{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T19:33:11Z","timestamp":1792956791467,"version":"4.5.0"},"reference-count":70,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive\/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.<\/jats:p>","DOI":"10.3390\/biom14050535","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T08:59:39Z","timestamp":1714553979000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuki","family":"Umemori","sequence":"first","affiliation":[{"name":"DMPK Research Department, Teijin Institute for Bio-Medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2748-9742","authenticated-orcid":false,"given":"Koichi","family":"Handa","sequence":"additional","affiliation":[{"name":"DMPK Research Department, Teijin Institute for Bio-Medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saki","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"DMPK Research Department, Teijin Institute for Bio-Medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michiharu","family":"Kageyama","sequence":"additional","affiliation":[{"name":"DMPK Research Department, Teijin Institute for Bio-Medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Iijima","sequence":"additional","affiliation":[{"name":"DMPK Research Department, Teijin Institute for Bio-Medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharm. Sin. B"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1016\/j.drudis.2013.12.008","article-title":"Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how","volume":"19","author":"Hornberg","year":"2014","journal-title":"Drug Discov. Today"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1002\/cpt.564","article-title":"Drug-induced liver injury: Advances in mechanistic understanding that will inform risk management","volume":"101","author":"Mosedale","year":"2017","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1111\/j.1365-2125.2004.02133.x","article-title":"Acute and clinically relevant drug-induced liver injury: A population based case-control study","volume":"58","author":"Montero","year":"2004","journal-title":"Br. J. Clin. Pharmacol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1053\/j.gastro.2019.02.002","article-title":"Incidence and etiology of drug-induced liver injury in Mainland China","volume":"156","author":"Shen","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.cld.2013.07.001","article-title":"Drug-induced acute liver failure","volume":"17","author":"Lee","year":"2013","journal-title":"Clin. Liver Dis."},{"key":"ref_7","first-page":"957","article-title":"Postmarketing surveillance and black box warnings","volume":"288","author":"Greene","year":"2002","journal-title":"JAMA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s40264-018-0642-6","article-title":"Liver safety of Fasiglifam (TAK-875) in patients with Type 2 diabetes: Review of the global clinical trial experience","volume":"41","author":"Marcinak","year":"2018","journal-title":"Drug Saf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1517\/17425255.2015.985649","article-title":"Current understanding of the mechanisms of idiosyncratic drug-induced agranulocytosis","volume":"11","author":"Johnston","year":"2015","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1124\/dmd.111.040758","article-title":"In vitro inhibition of the bile salt export pump correlates with risk of cholestatic drug-induced liver injury in humans","volume":"40","author":"Dawson","year":"2012","journal-title":"Drug Metab. Dispos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1093\/toxsci\/kft176","article-title":"A multifactorial approach to hepatobiliary transporter assessment enables improved therapeutic compound development","volume":"136","author":"Morgan","year":"2013","journal-title":"Toxicol. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.3748\/wjg.v24.i13.1373","article-title":"Dissecting the molecular pathophysiology of drug-induced liver injury","volume":"24","author":"Ye","year":"2018","journal-title":"World J. Gastroenterol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1021\/tx800161s","article-title":"Can in vitro metabolism-dependent covalent binding data in liver microsomes distinguish hepatotoxic from nonhepatotoxic drugs? An analysis of 18 drugs with consideration of intrinsic clearance and daily dose","volume":"21","author":"Obach","year":"2008","journal-title":"Chem. Res. Toxicol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1124\/dmd.109.028860","article-title":"Evaluation of the potential for drug-induced liver injury based on in vitro covalent binding to human liver proteins","volume":"37","author":"Usui","year":"2009","journal-title":"Drug Metab. Dispos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1124\/dmd.109.027797","article-title":"A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding","volume":"37","author":"Nakayama","year":"2009","journal-title":"Drug Metab. Dispos."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1021\/tx300091x","article-title":"In vitro approach to assess the potential for risk of idiosyncratic adverse reactions caused by candidate drugs","volume":"25","author":"Thompson","year":"2012","journal-title":"Chem. Res. Toxicol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1021\/tx0496791","article-title":"Dansyl glutathione as a trapping agent for the quantitative estimation and identification of reactive metabolites","volume":"18","author":"Gan","year":"2005","journal-title":"Chem. Res. Toxicol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2133\/dmpk.24.245","article-title":"A trapping method for semi-quantitative assessment of reactive metabolite formation using [35S]cysteine and [14C]cyanide","volume":"24","author":"Inoue","year":"2009","journal-title":"Drug Metab. Pharmacokinet."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"201","DOI":"10.2131\/jts.44.201","article-title":"Novel risk assessment of reactive metabolites from discovery to clinical stage","volume":"44","author":"Kakutani","year":"2019","journal-title":"J. Toxicol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.bbrc.2011.11.032","article-title":"Bile salt export pump inhibitors are associated with bile acid-dependent drug-induced toxicity in sandwich-cultured hepatocytes","volume":"416","author":"Ogimura","year":"2011","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_21","unstructured":"(2023, July 12). DILIsym\u00ae. Available online: https:\/\/www.simulations-plus.com\/software\/dilisym\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1124\/jpet.112.192930","article-title":"An analysis of N-acetylCysteine treatment for acetaminophen overdose using a systems model of drug-induced liver injury","volume":"342","author":"Woodhead","year":"2012","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1093\/toxsci\/kfaa093","article-title":"Mechanistic investigations support liver safety of ubrogepant","volume":"177","author":"Smith","year":"2020","journal-title":"Toxicol. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1093\/toxsci\/kfaa019","article-title":"Assessing Effects of BHV-0223 40 mg Zydis sublingual Formulation and riluzole 50 mg Oral Tablet on liver function test Parameters Utilizing DILIsym","volume":"175","author":"Longo","year":"2020","journal-title":"Toxicol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/cpt.2007","article-title":"Quantitative systems toxicology modeling predicts that reduced biliary efflux contributes to tolvaptan hepatotoxicity","volume":"109","author":"Beaudoin","year":"2021","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1093\/toxsci\/kft189","article-title":"Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs","volume":"136","author":"Chen","year":"2013","journal-title":"Toxicol. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","article-title":"Deep learning for drug-induced liver injury","volume":"55","author":"Xu","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"25432","DOI":"10.1021\/acsomega.0c03866","article-title":"Predicting drug-induced liver injury using convolutional neural network and molecular fingerprint-embedded features","volume":"5","author":"Nguyen","year":"2020","journal-title":"ACS Omega"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, T., Tong, W., Roberts, R., Liu, Z., and Thakkar, S. (2020). Deep learning on high-throughput transcriptomics to predict drug-induced liver injury. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.562677"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, A., Walter, M., Wright, P., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., and Bender, A. (2021). Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol. Direct, 16.","DOI":"10.1186\/s13062-020-00285-0"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, X., Zheng, D., Zhong, Y., Xia, Z., Luo, H., and Weng, Z. (2020). Machine-learning prediction of oral drug-induced liver injury (DILI) via multiple features and endpoints. BioMed Res. Int., 2020.","DOI":"10.1155\/2020\/4795140"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1002\/jat.2879","article-title":"Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information","volume":"34","author":"Zhu","year":"2014","journal-title":"J. Appl. Toxicol."},{"key":"ref_33","unstructured":"(2023, July 12). LTKB Benchmark Dataset, Available online: https:\/\/www.fda.gov\/science-research\/liver-toxicity-knowledge-base-ltkb\/ltkb-benchmark-dataset."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"s104","DOI":"10.7861\/clinmedicine.16-6-s104","article-title":"Drug-induced liver injury","volume":"16","author":"Katarey","year":"2016","journal-title":"Clin. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"153736","DOI":"10.1016\/j.tox.2024.153736","article-title":"Deep learning algorithm based on molecular fingerprint for prediction of drug-induced liver injury","volume":"502","author":"Yang","year":"2024","journal-title":"Toxicology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e2200284","DOI":"10.1002\/minf.202200284","article-title":"In silico prediction of drug-induced liver injury with a complementary integration strategy based on hybrid representation","volume":"42","author":"Gu","year":"2023","journal-title":"Mol. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4602","DOI":"10.1021\/acs.jcim.3c00687","article-title":"Machine learning-based prediction of drug-induced hepatotoxicity: An OvA-QSTR approach","volume":"63","author":"Karaduman","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1021\/acs.chemrestox.5b00017","article-title":"Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione","volume":"28","author":"Hughes","year":"2015","journal-title":"Chem. Res. Toxicol."},{"key":"ref_39","unstructured":"(2023, July 04). CHEMBL. Available online: https:\/\/www.ebi.ac.uk\/chembl\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.ddtec.2004.11.007","article-title":"Lead- and drug-like compounds: The rule-of-five revolution","volume":"1","author":"Lipinski","year":"2004","journal-title":"Drug Discov. Today Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1023\/A:1015040217741","article-title":"Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs","volume":"16","author":"Kelder","year":"1999","journal-title":"Pharm. Res."},{"key":"ref_42","unstructured":"(2024, March 22). Maestro. Life Science: Maestro-Schr\u00f6dinger. Available online: https:\/\/www.schrodinger.com\/platform\/products\/maestro\/."},{"key":"ref_43","unstructured":"(2024, March 06). Insight for Excel. Available online: https:\/\/www.3ds.com\/products\/biovia\/insight\/insight-excel."},{"key":"ref_44","unstructured":"(2023, October 04). Datawarrior. Available online: www.openmolecules.org\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"ref_46","unstructured":"(2022, March 25). RD-kit. Available online: https:\/\/www.rdkit.org\/docs\/index.html#."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s13321-016-0148-0","article-title":"Comparing structural fingerprints using a literature-based similarity benchmark","volume":"8","author":"Sayle","year":"2016","journal-title":"J. Cheminform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1021\/acs.jcim.7b00087","article-title":"Demystifying multitask deep neural networks for quantitative structure-activity relationships","volume":"57","author":"Xu","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1039\/D3DD00144J","article-title":"Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage","volume":"2","author":"Handa","year":"2023","journal-title":"Digit. Discov."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1016\/j.drudis.2020.07.001","article-title":"Bayer\u2019s in silico ADMET platform: A journey of machine learning over the past two decades","volume":"25","author":"Kuhnke","year":"2020","journal-title":"Drug Discov. Today"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13321-020-00479-8","article-title":"Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1021\/acsomega.2c05607","article-title":"Machine learning for fast, quantum mechanics-based approximation of drug lipophilicity","volume":"8","author":"Isert","year":"2023","journal-title":"ACS Omega"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1021\/acs.jcim.3c01250","article-title":"Chemprop: A Machine Learning Package for Chemical Property Prediction","volume":"64","author":"Heid","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_56","unstructured":"(2023, March 25). Chemprop. Available online: https:\/\/chemprop.readthedocs.io\/en\/latest\/#."},{"key":"ref_57","unstructured":"Jin, W., Barzilay, R., and Jaakkola, T. (2020). Multi-objective molecule generation using interpretable substructures. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1021\/ci400084k","article-title":"Time-split cross-validation as a method for estimating the goodness of prospective prediction","volume":"53","author":"Sheridan","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1021\/acs.jcim.3c00160","article-title":"Prospective validation of machine learning algorithms for absorption, distribution, metabolism, and excretion prediction: An industrial perspective","volume":"63","author":"Fang","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1021\/acs.molpharmaceut.2c00962","article-title":"Systematic evaluation of local and global machine learning models for the prediction of ADME properties","volume":"20","author":"Gerebtzoff","year":"2023","journal-title":"Mol. Pharm."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3772","DOI":"10.1002\/cmdc.202100418","article-title":"Application of deep neural network models in drug discovery programs","volume":"16","author":"Grebner","year":"2021","journal-title":"ChemMedChem"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1021\/tx960165e","article-title":"Covalent binding of a reactive metabolite derived from propranolol and its active metabolite 4-hydroxypropranolol to hepatic microsomal proteins of the rat","volume":"10","author":"Narimatsu","year":"1997","journal-title":"Chem. Res. Toxicol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.bmcl.2006.11.044","article-title":"A generally applicable method for assessing the electrophilicity and reactivity of diverse nitrile-containing compounds","volume":"17","author":"Oballa","year":"2007","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1007\/s43440-020-00151-w","article-title":"The effects of agomelatine and imipramine on liver cytochrome P450 during chronic mild stress (CMS) in the rat","volume":"72","author":"Haduch","year":"2020","journal-title":"Pharmacol. Rep."},{"key":"ref_65","first-page":"724","article-title":"Imipramine-induced inactivation of a cytochrome P450 2D enzyme in rat liver microsomes: In relation to covalent binding of its reactive intermediate","volume":"279","author":"Masubuchi","year":"1996","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1080\/10408440590935620","article-title":"Role of metabolism in drug-induced idiosyncratic hepatotoxicity","volume":"35","author":"Walgren","year":"2005","journal-title":"Crit. Rev. Toxicol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1021\/tx0001981","article-title":"Enzyme-induction dependent bioactivation of troglitazone and troglitazone quinone in vivo","volume":"14","author":"Tettey","year":"2001","journal-title":"Chem. Res. Toxicol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1021\/tx000180q","article-title":"Studies on the metabolism of troglitazone to reactive intermediates in vitro and in vivo. Evidence for novel biotransformation pathways involving quinone methide formation and thiazolidinedione ring scission","volume":"14","author":"Kassahun","year":"2001","journal-title":"Chem. Res. Toxicol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1021\/tx034033e","article-title":"Mechanisms of troglitazone hepatotoxicity","volume":"16","author":"Smith","year":"2003","journal-title":"Chem. Res. Toxicol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1038\/s41375-021-01123-6","article-title":"BTK gatekeeper residue variation combined with cysteine 481 substitution causes super-resistance to irreversible inhibitors acalabrutinib, ibrutinib and zanubrutinib","volume":"35","author":"Wang","year":"2021","journal-title":"Leukemia"}],"container-title":["Biomolecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-273X\/14\/5\/535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:36:46Z","timestamp":1760107006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-273X\/14\/5\/535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":70,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["biom14050535"],"URL":"https:\/\/doi.org\/10.3390\/biom14050535","relation":{},"ISSN":["2218-273X"],"issn-type":[{"value":"2218-273X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]}}}