</td>
<td valign="middle" align="center">4.78 (3.87)</td>
<td valign="middle" align="center">2.26 (1.89)</td>
</tr>
<tr>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">L01FD01</td>
<td valign="middle" align="center">Trastuzumab</td>
<td valign="middle" align="center">146</td>
<td valign="middle" align="center">4.31 (3.66-5.08)</td>
<td valign="middle" align="center">4.3 (367.19)</td>
<td valign="middle" align="center">4.27 (3.73)</td>
<td valign="middle" align="center">2.1 (1.86)</td>
</tr>
<tr>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">L01EK01</td>
<td valign="middle" align="center">Axitinib</td>
<td valign="middle" align="center">54</td>
<td valign="middle" align="center">3.53 (2.7-4.62)</td>
<td valign="middle" align="center">3.52 (97.39)</td>
<td valign="middle" align="center">3.52 (2.81)</td>
<td valign="middle" align="center">1.81 (1.42)</td>
</tr>
<tr>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">L01XA02</td>
<td valign="middle" align="center">Carboplatin</td>
<td valign="middle" align="center">147</td>
<td valign="middle" align="center">3.39 (2.88-3.99)</td>
<td valign="middle" align="center">3.38 (245.11)</td>
<td valign="middle" align="center">3.36 (2.94)</td>
<td valign="middle" align="center">1.75 (1.51)</td>
</tr>
<tr>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">L01EX09</td>
<td valign="middle" align="center">Nintedanib</td>
<td valign="middle" align="center">71</td>
<td valign="middle" align="center">3.35 (2.66-4.24)</td>
<td valign="middle" align="center">3.35 (116.47)</td>
<td valign="middle" align="center">3.34 (2.75)</td>
<td valign="middle" align="center">1.74 (1.4)</td>
</tr>
<tr>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">L01AA01</td>
<td valign="middle" align="center">Cyclophosphamide</td>
<td valign="middle" align="center">97</td>
<td valign="middle" align="center">3.2 (2.62-3.91)</td>
<td valign="middle" align="center">3.19 (145.54)</td>
<td valign="middle" align="center">3.18 (2.69)</td>
<td valign="middle" align="center">1.67 (1.38)</td>
</tr>
<tr>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">L01FA01</td>
<td valign="middle" align="center">Rituximab</td>
<td valign="middle" align="center">306</td>
<td valign="middle" align="center">3.15 (2.82-3.53)</td>
<td valign="middle" align="center">3.15 (441.13)</td>
<td valign="middle" align="center">3.11 (2.83)</td>
<td valign="middle" align="center">1.64 (1.47)</td>
</tr>
<tr>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">L01EA01</td>
<td valign="middle" align="center">Imatinib</td>
<td valign="middle" align="center">176</td>
<td valign="middle" align="center">3.06 (2.64-3.55)</td>
<td valign="middle" align="center">3.05 (240.84)</td>
<td valign="middle" align="center">3.03 (2.68)</td>
<td valign="middle" align="center">1.6 (1.38)</td>
</tr>
<tr>
<td valign="middle" align="center">26</td>
<td valign="middle" align="center">L04AC07</td>
<td valign="middle" align="center">Tocilizumab</td>
<td valign="middle" align="center">176</td>
<td valign="middle" align="center">3.06 (2.64-3.55)</td>
<td valign="middle" align="center">3.06 (241.4)</td>
<td valign="middle" align="center">3.04 (2.68)</td>
<td valign="middle" align="center">1.6 (1.38)</td>
</tr>
<tr>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">L01CD01</td>
<td valign="middle" align="center">Paclitaxel</td>
<td valign="middle" align="center">108</td>
<td valign="middle" align="center">2.98 (2.46-3.6)</td>
<td valign="middle" align="center">2.97 (140.47)</td>
<td valign="middle" align="center">2.96 (2.53)</td>
<td valign="middle" align="center">1.57 (1.29)</td>
</tr>
<tr>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">L01BC05</td>
<td valign="middle" align="center">Gemcitabine</td>
<td valign="middle" align="center">78</td>
<td valign="middle" align="center">2.92 (2.34-3.65)</td>
<td valign="middle" align="center">2.92 (97.95)</td>
<td valign="middle" align="center">2.91 (2.41)</td>
<td valign="middle" align="center">1.54 (1.21)</td>
</tr>
<tr>
<td valign="middle" align="center">29</td>
<td valign="middle" align="center">L01FF01</td>
<td valign="middle" align="center">Nivolumab</td>
<td valign="middle" align="center">188</td>
<td valign="middle" align="center">2.89 (2.5-3.33)</td>
<td valign="middle" align="center">2.88 (228.65)</td>
<td valign="middle" align="center">2.86 (2.54)</td>
<td valign="middle" align="center">1.52 (1.31)</td>
</tr>
<tr>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">L01XA03</td>
<td valign="middle" align="center">Oxaliplatin</td>
<td valign="middle" align="center">88</td>
<td valign="middle" align="center">2.76 (2.24-3.4)</td>
<td valign="middle" align="center">2.75 (98.03)</td>
<td valign="middle" align="center">2.75 (2.3)</td>
<td valign="middle" align="center">1.46 (1.15)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>ADE, adverse drug event; ATC, Anatomical Therapeutic Chemical; CI, confidence interval; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; BCPNN, Bayesian Confidence Propagation Neural Network; MGPS, Multi-item Gamma Poisson Shrinker.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Forest plot of ROR values for different type of anti-neoplastic agents associated with hepatotoxicity in the FAERS database.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g003.tif">
<alt-text content-type="machine-generated">This forest plot  shows ROR values (with 95% CI) for anti-neoplastic agents linked to hepatotoxicity . Enzymes have the highest ROR (13.67, 95% CI:10.59-17.64) with 60 cases, while Monoclonal Antibodies have the lowest (2.55, 95% CI:2.32-2.81) with 452 cases. RORs &gt;1 (red line) indicate hepatotoxicity association.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Risk factors for hepatotoxicity related to anti-neoplastic agents</title>
<p>Suspected drugs with &gt;100 case reports, a lower 95% CI limit of the ROR &gt;1 and p-adjust &lt; 0.01 were extracted for univariate analysis. Drugs with p &lt; 0.01 in univariate analysis were subjected to LASSO regression, and a total of 13 drugs were identified (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>). Multi-factor logistic regression analysis of these drugs was performed in combination with patient information (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>). Analysis showed that 13 drugs, including pazopanib, ribociclib, letrozole, methotrexate, pembrolizumab, trastuzumab, doxorubicin, atezolizumab, imatinib, paclitaxel, carboplatin, nivolumab and tocilizumab were identified as independent risk factors for anti-neoplastic agents associated with hepatotoxicity.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Results of LASSO regression analysis. LASSO, least absolute shrinkage and selection operator.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g004.tif">
<alt-text content-type="machine-generated">This presents LASSO regression results. The left plot shows AUC (with error bars) against log(&#x3bb;): AUC stays stable initially then decreases as log(&#x3bb;) rises, with dashed lines marking key &#x3bb; points (top numbers show feature counts). The right plot displays coefficient shrinkage: coefficients decline with log(&#x3bb;), eventually approaching 0, reflecting feature selection by LASSO.</alt-text>
</graphic></fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Results arising from multi-factor logistic regression analysis. CI, confidence interval; OR, odds ratio; P-adjust, p-value after Bonferroni correction; P-adjust&lt;0.01, statistically significant.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g005.tif">
<alt-text content-type="machine-generated">Table displaying odds ratios (OR) with 95% confidence intervals (CI) and p-values for various variables. Female and male have ORs of 0.5 and 0.7, respectively. Age has an OR of 0.99. The medications Pazopanib, Ribociclib, Letrozole, and others show ORs ranging from 12 to 2.3, all with p-values less than 0.01. A forest plot on the right illustrates these values with confidence intervals.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Time to onset analysis</title>
<p>To enhance the credibility of our TTO assessment, data inconsistencies and missing values were systematically removed, yielding a reduced cohort for subsequent analysis compared to the original dataset. <xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref> illustrates the temporal distribution of events, with 44.18% of subjects (n=338) experiencing onset within the first 30 days following the initiation of treatment. <xref ref-type="table" rid="T5"><bold>Table&#xa0;5</bold></xref> presents the finding of TTO and WSP analysis for the top 30 anti-neoplastic agents associated with hepatotoxicity. According to Weibull assessment, most of the top 30 drugs were random failure models, indicating that hepatotoxicity could occur at any point during treatment without specific time dependency. Cumulative distribution curves demonstrated the onset time for anti-neoplastic agents associated with hepatotoxicity among the different subgroups (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7</bold></xref>). The median onset time for hepatotoxicity associated with male gender was 29.5 days (IQR:13&#x2013;75.5), compared to 42 days (IQR: 18&#x2013;87.5) for female cases; there was no significant difference between males and females (<italic>P</italic>&#xa0;=&#xa0;0.7287). Significant differences were detected for the TTO of hepatotoxicity in terms of fatal status. The median onset time was 22.5 days (IQR 9.25&#x2013;66) for fatal cases; this was significantly different than the 42 days (IQR:18&#x2013;83) for non-fatal cases (<italic>P</italic> =&#xa0;0.00051).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Time-to-onset (TTO) of anti-neoplastic-associated hepatotoxicity.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g006.tif">
<alt-text content-type="machine-generated">Bar chart showing time to event onset in days, with percentage and case number. The 0-30 days category has the highest values with 44.18% and 338 cases. Other categories are progressively smaller, with 31-60 days at 22.61% and 173 cases, and &gt;360 days at 4.58% and 35 cases.</alt-text>
</graphic></fig>
<table-wrap id="T5" position="float">
<label>Table&#xa0;5</label>
<caption>
<p>TTO for the top 30 anti-neoplastic associated with ADE of hepatotoxicity.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="3" align="center">SN</th>
<th valign="middle" rowspan="3" align="center">Drug</th>
<th valign="middle" rowspan="2" align="center">Cases</th>
<th valign="middle" rowspan="2" colspan="2" align="center">TTO (days)</th>
<th valign="middle" colspan="4" align="center">Weibull distribution</th>
<th valign="middle" rowspan="3" align="center">Failure type</th>
</tr>
<tr>
<th valign="middle" colspan="2" align="center">Scale parameter</th>
<th valign="middle" colspan="2" align="center">Shape parameter</th>
</tr>
<tr>
<th valign="middle" align="center">n</th>
<th valign="middle" align="center">Median</th>
<th valign="middle" align="center">IQR</th>
<th valign="middle" align="center">&#x3b1;</th>
<th valign="middle" align="center">95%CI</th>
<th valign="middle" align="center">&#x3b2;</th>
<th valign="middle" align="center">95%CI</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">Mercaptopurine</td>
<td valign="middle" align="center">67</td>
<td valign="middle" align="center">72</td>
<td valign="middle" align="center">10.5-315</td>
<td valign="middle" align="center">235.14</td>
<td valign="middle" align="center">17.16- 453.11</td>
<td valign="middle" align="center">0.43</td>
<td valign="middle" align="center">0.31-0.55</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">2</td>
<td valign="middle" align="center">Pegaspargase</td>
<td valign="middle" align="center">60</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">1-7.5</td>
<td valign="middle" align="center">4.66</td>
<td valign="middle" align="center">-2.33-11.64</td>
<td valign="middle" align="center">0.80</td>
<td valign="middle" align="center">0.11-1.50</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">3</td>
<td valign="middle" align="center">Blinatumomab</td>
<td valign="middle" align="center">85</td>
<td valign="middle" align="center">4</td>
<td valign="middle" align="center">1-10</td>
<td valign="middle" align="center">11.30</td>
<td valign="middle" align="center">-0.87-23.46</td>
<td valign="middle" align="center">0.54</td>
<td valign="middle" align="center">0.34- 0.74</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">4</td>
<td valign="middle" align="center">Letrozole</td>
<td valign="middle" align="center">221</td>
<td valign="middle" align="center">51</td>
<td valign="middle" align="center">27.5-91</td>
<td valign="middle" align="center">86.35</td>
<td valign="middle" align="center">64.58-108.12</td>
<td valign="middle" align="center">0.93</td>
<td valign="middle" align="center">0.78-1.07</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">5</td>
<td valign="middle" align="center">Dasatinib</td>
<td valign="middle" align="center">299</td>
<td valign="middle" align="center">64</td>
<td valign="middle" align="center">36-94.5</td>
<td valign="middle" align="center">68.87</td>
<td valign="middle" align="center">-1.38-139.12</td>
<td valign="middle" align="center">1.16</td>
<td valign="middle" align="center">0.03- 2.29</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">6</td>
<td valign="middle" align="center">Ribociclib</td>
<td valign="middle" align="center">141</td>
<td valign="middle" align="center">26</td>
<td valign="middle" align="center">10.5-114.5</td>
<td valign="middle" align="center">70.14</td>
<td valign="middle" align="center">-9.90-150.19</td>
<td valign="middle" align="center">0.69</td>
<td valign="middle" align="center">0.30- 1.08</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">7</td>
<td valign="middle" align="center">Abemaciclib</td>
<td valign="middle" align="center">86</td>
<td valign="middle" align="center">60</td>
<td valign="middle" align="center">21-84</td>
<td valign="middle" align="center">76.99</td>
<td valign="middle" align="center">16.45-137.53</td>
<td valign="middle" align="center">0.88</td>
<td valign="middle" align="center">0.45-1.31</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">8</td>
<td valign="middle" align="center">Pazopanib</td>
<td valign="middle" align="center">174</td>
<td valign="middle" align="center">40.5</td>
<td valign="middle" align="center">21-49</td>
<td valign="middle" align="center">49.52</td>
<td valign="middle" align="center">30.08-68.97</td>
<td valign="middle" align="center">1.13</td>
<td valign="middle" align="center">0.81-1.44</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">9</td>
<td valign="middle" align="center">Vemurafenib</td>
<td valign="middle" align="center">65</td>
<td valign="middle" align="center">43.5</td>
<td valign="middle" align="center">12-56.25</td>
<td valign="middle" align="center">48.53</td>
<td valign="middle" align="center">19.13-77.92</td>
<td valign="middle" align="center">1.08</td>
<td valign="middle" align="center">0.57- 1.59</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">10</td>
<td valign="middle" align="center">Cytarabine</td>
<td valign="middle" align="center">74</td>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">12-15</td>
<td valign="middle" align="center">15.77</td>
<td valign="middle" align="center">10.28-21.26</td>
<td valign="middle" align="center">2.64</td>
<td valign="middle" align="center">0.77-4.52</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">Doxorubicin</td>
<td valign="middle" align="center">198</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="center">12</td>
<td valign="middle" align="center">Methotrexate</td>
<td valign="middle" align="center">766</td>
<td valign="middle" align="center">72</td>
<td valign="middle" align="center">10.5-315</td>
<td valign="middle" align="center">235.14</td>
<td valign="middle" align="center">17.16-453.11</td>
<td valign="middle" align="center">0.43</td>
<td valign="middle" align="center">0.31- 0.55</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">13</td>
<td valign="middle" align="center">Dabrafenib</td>
<td valign="middle" align="center">72</td>
<td valign="middle" align="center">107</td>
<td valign="middle" align="center">58-195</td>
<td valign="middle" align="center">184.32</td>
<td valign="middle" align="center">83.89-284.74</td>
<td valign="middle" align="center">0.93</td>
<td valign="middle" align="center">0.61-1.24</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">14</td>
<td valign="middle" align="center">Pembrolizumab</td>
<td valign="middle" align="center">245</td>
<td valign="middle" align="center">50.5</td>
<td valign="middle" align="center">21-71.75</td>
<td valign="middle" align="center">73.55</td>
<td valign="middle" align="center">36.59-110.50</td>
<td valign="middle" align="center">0.84</td>
<td valign="middle" align="center">0.61-1.08</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">Ipilimumab</td>
<td valign="middle" align="center">91</td>
<td valign="middle" align="center">44</td>
<td valign="middle" align="center">22-96</td>
<td valign="middle" align="center">82.92</td>
<td valign="middle" align="center">41.31-124.52</td>
<td valign="middle" align="center">0.91</td>
<td valign="middle" align="center">0.63-1.18</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">Lapatinib</td>
<td valign="middle" align="center">62</td>
<td valign="middle" align="center">42</td>
<td valign="middle" align="center">36-91</td>
<td valign="middle" align="center">77.21</td>
<td valign="middle" align="center">48.73-105.70</td>
<td valign="middle" align="center">1.46</td>
<td valign="middle" align="center">0.91-2.01</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">Atezolizumab</td>
<td valign="middle" align="center">106</td>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">19.5-112.5</td>
<td valign="middle" align="center">74.98</td>
<td valign="middle" align="center">37.45-112.50</td>
<td valign="middle" align="center">0.75</td>
<td valign="middle" align="center">0.55-0.94</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">Pemetrexed</td>
<td valign="middle" align="center">61</td>
<td valign="middle" align="center">41</td>
<td valign="middle" align="center">41-41</td>
<td valign="middle" align="center">45.17</td>
<td valign="middle" align="center">37.09-53.25</td>
<td valign="middle" align="center">3.10</td>
<td valign="middle" align="center">1.94-4.26</td>
<td valign="middle" align="center">Wear-out Failure</td>
</tr>
<tr>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">Trastuzumab</td>
<td valign="middle" align="center">146</td>
<td valign="middle" align="center">49</td>
<td valign="middle" align="center">23-101</td>
<td valign="middle" align="center">212.76</td>
<td valign="middle" align="center">32.21-393.31</td>
<td valign="middle" align="center">0.54</td>
<td valign="middle" align="center">0.37-0.71</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">Axitinib</td>
<td valign="middle" align="center">54</td>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">11.5-37</td>
<td valign="middle" align="center">27.13</td>
<td valign="middle" align="center">-1.67-55.93</td>
<td valign="middle" align="center">1.12</td>
<td valign="middle" align="center">0.09-2.16</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">Carboplatin</td>
<td valign="middle" align="center">147</td>
<td valign="middle" align="center">45</td>
<td valign="middle" align="center">31-78.5</td>
<td valign="middle" align="center">63.91</td>
<td valign="middle" align="center">40.60-87.23</td>
<td valign="middle" align="center">1.17</td>
<td valign="middle" align="center">0.78-1.56</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">Nintedanib</td>
<td valign="middle" align="center">71</td>
<td valign="middle" align="center">9.5</td>
<td valign="middle" align="center">3.5-71</td>
<td valign="middle" align="center">33.34</td>
<td valign="middle" align="center">-13.08-79.77</td>
<td valign="middle" align="center">0.61</td>
<td valign="middle" align="center">0.23-0.99</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">23</td>
<td valign="middle" align="center">Cyclophosphamide</td>
<td valign="middle" align="center">97</td>
<td valign="middle" align="center">37</td>
<td valign="middle" align="center">16-63</td>
<td valign="middle" align="center">39.10</td>
<td valign="middle" align="center">-8.55-86.75</td>
<td valign="middle" align="center">0.84</td>
<td valign="middle" align="center">0.12-1.55</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">Rituximab</td>
<td valign="middle" align="center">306</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">9-39</td>
<td valign="middle" align="center">48.73</td>
<td valign="middle" align="center">-13.87-111.34</td>
<td valign="middle" align="center">0.61</td>
<td valign="middle" align="center">0.28-0.94</td>
<td valign="middle" align="center">Early Failure</td>
</tr>
<tr>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">Imatinib</td>
<td valign="middle" align="center">176</td>
<td valign="middle" align="center">75.5</td>
<td valign="middle" align="center">53.75-87.75</td>
<td valign="middle" align="center">73.85</td>
<td valign="middle" align="center">37.88-109.82</td>
<td valign="middle" align="center">2.09</td>
<td valign="middle" align="center">0.23-3.95</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">26</td>
<td valign="middle" align="center">Tocilizumab</td>
<td valign="middle" align="center">176</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">7.25-40.75</td>
<td valign="middle" align="center">29.90</td>
<td valign="middle" align="center">-1.40-61.20</td>
<td valign="middle" align="center">0.99</td>
<td valign="middle" align="center">0.24-1.75</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">27</td>
<td valign="middle" align="center">Paclitaxel</td>
<td valign="middle" align="center">108</td>
<td valign="middle" align="center">56</td>
<td valign="middle" align="center">32-73</td>
<td valign="middle" align="center">55.94</td>
<td valign="middle" align="center">42.16-69.72</td>
<td valign="middle" align="center">1.82</td>
<td valign="middle" align="center">1.18-2.46</td>
<td valign="middle" align="center">Wear-out Failure</td>
</tr>
<tr>
<td valign="middle" align="center">28</td>
<td valign="middle" align="center">Gemcitabine</td>
<td valign="middle" align="center">78</td>
<td valign="middle" align="center">19.5</td>
<td valign="middle" align="center">5.5-54.5</td>
<td valign="middle" align="center">35.06</td>
<td valign="middle" align="center">-11.22-81.33</td>
<td valign="middle" align="center">0.79</td>
<td valign="middle" align="center">0.19-1.39</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">29</td>
<td valign="middle" align="center">Nivolumab</td>
<td valign="middle" align="center">188</td>
<td valign="middle" align="center">47.5</td>
<td valign="middle" align="center">20.25-71</td>
<td valign="middle" align="center">68.38</td>
<td valign="middle" align="center">50.14-86.61</td>
<td valign="middle" align="center">1.06</td>
<td valign="middle" align="center">0.85-1.27</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
<tr>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">Oxaliplatin</td>
<td valign="middle" align="center">88</td>
<td valign="middle" align="center">10.5</td>
<td valign="middle" align="center">4-14</td>
<td valign="middle" align="center">16.36</td>
<td valign="middle" align="center">3.01-29.71</td>
<td valign="middle" align="center">0.90</td>
<td valign="middle" align="center">0.46-1.35</td>
<td valign="middle" align="center">Random Failure</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SN, Serial Number; TTO, Time-to-onset; ADE, adverse drug event; CI, confidence interval; IQR, interquartile range.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Cumulative distribution curves demonstrating the time-to-onset (TTO) for anti-neoplastic-related hepatotoxicity among different subgroups. <bold>(A)</bold>Sex. <bold>(B)</bold> Fatal status. Statistical significance was assessed using the non-parametric Wilcoxon rank sum test.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1665425-g007.tif">
<alt-text content-type="machine-generated">Two cumulative distribution graphs, labeled A and B. Graph A compares time to onset in days for males and females, with median times of 29.5 days for males and 42 days for females. The Wilcoxon P-value is 0.7287. Graph B compares time to onset for fatal and non-fatal outcomes, showing median times of 22.5 days for fatal and 42 days for non-fatal cases. The Wilcoxon P-value is 0.00051.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Hepatotoxicity represents a well-documented adverse reaction associated with anti-neoplastic therapies. However, there is a scarcity of large-scale studies investigating the potential correlation between anti-neoplastic agents and hepatoxicity. In this study, we mined data from the FAERS database to identify anti-neoplastic agents associated with hepatotoxicity. Small molecule kinase inhibitors accounted for the highest proportion of case reports, with submissions predominantly originating from Europe and North America. Demographic analysis demonstrated that most AEs involved females, and that most AEs were concentrated in patients aged 18&#x2013;64.9 years, with a median age of 56 years. Temporally, 44.18% of events occurred within one month of the initiation of treatment. Notably, life-threatening outcomes or fatalities were documented in 14.95% of cases.</p>
<p>In the present study, we revealed a higher incidence of hepatotoxicity associated with anti-neoplastic agents in female patients when compared to males. This disparity may have arisen because of sex-specific physiological variations, including differences in body weight, fat distribution, hepatic function and gastrointestinal physiology (<xref ref-type="bibr" rid="B15">15</xref>). When administering equivalent drug doses, these factors may impair metabolic efficiency in females, thereby elevating the risk of adverse drug reactions. Furthermore, hepatotoxic AEs associated with anti-neoplastic agents predominantly occurred in individuals aged 18&#x2013;64.9 year. Previous research showed that advanced age represents a critical risk factor for DILI (<xref ref-type="bibr" rid="B16">16</xref>). Elderly patients are predisposed to hepatotoxicity due to age-related immune decline, higher burdens of comorbidity and diminished drug metabolism capacity (<xref ref-type="bibr" rid="B17">17</xref>). However, two prospective studies from the United States and Spain failed to identify significant differences in the incidence of hepatotoxicity when compared between elderly and adolescent populations, although elderly individuals exhibited a higher incidence of cholestatic liver injury than younger adults (<xref ref-type="bibr" rid="B18">18</xref>). These findings might be related to different geographical environments.</p>
<p>With regards to the TTO for hepatotoxicity, we found that over half of all hepatotoxicity cases occurred within the first two months of treatment, thus indicating that most hepatotoxicity events arise early, consistent with previous studies (<xref ref-type="bibr" rid="B19">19</xref>). Moreover, the median TTO was shorter in fatal cases than in non-fatal cases, thus indicating more rapid disease progression in fatal outcomes. This disparity may be attributed to the critical nature of these cases compounded by poor baseline health status, particularly pre-existing liver damage, which could enhance susceptibility to drug-induced AEs and thereby accelerate pathological deterioration. Collectively, these findings highlight the importance of early identification and therapeutic intervention for drug-induced hepatotoxicity to mitigate disease escalation. The WSP test further revealed that the occurrence of hepatotoxicity for most drugs was not time-dependent, thus suggesting that hepatotoxicity may arise at any phase during the treatment process. Therefore, although enhanced monitoring of hepatic function is critical during the initial phase of chemotherapy, continuous surveillance throughout the entire course of treatment is essential to ensure that no potential cases are missed.</p>
<p>Further classification revealed that enzymes had the highest ROR values. Pegaspargase, a covalent conjugate of polyethylene glycol (PEG) and L-asparaginase, serves as a first-line agent for the treatment of acute lymphoblastic leukemia (ALL). However, hepatotoxicity frequently occurs during pegaspargase therapy. A previous clinical study demonstrated that asparaginase may induce liver injury accompanied by jaundice, typically characterized by short latency, marked steatosis, and prolonged cholestasis, potentially attributable to the inhibition of hepatic protein synthesis secondary to the depletion of asparagine (<xref ref-type="bibr" rid="B20">20</xref>). Interestingly, a retrospective analysis of 141 pegaspargase-treated patients revealed that administration on day 15 of the ALL induction phase significantly reduced the risk of high-grade hepatotoxicity when compared to dosing on day 4, while age and elevated BMI were further identified as independent risk factors for severe hepatotoxicity (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Furthermore, a pharmacokinetic study of pegaspargase demonstrated that an interval of more than four weeks between successive administrations is required to prevent drug accumulation in adults (<xref ref-type="bibr" rid="B23">23</xref>). Therefore, delayed administration, BMI optimization and controlled dosing intervals should be considered in clinical practice to mitigate pegaspargase-associated adverse effects.</p>
<p>Multi-factor analysis further showed that small molecule kinase inhibitors (pazopanib, ribociclib and imatinib), immune checkpoint inhibitors (pembrolizumab, atezolizumab and nivolumab), letrozole, methotrexate, doxorubicin, paclitaxel, tocilizumab were all risk factors for the induction of drug-related hepatotoxicity.</p>
<p>In this study, we revealed that small molecule kinase inhibitors accounted for the highest proportion of drug-induced hepatotoxicity. Pazopanib, as a tyrosine kinase inhibitor (TKI), is primarily utilized to treat patients with advanced renal cell carcinoma. While TKIs represent breakthrough therapies for several malignancies, these drugs are also associated with a high incidence of hepatotoxicity. As of April 2021, six approved TKIs (11%) carried black box warnings for hepatotoxicity, and an additional 25 agents (46%) included hepatotoxicity alerts in their prescribing information (<xref ref-type="bibr" rid="B24">24</xref>). A case report highlighted the fact that two patients developed severe hepatotoxicity within six weeks of the initiation of pazopanib and that one of these patients succumbed to this toxicity (<xref ref-type="bibr" rid="B25">25</xref>). In terms of mechanistic action, a previous study of sunitinib-induced hepatotoxicity demonstrated that sunitinib caused regional damage to hepatocytes, bile duct cells, and hepatic sinusoidal endothelial cells in the portal vein area; this process was associated with hallmark cellular events including autophagy, apoptosis, and mitochondrial injury (<xref ref-type="bibr" rid="B26">26</xref>). Beyond such intrinsic drug toxicity, hepatotoxicity can also be exacerbated by drug-drug interactions. For instance, the co-administration of erlotinib, which is primarily metabolized by CYP3A4, with strong inhibitors of this enzyme (e.g., ketoconazole or ritonavir) has been shown to elevate its systemic exposure, thereby increasing the risk of DILI (<xref ref-type="bibr" rid="B27">27</xref>). These findings highlight the necessity for rigorous monitoring of liver function parameters during the clinical application of such small molecule kinase inhibitors.</p>
<p>The advent of ICIs represented a groundbreaking advancement in oncology and was recognized by the 2018 Nobel Prize in Physiology or Medicine. ICIs are monoclonal antibodies that target immune checkpoint molecules, thus providing an immunotherapeutic approach for numerous advanced malignancies. The U.S. FDA approved the first ICI, ipilimumab, in 2011 for the treatment of metastatic melanoma. During ICI therapy, 2&#x2013;25% of patients may develop hepatic dysfunction characterized by abnormal hepatocellular serum biochemical parameters (<xref ref-type="bibr" rid="B28">28</xref>). Immune-related adverse events (irAEs) limit the clinical application of ICIs, with hepatotoxicity constituting a critical form of irAE. FDA clinical trials and observational studies have shown that up to 16% of patients receiving ICIs may experience immune-mediated liver injury, although the incidence of this condition varies substantially depending on ICI class, dosage and therapeutic regimens (<xref ref-type="bibr" rid="B29">29</xref>). When evaluating hepatotoxicity associated with ICIs, it is crucial to distinguish direct drug-induced liver injury from potentially confounding irAEs. Immune-mediated pancreatitis may lead to secondary cholestasis due to biliary obstruction (<xref ref-type="bibr" rid="B30">30</xref>), whereas drug-induced autoimmune hepatitis (DIAIH) presents with clinical features that overlap between classic drug-induced liver injury and autoimmune hepatitis. A reliable diagnosis of DIAIH requires the concurrent use of both the RUCAM and the simplified AIH score (<xref ref-type="bibr" rid="B31">31</xref>). Misclassification of these irAEs could compromise the specificity of hepatotoxicity signals attributed directly to ICIs. The mechanisms underlying checkpoint inhibitor-induced immune-mediated hepatotoxicity have yet to be fully elucidated. Notably, Johncilla et&#xa0;al. (<xref ref-type="bibr" rid="B32">32</xref>) reported the increased expression of T-cell activation markers in liver specimens acquired from 11 patients who developed hepatic injury following ipilimumab treatment, thus suggesting that this may represent one potential mechanism underlying ICI-induced hepatotoxicity. Furthermore, analysis of a mouse model demonstrated that ICI treatment induced liver damage as well as hepatocyte apoptosis and activation of the Nod-like receptor protein 3 (NLRP3) inflammasome (<xref ref-type="bibr" rid="B33">33</xref>).</p>
<p>Letrozole, a hormonal anti-neoplastic agent, is a highly selective aromatase inhibitor and serves as a first-line treatment for locally advanced or postmenopausal breast cancer. However, cases of letrozole-associated hepatotoxicity have been reported. For example, existing literature documents the case of a 70-year-old female patient who developed jaundice and markedly elevated levels of hepatic transaminases after three months of letrozole treatment, with liver biopsy confirming drug-induced hepatotoxicity. Her liver function gradually returned to normal within three weeks of drug discontinuation (<xref ref-type="bibr" rid="B34">34</xref>). A phase II clinical trial further reported that letrozole may increase the risk of hepatotoxicity (<xref ref-type="bibr" rid="B35">35</xref>). Although reports of letrozole-induced liver injury remain relatively rare, the close monitoring of liver function is strongly recommended during the clinical administration of letrozole.</p>
<p>Methotrexate, an anti-metabolite and anti-neoplastic agent, is primarily used for maintenance therapy in patients with ALL. Hepatotoxicity has been established as a severe adverse reaction associated with methotrexate. A meta-analysis of 32 studies, involving 13,177 patients, identified a significant association between the use of methotrexate and an increased risk of hepatic AEs (<xref ref-type="bibr" rid="B12">12</xref>). Furthermore, a randomized controlled trial (RCT) revealed significant elevations in the levels of liver enzymes in methotrexate-treated patients at 6- and 12-months intervals (<xref ref-type="bibr" rid="B13">13</xref>). Consistent with previous evidence, our analysis identified methotrexate as a risk factor for drug-induced liver injury. Furthermore, this hepatotoxicity was substantiated by pharmacogenomic findings linking polymorphisms in genes such as <italic>ABCC2</italic>, <italic>MTHFR</italic>, and <italic>SXR</italic> and <italic>MTX</italic>-induced toxicities, including hepatotoxicity and myelosuppression, in pediatric solid tumors (<xref ref-type="bibr" rid="B36">36</xref>). Mechanistically, <italic>in vitro</italic> studies have demonstrated that ferroptosis and oxidative stress contribute directly to MTX-induced liver injury (<xref ref-type="bibr" rid="B37">37</xref>).</p>
<p>The FAERS database comprises spontaneous reports of suspected adverse reactions that typically lack rigorous diagnostic validation for DILI. This represents a major methodological limitation, as the absence of a standardized causality assessment tool, such as the updated RUCAM, which is specifically validated for DILI, hinders the ability to distinguish true, idiosyncratic DILI from other causes of liver test abnormalities, including confounding immune-related adverse events or underlying diseases (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B38">38</xref>). Consequently, hepatotoxicity signals derived from such analyses remain associative rather than causally verified, limiting their mechanistic interpretation and clinical applicability. To enhance the quality and specificity of DILI data in pharmacovigilance, we strongly recommend that future case reporting and analysis mandate the application of the updated RUCAM. Inclusion of cases in registries or signal-detection studies should be contingent upon causality assessment using this validated instrument. Widespread adoption of RUCAM would improve the scientific rigor of pharmacovigilance research and yield more reliable data for characterizing the clinical and phenotypic spectrum of anti-neoplastic agent-induced DILI.</p>
<sec id="s4_1">
<label>4.1</label>
<title>Limitations</title>
<p>This study has several limitations that should be considered. First, a key limitation of our study is the inability to assess causality using the RUCAM scale due to insufficient clinical detail in FAERS reports. This highlights the need for future clinical studies to confirm our pharmacovigilance signals with established diagnostic tools. Second, AE reports in the FAERS database were collected by spontaneous reporting systems, which may have led to under-reporting, reporting biases, and frequent omission of critical patient demographic information&#x2014;particularly regarding age, sex, weight, and underlying medical conditions&#x2014;due to variations in pharmacovigilance practices across different regions and countries. Third, the disproportionality analysis method employed in this research can only identify statistical associations by quantitative signals. Such findings do not necessarily imply a definitive clinical causal relationship between the reported AEs and anti-neoplastic agents. Furthermore, some novel anti-neoplastic agents have been on the global market for a relatively short period of time, thus resulting in a relatively incomplete set of safety surveillance data. Therefore, further clinical studies are now warranted to validate these findings through more rigorous pharmacoepidemiological investigations.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>This study represents the first pharmacovigilance investigation utilizing the FAERS database to systematically analyze hepatotoxicity associated with anti-neoplastic agents. A key finding of our analysis was the contrast between the 56 anti-neoplastic agents significantly associated with hepatotoxicity and the complete lack of hepatotoxicity warnings in the package inserts for eleven of these agents. The median onset of hepatotoxicity in the Fatal group (22.5 days) was shorter than that in the non-fatal group (42 days). Furthermore, WSP analysis showed that 20 of the top 30 drugs by ROR value followed a random failure model, thus suggesting that the onset of hepatotoxicity could happen at any point during treatment, at random. In conclusion, our research provides crucial evidence for the clinical management and prevention of anti-neoplastic agents-induced hepatotoxicity. Nonetheless, given the inherent limitations of spontaneous reporting systems and signal detection methodologies, these findings warrant further validation through longitudinal pharmacoepidemiological studies incorporating comprehensive causality assessments.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>LS: Writing &#x2013; original draft, Writing &#x2013; review &amp; editing, Data curation. YW: Data curation, Writing &#x2013; review &amp; editing. YQ: Data curation, Writing &#x2013; review &amp; editing. RC: Writing &#x2013; review &amp; editing, Funding acquisition, Software, Supervision.</p></sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s11" sec-type="disclaimer">
<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>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Sung</surname> <given-names>H</given-names></name>
<name><surname>Ferlay</surname> <given-names>J</given-names></name>
<name><surname>Siegel</surname> <given-names>RL</given-names></name>
<name><surname>Laversanne</surname> <given-names>M</given-names></name>
<name><surname>Soerjomataram</surname> <given-names>I</given-names></name>
<name><surname>Jemal</surname> <given-names>A</given-names></name>
<etal/>
</person-group>. 
<article-title>Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries</article-title>. <source>CA Cancer J Clin</source>. (<year>2021</year>) <volume>71</volume>:<page-range>209&#x2013;49</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.3322/caac.21660</pub-id>, PMID: <pub-id pub-id-type="pmid">33538338</pub-id>
</mixed-citation>
</ref>
<ref id="B2">
<label>2</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Letai</surname> <given-names>A</given-names></name>
<name><surname>de The</surname> <given-names>H</given-names></name>
</person-group>. 
<article-title>Conventional chemotherapy: millions of cures, unresolved therapeutic index</article-title>. <source>Nat Rev Cancer</source>. (<year>2025</year>) <volume>25</volume>:<page-range>209&#x2013;18</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1038/s41568-024-00778-4</pub-id>, PMID: <pub-id pub-id-type="pmid">39681637</pub-id>
</mixed-citation>
</ref>
<ref id="B3">
<label>3</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Tetterton-Kellner</surname> <given-names>J</given-names></name>
<name><surname>Jensen</surname> <given-names>BC</given-names></name>
<name><surname>Nguyen</surname> <given-names>J</given-names></name>
</person-group>. 
<article-title>Navigating cancer therapy induced cardiotoxicity: From pathophysiology to treatment innovations</article-title>. <source>Adv Drug Delivery Rev</source>. (<year>2024</year>) <volume>211</volume>:<elocation-id>115361</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.addr.2024.115361</pub-id>, PMID: <pub-id pub-id-type="pmid">38901637</pub-id>
</mixed-citation>
</ref>
<ref id="B4">
<label>4</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Tamai</surname> <given-names>H</given-names></name>
<name><surname>Ikeda</surname> <given-names>K</given-names></name>
<name><surname>Miyamoto</surname> <given-names>T</given-names></name>
<name><surname>Taguchi</surname> <given-names>H</given-names></name>
<name><surname>Kuo</surname> <given-names>C-F</given-names></name>
<name><surname>Shin</surname> <given-names>K</given-names></name>
<etal/>
</person-group>. 
<article-title>Association of methotrexate polyglutamates concentration with methotrexate efficacy and safety in patients with rheumatoid arthritis treated with predefined dose: results from the MIRACLE trial</article-title>. <source>Ann Rheum Dis</source>. (<year>2025</year>) <volume>84</volume>:<page-range>41&#x2013;8</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1136/ard-2024-226350</pub-id>, PMID: <pub-id pub-id-type="pmid">39874232</pub-id>
</mixed-citation>
</ref>
<ref id="B5">
<label>5</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Hountondji</surname> <given-names>L</given-names></name>
<name><surname>Faure</surname> <given-names>S</given-names></name>
<name><surname>Palassin</surname> <given-names>P</given-names></name>
<name><surname>Pageaux</surname> <given-names>G-P</given-names></name>
<name><surname>Maria</surname> <given-names>ATJ</given-names></name>
<name><surname>Meunier</surname> <given-names>L</given-names></name>
</person-group>. 
<article-title>Ursodeoxycholic acid alone is effective and safe to treat cholestatic checkpoint inhibitor-induced liver injury</article-title>. <source>Liver Int</source>. (<year>2025</year>) <volume>45</volume>:<fpage>e70073</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/liv.70073</pub-id>, PMID: <pub-id pub-id-type="pmid">40198079</pub-id>
</mixed-citation>
</ref>
<ref id="B6">
<label>6</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Li</surname> <given-names>J</given-names></name>
<name><surname>Lian</surname> <given-names>X</given-names></name>
<name><surname>Li</surname> <given-names>B</given-names></name>
<name><surname>Ma</surname> <given-names>Q</given-names></name>
<name><surname>Yang</surname> <given-names>L</given-names></name>
<name><surname>Gao</surname> <given-names>G</given-names></name>
<etal/>
</person-group>. 
<article-title>Pharmacodynamic material basis of licorice and mechanisms of modulating bile acid metabolism and gut microbiota in cisplatin-induced liver injury based on LC-MS and network pharmacology analysis</article-title>. <source>J Ethnopharmacol</source>. (<year>2025</year>) <volume>340</volume>:<elocation-id>119293</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.jep.2024.119293</pub-id>, PMID: <pub-id pub-id-type="pmid">39736346</pub-id>
</mixed-citation>
</ref>
<ref id="B7">
<label>7</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Fan</surname> <given-names>M</given-names></name>
<name><surname>Xu</surname> <given-names>Y</given-names></name>
<name><surname>Wu</surname> <given-names>B</given-names></name>
<name><surname>Long</surname> <given-names>J</given-names></name>
<name><surname>Liu</surname> <given-names>C</given-names></name>
<name><surname>Liang</surname> <given-names>Z</given-names></name>
<etal/>
</person-group>. 
<article-title>Geniposidic acid targeting FXR "S332 and H447" Mediated conformational change to upregulate CYPs and miR-19a-3p to ameliorate drug-induced liver injury</article-title>. <source>Adv Sci (Weinh)</source>. (<year>2025</year>) <volume>12</volume>:<fpage>e2409107</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1002/advs.202409107</pub-id>, PMID: <pub-id pub-id-type="pmid">39998442</pub-id>
</mixed-citation>
</ref>
<ref id="B8">
<label>8</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Danan</surname> <given-names>G</given-names></name>
<name><surname>Teschke</surname> <given-names>R</given-names></name>
</person-group>. 
<article-title>RUCAM in drug and herb induced liver injury: the update</article-title>. <source>Int J Mol Sci</source>. (<year>2016</year>) <volume>17</volume>:<fpage>14</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/ijms17010014</pub-id>, PMID: <pub-id pub-id-type="pmid">26712744</pub-id>
</mixed-citation>
</ref>
<ref id="B9">
<label>9</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Moreno-Torres</surname> <given-names>M</given-names></name>
<name><surname>Quint&#xe1;s</surname> <given-names>G</given-names></name>
<name><surname>Castell</surname> <given-names>JV</given-names></name>
</person-group>. 
<article-title>The potential role of metabolomics in drug-induced liver injury (DILI) assessment</article-title>. <source>Metabolites</source>. (<year>2022</year>) <volume>12</volume>:<fpage>564</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/metabo12060564</pub-id>, PMID: <pub-id pub-id-type="pmid">35736496</pub-id>
</mixed-citation>
</ref>
<ref id="B10">
<label>10</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Teschke</surname> <given-names>R</given-names></name>
<name><surname>Schulze</surname> <given-names>J</given-names></name>
<name><surname>Eickhoff</surname> <given-names>A</given-names></name>
<name><surname>Danan</surname> <given-names>G</given-names></name>
</person-group>. 
<article-title>Drug induced liver injury: can biomarkers assist RUCAM in causality assessment</article-title>? <source>Int J Mol Sci</source>. (<year>2017</year>) <volume>18</volume>:<fpage>803</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/ijms18040803</pub-id>, PMID: <pub-id pub-id-type="pmid">28398242</pub-id>
</mixed-citation>
</ref>
<ref id="B11">
<label>11</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Danan</surname> <given-names>G</given-names></name>
<name><surname>Teschke</surname> <given-names>R</given-names></name>
</person-group>. 
<article-title>Drug-induced liver injury: why is the roussel uclaf causality assessment method (RUCAM) still used 25 years after its launch</article-title>? <source>Drug Saf</source>. (<year>2018</year>) <volume>41</volume>:<page-range>735&#x2013;43</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1007/s40264-018-0654-2</pub-id>, PMID: <pub-id pub-id-type="pmid">29502198</pub-id>
</mixed-citation>
</ref>
<ref id="B12">
<label>12</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Conway</surname> <given-names>R</given-names></name>
<name><surname>Low</surname> <given-names>C</given-names></name>
<name><surname>Coughlan</surname> <given-names>RJ</given-names></name>
<name><surname>O'Donnell</surname> <given-names>MJ</given-names></name>
<name><surname>Carey</surname> <given-names>JJ</given-names></name>
</person-group>. 
<article-title>Risk of liver injury among methotrexate users: A meta-analysis of randomised controlled trials</article-title>. <source>Semin Arthritis Rheum</source>. (<year>2015</year>) <volume>45</volume>:<page-range>156&#x2013;62</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.semarthrit.2015.05.003</pub-id>, PMID: <pub-id pub-id-type="pmid">26088004</pub-id>
</mixed-citation>
</ref>
<ref id="B13">
<label>13</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Gisondi</surname> <given-names>P</given-names></name>
<name><surname>Bellinato</surname> <given-names>F</given-names></name>
<name><surname>Bruni</surname> <given-names>M</given-names></name>
<name><surname>De Angelis</surname> <given-names>G</given-names></name>
<name><surname>Girolomoni</surname> <given-names>G</given-names></name>
</person-group>. 
<article-title>Methotrexate vs secukinumab safety in psoriasis patients with metabolic syndrome</article-title>. <source>Dermatol Ther</source>. (<year>2020</year>) <volume>33</volume>:<fpage>e14281</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/dth.14281</pub-id>, PMID: <pub-id pub-id-type="pmid">32888370</pub-id>
</mixed-citation>
</ref>
<ref id="B14">
<label>14</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Sakaeda</surname> <given-names>T</given-names></name>
<name><surname>Tamon</surname> <given-names>A</given-names></name>
<name><surname>Kadoyama</surname> <given-names>K</given-names></name>
<name><surname>Okuno</surname> <given-names>Y</given-names></name>
</person-group>. 
<article-title>Data mining of the public version of the FDA Adverse Event Reporting System</article-title>. <source>Int J Med Sci</source>. (<year>2013</year>) <volume>10</volume>:<fpage>796</fpage>&#x2013;<lpage>803</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.7150/ijms.6048</pub-id>, PMID: <pub-id pub-id-type="pmid">23794943</pub-id>
</mixed-citation>
</ref>
<ref id="B15">
<label>15</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Wilson</surname> <given-names>LAB</given-names></name>
<name><surname>Zajitschek</surname> <given-names>SRK</given-names></name>
<name><surname>Lagisz</surname> <given-names>M</given-names></name>
<name><surname>Mason</surname> <given-names>J</given-names></name>
<name><surname>Haselimashhadi</surname> <given-names>H</given-names></name>
<name><surname>Nakagawa</surname> <given-names>S</given-names></name>
</person-group>. 
<article-title>Sex differences in allometry for phenotypic traits in mice indicate that females are not scaled males</article-title>. <source>Nat Commun</source>. (<year>2022</year>) <volume>13</volume>:<fpage>7502</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1038/s41467-022-35266-6</pub-id>, PMID: <pub-id pub-id-type="pmid">36509767</pub-id>
</mixed-citation>
</ref>
<ref id="B16">
<label>16</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Bj&#xf6;rnsson</surname> <given-names>ES</given-names></name>
</person-group>. 
<article-title>Epidemiology, predisposing factors, and outcomes of drug-induced liver injury</article-title>. <source>Clin Liver Dis</source>. (<year>2020</year>) <volume>24</volume>:<page-range>1&#x2013;10</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.cld.2019.08.002</pub-id>, PMID: <pub-id pub-id-type="pmid">31753242</pub-id>
</mixed-citation>
</ref>
<ref id="B17">
<label>17</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Yu</surname> <given-names>S</given-names></name>
<name><surname>Li</surname> <given-names>J</given-names></name>
<name><surname>He</surname> <given-names>T</given-names></name>
<name><surname>Zheng</surname> <given-names>H</given-names></name>
<name><surname>Wang</surname> <given-names>S</given-names></name>
<name><surname>Sun</surname> <given-names>Y</given-names></name>
<etal/>
</person-group>. 
<article-title>Age-related differences in drug-induced liver injury: a retrospective single-center study from a large liver disease specialty hospital in China, 2002-2022</article-title>. <source>Hepatol Int</source>. (<year>2024</year>) <volume>18</volume>:<page-range>1202&#x2013;13</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1007/s12072-024-10679-1</pub-id>, PMID: <pub-id pub-id-type="pmid">38898191</pub-id>
</mixed-citation>
</ref>
<ref id="B18">
<label>18</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Villanueva-Paz</surname> <given-names>M</given-names></name>
<name><surname>Mor&#xe1;n</surname> <given-names>L</given-names></name>
<name><surname>L&#xf3;pez-Alc&#xe1;ntara</surname> <given-names>N</given-names></name>
<name><surname>Freixo</surname> <given-names>C</given-names></name>
<name><surname>Andrade</surname> <given-names>RJ</given-names></name>
<name><surname>Lucena</surname> <given-names>MI</given-names></name>
<etal/>
</person-group>. 
<article-title>Oxidative stress in drug-induced liver injury (DILI): from mechanisms to biomarkers for use in clinical practice</article-title>. <source>Antioxidants (Basel)</source>. (<year>2021</year>) <volume>10</volume>:<fpage>390</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/antiox10030390</pub-id>, PMID: <pub-id pub-id-type="pmid">33807700</pub-id>
</mixed-citation>
</ref>
<ref id="B19">
<label>19</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Oosterom</surname> <given-names>N</given-names></name>
<name><surname>Gooskens</surname> <given-names>SLM</given-names></name>
<name><surname>Renfro</surname> <given-names>LA</given-names></name>
<name><surname>Perlman</surname> <given-names>EJ</given-names></name>
<name><surname>van den Heuvel-Eibrink</surname> <given-names>MM</given-names></name>
<name><surname>Hamilton</surname> <given-names>TE</given-names></name>
<etal/>
</person-group>. 
<article-title>Severe hepatopathy in national wilms tumor studies 3-5: prevalence, clinical features, and outcomes after reintroduction of chemotherapy</article-title>. <source>J Clin Oncol</source>. (<year>2023</year>) <volume>41</volume>:<page-range>4247&#x2013;56</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1200/JCO.22.02555</pub-id>, PMID: <pub-id pub-id-type="pmid">37343199</pub-id>
</mixed-citation>
</ref>
<ref id="B20">
<label>20</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Kamal</surname> <given-names>N</given-names></name>
<name><surname>Koh</surname> <given-names>C</given-names></name>
<name><surname>Samala</surname> <given-names>N</given-names></name>
<name><surname>Fontana</surname> <given-names>RJ</given-names></name>
<name><surname>Stolz</surname> <given-names>A</given-names></name>
<name><surname>Durazo</surname> <given-names>F</given-names></name>
<etal/>
</person-group>. 
<article-title>Asparaginase-induced hepatotoxicity: rapid development of cholestasis and hepatic steatosis</article-title>. <source>Hepatol Int</source>. (<year>2019</year>) <volume>13</volume>:<page-range>641&#x2013;8</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1007/s12072-019-09971-2</pub-id>, PMID: <pub-id pub-id-type="pmid">31392570</pub-id>
</mixed-citation>
</ref>
<ref id="B21">
<label>21</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Tinajero</surname> <given-names>J</given-names></name>
<name><surname>Xu</surname> <given-names>S</given-names></name>
<name><surname>Ngo</surname> <given-names>D</given-names></name>
<name><surname>Li</surname> <given-names>S</given-names></name>
<name><surname>Palmer</surname> <given-names>J</given-names></name>
<name><surname>Nguyen</surname> <given-names>T</given-names></name>
<etal/>
</person-group>. 
<article-title>Delaying pegaspargase during induction in adults with acute lymphoblastic leukaemia is associated with lower risk of high-grade hepatotoxicity without adversely impacting outcomes</article-title>. <source>Br J Haematol</source>. (<year>2025</year>) <volume>206</volume>:<page-range>868&#x2013;75</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/bjh.19880</pub-id>, PMID: <pub-id pub-id-type="pmid">39505575</pub-id>
</mixed-citation>
</ref>
<ref id="B22">
<label>22</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Schulte</surname> <given-names>R</given-names></name>
<name><surname>Hinson</surname> <given-names>A</given-names></name>
<name><surname>Huynh</surname> <given-names>V</given-names></name>
<name><surname>Breese</surname> <given-names>EH</given-names></name>
<name><surname>Pierro</surname> <given-names>J</given-names></name>
<name><surname>Rotz</surname> <given-names>S</given-names></name>
<etal/>
</person-group>. 
<article-title>Levocarnitine for pegaspargase-induced hepatotoxicity in older children and young adults with acute lymphoblastic leukemia</article-title>. <source>Cancer Med</source>. (<year>2021</year>) <volume>10</volume>:<page-range>7551&#x2013;60</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1002/cam4.4281</pub-id>, PMID: <pub-id pub-id-type="pmid">34528411</pub-id>
</mixed-citation>
</ref>
<ref id="B23">
<label>23</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Douer</surname> <given-names>D</given-names></name>
<name><surname>Aldoss</surname> <given-names>I</given-names></name>
<name><surname>Lunning</surname> <given-names>MA</given-names></name>
<name><surname>Burke</surname> <given-names>PW</given-names></name>
<name><surname>Ramezani</surname> <given-names>L</given-names></name>
<name><surname>Mark</surname> <given-names>L</given-names></name>
<etal/>
</person-group>. 
<article-title>Pharmacokinetics-based integration of multiple doses of intravenous pegaspargase in a pediatric regimen for adults with newly diagnosed acute lymphoblastic leukemia</article-title>. <source>J Clin Oncol</source>. (<year>2014</year>) <volume>32</volume>:<page-range>905&#x2013;11</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1200/JCO.2013.50.2708</pub-id>, PMID: <pub-id pub-id-type="pmid">24516026</pub-id>
</mixed-citation>
</ref>
<ref id="B24">
<label>24</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Saran</surname> <given-names>C</given-names></name>
<name><surname>Sundqvist</surname> <given-names>L</given-names></name>
<name><surname>Ho</surname> <given-names>H</given-names></name>
<name><surname>Niskanen</surname> <given-names>J</given-names></name>
<name><surname>Honkakoski</surname> <given-names>P</given-names></name>
<name><surname>Brouwer</surname> <given-names>KLR</given-names></name>
</person-group>. 
<article-title>Novel bile acid-dependent mechanisms of hepatotoxicity associated with tyrosine kinase inhibitors</article-title>. <source>J Pharmacol Exp Ther</source>. (<year>2022</year>) <volume>380</volume>:<page-range>114&#x2013;25</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1124/jpet.121.000828</pub-id>, PMID: <pub-id pub-id-type="pmid">34794962</pub-id>
</mixed-citation>
</ref>
<ref id="B25">
<label>25</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Klempner</surname> <given-names>SJ</given-names></name>
<name><surname>Choueiri</surname> <given-names>TK</given-names></name>
<name><surname>Yee</surname> <given-names>E</given-names></name>
<name><surname>Doyle</surname> <given-names>LA</given-names></name>
<name><surname>Schuppan</surname> <given-names>D</given-names></name>
<name><surname>Atkins</surname> <given-names>MB</given-names></name>
</person-group>. 
<article-title>Severe pazopanib-induced hepatotoxicity: clinical and histologic course in two patients</article-title>. <source>J Clin Oncol</source>. (<year>2012</year>) <volume>30</volume>:<page-range>e264&#x2013;8</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1200/JCO.2011.41.0332</pub-id>, PMID: <pub-id pub-id-type="pmid">22802316</pub-id>
</mixed-citation>
</ref>
<ref id="B26">
<label>26</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Zhao</surname> <given-names>Q</given-names></name>
<name><surname>Lu</surname> <given-names>Y</given-names></name>
<name><surname>Duan</surname> <given-names>J</given-names></name>
<name><surname>Du</surname> <given-names>D</given-names></name>
<name><surname>Pu</surname> <given-names>Q</given-names></name>
<name><surname>Li</surname> <given-names>F</given-names></name>
</person-group>. 
<article-title>Gut microbiota depletion and FXR inhibition exacerbates zonal hepatotoxicity of sunitinib</article-title>. <source>Theranostics</source>. (<year>2024</year>) <volume>14</volume>:<page-range>7219&#x2013;40</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.7150/thno.99926</pub-id>, PMID: <pub-id pub-id-type="pmid">39629129</pub-id>
</mixed-citation>
</ref>
<ref id="B27">
<label>27</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Bud&#x103;u</surname> <given-names>LV</given-names></name>
<name><surname>Pop</surname> <given-names>C</given-names></name>
<name><surname>Mogo&#x15f;an</surname> <given-names>C</given-names></name>
</person-group>. 
<article-title>Beyond the basics: exploring pharmacokinetic interactions and safety in tyrosine-kinase inhibitor oral therapy for solid tumors</article-title>. <source>Pharm (Basel)</source>. (<year>2025</year>) <volume>18</volume>:<fpage>959</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/ph18070959</pub-id>, PMID: <pub-id pub-id-type="pmid">40732249</pub-id>
</mixed-citation>
</ref>
<ref id="B28">
<label>28</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Cunningham</surname> <given-names>M</given-names></name>
<name><surname>Gupta</surname> <given-names>R</given-names></name>
<name><surname>Butler</surname> <given-names>M</given-names></name>
</person-group>. 
<article-title>Checkpoint inhibitor hepatotoxicity: pathogenesis and management</article-title>. <source>Hepatology</source>. (<year>2024</year>) <volume>79</volume>:<fpage>198</fpage>&#x2013;<lpage>212</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1097/hep.0000000000000045</pub-id>, PMID: <pub-id pub-id-type="pmid">36633259</pub-id>
</mixed-citation>
</ref>
<ref id="B29">
<label>29</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Peeraphatdit</surname> <given-names>TB</given-names></name>
<name><surname>Wang</surname> <given-names>J</given-names></name>
<name><surname>Odenwald</surname> <given-names>MA</given-names></name>
<name><surname>Hu</surname> <given-names>S</given-names></name>
<name><surname>Hart</surname> <given-names>J</given-names></name>
<name><surname>Charlton</surname> <given-names>MR</given-names></name>
</person-group>. 
<article-title>Hepatotoxicity from immune checkpoint inhibitors: A systematic review and management recommendation</article-title>. <source>Hepatology</source>. (<year>2020</year>) <volume>72</volume>:<page-range>315&#x2013;29</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1002/hep.31227</pub-id>, PMID: <pub-id pub-id-type="pmid">32167613</pub-id>
</mixed-citation>
</ref>
<ref id="B30">
<label>30</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Scott</surname> <given-names>J</given-names></name>
<name><surname>Summerfield</surname> <given-names>JA</given-names></name>
<name><surname>Elias</surname> <given-names>E</given-names></name>
<name><surname>Dick</surname> <given-names>R</given-names></name>
<name><surname>Sherlock</surname> <given-names>S</given-names></name>
</person-group>. 
<article-title>Chronic pancreatitis: a cause of cholestasis</article-title>. <source>Gut</source>. (<year>1977</year>) <volume>18</volume>:<fpage>196</fpage>&#x2013;<lpage>201</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1136/gut.18.3.196</pub-id>, PMID: <pub-id pub-id-type="pmid">856677</pub-id>
</mixed-citation>
</ref>
<ref id="B31">
<label>31</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Teschke</surname> <given-names>R</given-names></name>
<name><surname>Eickhoff</surname> <given-names>A</given-names></name>
<name><surname>Danan</surname> <given-names>G</given-names></name>
</person-group>. 
<article-title>Drug-induced autoimmune hepatitis: robust causality assessment using two different validated and scoring diagnostic algorithms</article-title>. <source>Diagnostics (Basel)</source>. (<year>2025</year>) <volume>15</volume>:<fpage>1588</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/diagnostics15131588</pub-id>, PMID: <pub-id pub-id-type="pmid">40647587</pub-id>
</mixed-citation>
</ref>
<ref id="B32">
<label>32</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Johncilla</surname> <given-names>M</given-names></name>
<name><surname>Misdraji</surname> <given-names>J</given-names></name>
<name><surname>Pratt</surname> <given-names>DS</given-names></name>
<name><surname>Agoston</surname> <given-names>AT</given-names></name>
<name><surname>Lauwers</surname> <given-names>GY</given-names></name>
<name><surname>Srivastava</surname> <given-names>A</given-names></name>
<etal/>
</person-group>. 
<article-title>Ipilimumab-associated hepatitis: clinicopathologic characterization in a series of 11 cases</article-title>. <source>Am J Surg Pathol</source>. (<year>2015</year>) <volume>39</volume>:<page-range>1075&#x2013;84</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1097/pas.0000000000000453</pub-id>, PMID: <pub-id pub-id-type="pmid">26034866</pub-id>
</mixed-citation>
</ref>
<ref id="B33">
<label>33</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Shojaie</surname> <given-names>L</given-names></name>
<name><surname>Bogdanov</surname> <given-names>JM</given-names></name>
<name><surname>Alavifard</surname> <given-names>H</given-names></name>
<name><surname>Mohamed</surname> <given-names>MG</given-names></name>
<name><surname>Baktash</surname> <given-names>A</given-names></name>
<name><surname>Ali</surname> <given-names>M</given-names></name>
<etal/>
</person-group>. 
<article-title>Innate and adaptive immune cell interaction drives inflammasome activation and hepatocyte apoptosis in murine liver injury from immune checkpoint inhibitors</article-title>. <source>Cell Death Dis</source>. (<year>2024</year>) <volume>15</volume>:<fpage>140</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1038/s41419-024-06535-7</pub-id>, PMID: <pub-id pub-id-type="pmid">38355725</pub-id>
</mixed-citation>
</ref>
<ref id="B34">
<label>34</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Gharia</surname> <given-names>B</given-names></name>
<name><surname>Seegobin</surname> <given-names>K</given-names></name>
<name><surname>Maharaj</surname> <given-names>S</given-names></name>
<name><surname>Marji</surname> <given-names>N</given-names></name>
<name><surname>Deutch</surname> <given-names>A</given-names></name>
<name><surname>Zuberi</surname> <given-names>L</given-names></name>
</person-group>. 
<article-title>Letrozole-induced hepatitis with autoimmune features: a rare adverse drug reaction with review of the relevant literature</article-title>. <source>Oxford Med Case Rep</source>. (<year>2017</year>) <volume>2017</volume>:<elocation-id>omx074</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.1093/omcr/omx074</pub-id>, PMID: <pub-id pub-id-type="pmid">29230302</pub-id>
</mixed-citation>
</ref>
<ref id="B35">
<label>35</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Moy</surname> <given-names>B</given-names></name>
<name><surname>Neven</surname> <given-names>P</given-names></name>
<name><surname>Lebrun</surname> <given-names>F</given-names></name>
<name><surname>Bellet</surname> <given-names>M</given-names></name>
<name><surname>Xu</surname> <given-names>B</given-names></name>
<name><surname>Sarosiek</surname> <given-names>T</given-names></name>
<etal/>
</person-group>. 
<article-title>Bosutinib in combination with the aromatase inhibitor letrozole: a phase II trial in postmenopausal women evaluating first-line endocrine therapy in locally advanced or metastatic hormone receptor-positive/HER2-negative breast cancer</article-title>. <source>Oncologist</source>. (<year>2014</year>) <volume>19</volume>:<page-range>348&#x2013;9</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1634/theoncologist.2014-0021</pub-id>, PMID: <pub-id pub-id-type="pmid">24674874</pub-id>
</mixed-citation>
</ref>
<ref id="B36">
<label>36</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Hansson</surname> <given-names>P</given-names></name>
<name><surname>Blacker</surname> <given-names>C</given-names></name>
<name><surname>Uvdal</surname> <given-names>H</given-names></name>
<name><surname>Wadelius</surname> <given-names>M</given-names></name>
<name><surname>Green</surname> <given-names>H</given-names></name>
<name><surname>Ljungman</surname> <given-names>G</given-names></name>
</person-group>. 
<article-title>Pharmacogenomics in pediatric oncology patients with solid tumors related to chemotherapy-induced toxicity: A systematic review</article-title>. <source>Crit Rev In Oncology/hematology</source>. (<year>2025</year>) <volume>211</volume>:<elocation-id>104720</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.critrevonc.2025.104720</pub-id>, PMID: <pub-id pub-id-type="pmid">40222694</pub-id>
</mixed-citation>
</ref>
<ref id="B37">
<label>37</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Wang</surname> <given-names>HF</given-names></name>
<name><surname>He</surname> <given-names>YQ</given-names></name>
<name><surname>Ke</surname> <given-names>Z</given-names></name>
<name><surname>Liang</surname> <given-names>ZW</given-names></name>
<name><surname>Zhou</surname> <given-names>JH</given-names></name>
<name><surname>Ni</surname> <given-names>K</given-names></name>
<etal/>
</person-group>. 
<article-title>STING signaling contributes to methotrexate-induced liver injury by regulating ferroptosis in mice</article-title>. <source>Ecotoxicol Environ Saf</source>. (<year>2024</year>) <volume>287</volume>:<elocation-id>117306</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.ecoenv.2024.117306</pub-id>, PMID: <pub-id pub-id-type="pmid">39547058</pub-id>
</mixed-citation>
</ref>
<ref id="B38">
<label>38</label>
<mixed-citation publication-type="journal">
<person-group person-group-type="author">
<name><surname>Teschke</surname> <given-names>R</given-names></name>
<name><surname>Danan</surname> <given-names>G</given-names></name>
</person-group>. 
<article-title>Idiosyncratic drug-induced liver injury (DILI) and herb-induced liver injury (HILI): diagnostic algorithm based on the quantitative roussel uclaf causality assessment method (RUCAM)</article-title>. <source>Diagnostics (Basel)</source>. (<year>2021</year>) <volume>11</volume>:<fpage>458</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/diagnostics11030458</pub-id>, PMID: <pub-id pub-id-type="pmid">33800917</pub-id>
</mixed-citation>
</ref>
</ref-list>
<fn-group>
<fn id="n1" fn-type="custom" custom-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/196659">Rolf Teschke</ext-link>, Hospital Hanau, Germany</p></fn>
<fn id="n2" fn-type="custom" custom-type="reviewed-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1707969">Osmar Antonio Jaramillo-Morales</ext-link>, University of Guanajuato, Mexico</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3012035">Mahmoud Abdelrahman Alkabbani</ext-link>, Egyptian Russian University, Egypt</p></fn>
</fn-group>
</back>
</article>