Cited by 50

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment

Moreno-Torres, Marta, Quintás, Guillermo, Castell, José V.·2022·Metabolites

Abstract

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms “metabolomics”, “DILI”, and “humans”. Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/metabo12060564

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 1 references

Research works printed but not linked: 109

  1. Vuppalanchi, 2007, Etiology of new-onset jaundice: How often is it caused by idiosyncratic drug-induced liver injury in the United States?, Am. J. Gastroenterol., 102, 558, 10.1111/j.1572-0241.2006.01019.x · DOI
  2. Walker, 2020, The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development, Arch. Toxicol., 94, 2559, 10.1007/s00204-020-02763-w · DOI
  3. Larson, 2005, Acetaminophen-induced acute liver failure: Results of a United States multicenter, prospective study, Hepatology, 42, 1364, 10.1002/hep.20948 · DOI
  4. Ostapowicz, 2002, Results of a Prospective Study of Acute Liver Failure at 17 Tertiary Care Centers in the United States, Ann. Intern. Med., 137, 947, 10.7326/0003-4819-137-12-200212170-00007 · DOI · Paper page
  5. Jerlstad, 2005, Fulminant drug-induced hepatic failure leading to death or liver transplantation in Sweden, Scand. J. Gastroenterol., 40, 1095, 10.1080/00365520510023846 · DOI · Paper page
  6. Russo, 2004, Liver transplantation for acute liver failure from drug induced liver injury in the United States, Liver Transplant., 10, 1018, 10.1002/lt.20204 · DOI · Paper page
  7. Saxena, 2006, 1H NMR spectroscopy for the prediction of therapeutic outcome in patients with fulminant hepatic failure, NMR Biomed., 19, 521, 10.1002/nbm.1034 · DOI
  8. Montero, 2004, Acute and clinically relevant drug-induced liver injury: A population based case-control study, Br. J. Clin. Pharmacol., 58, 71, 10.1111/j.1365-2125.2004.02133.x · DOI
  9. Regev, 2014, Drug-Induced Liver Injury and Drug Development: Industry Perspective, Semin. Liver Dis., 34, 227, 10.1055/s-0034-1375962 · DOI · Paper page
  10. Chen, 2015, Drug-induced liver injury: Interactions between drug properties and host factors, J. Hepatol., 63, 503, 10.1016/j.jhep.2015.04.016 · DOI
  11. Castell, 1998, Allergic hepatitis: A drug-mediated organ-specific immune reaction, Clin. Exp. Allergy, 28, 13
  12. Castell, 2006, Allergic hepatitis induced by drugs, Curr. Opin. Allergy Clin. Immunol., 6, 258, 10.1097/01.all.0000235898.80052.80 · DOI
  13. Yu, 2017, CSH guidelines for the diagnosis and treatment of drug-induced liver injury, Hepatol. Int., 11, 221, 10.1007/s12072-017-9793-2 · DOI · Paper page
  14. Sandhu, 2020, Drug-Induced Liver Injury in GI Practice, Hepatol. Commun., 4, 631, 10.1002/hep4.1503 · DOI
  15. Kleiner, 2009, The Pathology of Drug-Induced Liver Injury, Semin. Liver Dis., 29, 364, 10.1055/s-0029-1240005 · DOI
  16. Tillmann, 2019, Tools for causality assessment in drug-induced liver disease, Curr. Opin. Gastroenterol., 35, 183, 10.1097/MOG.0000000000000526 · DOI
  17. Lucena, 2006, Determinants of the clinical expression of amoxicillin-clavulanate hepatotoxicity: A prospective series from Spain, Hepatology, 44, 850, 10.1002/hep.21324 · DOI
  18. Lucena, 2000, Trovafloxacin-Induced Acute Hepatitis, Clin. Infect. Dis., 30, 400, 10.1086/313680 · DOI
  19. Lee, 2005, Recognizing Drug-Induced Liver Injury: Current Problems, Possible Solutions, Toxicol. Pathol., 33, 155, 10.1080/01926230590522356 · DOI
  20. Danjuma, 2019, Novel biomarkers for potential risk stratification of drug induced liver injury (DILI): A narrative perspective on current trends, Medicine, 98, e18322, 10.1097/MD.0000000000018322 · DOI
  21. Yang, X., Schnackenberg, L.K., Shi, Q., and Salminen, W.F. (2014). Chapter 13—Hepatic toxicity biomarkers. Biomarkers in Toxicology, Academic Press. · DOI
  22. Watkins, 2013, Managing the risk of drug-induced liver injury, Clin. Pharmacol. Ther., 94, 629, 10.1038/clpt.2013.182 · DOI
  23. Fontana, 2014, Idiosyncratic Drug-Induced Liver Injury Is Associated with Substantial Morbidity and Mortality within 6 Months from Onset, Gastroenterology, 147, 96, 10.1053/j.gastro.2014.03.045 · DOI · Paper page
  24. Kaplowitz, N., and DeLeve, L.D. (2003). Drug-Induced Liver Disease, Marcel Decker. · DOI
  25. Devarbhavi, 2012, An Update on Drug-induced Liver Injury, J. Clin. Exp. Hepatol., 2, 247, 10.1016/j.jceh.2012.05.002 · DOI
  26. Amacher, 2013, Development of Blood Biomarkers for Drug-Induced Liver Injury: An Evaluation of Their Potential for Risk Assessment and Diagnostics, Mol. Diagn. Ther., 17, 343, 10.1007/s40291-013-0049-0 · DOI
  27. Russmann, 2009, Current concepts of mechanisms in drug-induced hepatotoxicity, Curr. Med. Chem., 16, 3041, 10.2174/092986709788803097 · DOI
  28. Antoine, 2013, Mechanistic biomarkers provide early and sensitive detection of acetaminophen-induced acute liver injury at first presentation to hospital, Hepatology, 58, 777, 10.1002/hep.26294 · DOI
  29. Church, 2019, Candidate biomarkers for the diagnosis and prognosis of drug-induced liver injury: An international collaborative effort, Hepatology, 69, 760, 10.1002/hep.29802 · DOI · Paper page
  30. Fu, 2020, Molecular Biomarkers in Drug-Induced Liver Injury: Challenges and Future Perspectives, Front. Pharmacol., 10, 1667, 10.3389/fphar.2019.01667 · DOI
  31. Daly, 2009, HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin, Nat. Genet., 41, 816, 10.1038/ng.379 · DOI
  32. Singer, 2010, A genome-wide study identifies HLA alleles associated with lumiracoxib-related liver injury, Nat. Genet., 42, 711, 10.1038/ng.632 · DOI
  33. Mallal, 2008, HLA-B*5701 Screening for Hypersensitivity to Abacavir, N. Engl. J. Med., 358, 568, 10.1056/NEJMoa0706135 · DOI
  34. Waters, 2006, Integrated Metabonomic Analysis of Bromobenzene-Induced Hepatotoxicity: Novel Induction of 5-Oxoprolinosis, J. Proteome Res., 5, 1448, 10.1021/pr060024q · DOI
  35. Vazquez, J.H., and McGill, M.R. (2021). Redrawing the Map to Novel DILI Biomarkers in Circulation: Where Are We, Where Should We Go, and How Can We Get There?. Livers, 1. · DOI
  36. Abraham, 2008, Application of a High-Content Multiparameter Cytotoxicity Assay to Prioritize Compounds Based on Toxicity Potential in Humans, SLAS Discov. Adv. Sci. Drug Discov., 13, 527, 10.1177/1087057108318428 · DOI
  37. Benet, 2014, A simple transcriptomic signature able to predict drug-induced hepatic steatosis, Arch. Toxicol., 88, 967, 10.1007/s00204-014-1197-7 · DOI
  38. Bleibel, 2007, Drug-Induced Liver Injury: Review Article, Am. J. Dig. Dis., 52, 2463
  39. Iruzubieta, 2015, The Need for Biomarkers in Diagnosis and Prognosis of Drug-Induced Liver Disease: Does Metabolomics Have Any Role?, BioMed Res. Int., 2015, 386186, 10.1155/2015/386186 · DOI · Paper page
  40. Oldiges, 2007, Metabolomics: Current state and evolving methodologies and tools, Appl. Microbiol. Biotechnol., 76, 495, 10.1007/s00253-007-1029-2 · DOI
  41. Goodacre, 2004, Metabolomics by numbers: Acquiring and understanding global metabolite data, Trends Biotechnol., 22, 245, 10.1016/j.tibtech.2004.03.007 · DOI
  42. Clayton, 2009, Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism, Proc. Natl. Acad. Sci. USA, 106, 14728, 10.1073/pnas.0904489106 · DOI · Paper page
  43. Liu, 2010, Metabonomics study of urine from Sprague–Dawley rats exposed to Huang-yao-zi using 1H NMR spectroscopy, J. Pharm. Biomed. Anal., 52, 136, 10.1016/j.jpba.2009.12.026 · DOI · Paper page
  44. Lu, 2010, NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats, Toxicol. Appl. Pharmacol., 248, 178, 10.1016/j.taap.2010.07.020 · DOI · Paper page
  45. Shima, 2011, Influences of methamphetamine-induced acute intoxication on urinary and plasma metabolic profiles in the rat, Toxicology, 287, 29, 10.1016/j.tox.2011.05.012 · DOI · Paper page
  46. Winnike, 2010, Use of Pharmaco-Metabonomics for Early Prediction of Acetaminophen-Induced Hepatotoxicity in Humans, Clin. Pharmacol. Ther., 88, 45, 10.1038/clpt.2009.240 · DOI
  47. Jaeschke, 2014, Acetaminophen-induced Liver Injury: From Animal Models to Humans, J. Clin. Transl. Hepatol., 2, 153
  48. Donato, 2012, Targeted profiling of circulating and hepatic bile acids in human, mouse, and rat using a UPLC-MRM-MS-validated method, J. Lipid Res., 53, 2231, 10.1194/jlr.D028803 · DOI
  49. Sundaram, 2017, Drug-induced cholestasis, Hepatol. Commun., 1, 726, 10.1002/hep4.1088 · DOI · Paper page
  50. Kawada, 2001, Characterization of a stellate cell activation-associated protein (STAP) with peroxidase activity found in rat hepatic stellate cells, J. Biol. Chem., 276, 25318, 10.1074/jbc.M102630200 · DOI
  51. Teranishi, 2015, Involvement of hepatic stellate cell cytoglobin in acute hepatocyte damage through the regulation of CYP2E1-mediated xenobiotic metabolism, Lab. Investig., 95, 515, 10.1038/labinvest.2015.29 · DOI
  52. Lecoeur, 2019, Determination of acetaminophen and its main metabolites in urine by capillary electrophoresis hyphenated to mass spectrometry, Talanta, 205, 120108, 10.1016/j.talanta.2019.07.003 · DOI
  53. Moro, 2021, Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: A systematic survey, Sci. Rep., 11, 22119, 10.1038/s41598-021-01652-1 · DOI
  54. Fannin, 2009, Acetaminophen dosing of humans results in blood transcriptome and metabolome changes consistent with impaired oxidative phosphorylation, Hepatology, 51, 227, 10.1002/hep.23330 · DOI
  55. Beger, 2010, Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity, Toxicol. Appl. Pharmacol., 243, 154, 10.1016/j.taap.2009.11.019 · DOI
  56. Yu, 2017, Metabonomics Research Progress on Liver Diseases, Can. J. Gastroenterol. Hepatol., 2017, 8467192, 10.1155/2017/8467192 · DOI
  57. Beckonert, 2010, High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues, Nat. Protoc., 5, 1019, 10.1038/nprot.2010.45 · DOI
  58. Zimmerman, 2000, Drug-induced liver disease, Clin. Liver Dis., 4, 73, 10.1016/S1089-3261(05)70097-0 · DOI · Paper page
  59. Lenz, 2006, Analytical Strategies in Metabonomics, J. Proteome Res., 6, 443, 10.1021/pr0605217 · DOI
  60. Beger, 2013, A Review of Applications of Metabolomics in Cancer, Metabolites, 3, 552, 10.3390/metabo3030552 · DOI
  61. Robards, K., Haddad, P., and Jackson, P. (1994). Principles and Practice of Modern Chromatographic Methods, Academic Press.
  62. Misra, B.B., Bassey, E., and Olivier, M. (2019). Comparison of a GC-Orbitrap-MS with parallel GC-FID capabilities for metabolomics of human serum. bioRxiv, 740795. · DOI
  63. Hawrył, A., Hawrył, M., and Waksmundzka-Hajnos, M. (2019). Liquid chromatography fingerprint analysis and antioxidant activity of selected lavender species with chemometric calculations. PLoS ONE, 14. · DOI
  64. Zheng, 2017, Automatic time-shift alignment method for chromatographic data analysis, Sci. Rep., 7, 256, 10.1038/s41598-017-00390-7 · DOI
  65. Pluskal, T., Castillo, S., Villar-Briones, A., and Orešič, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform., 11. · DOI
  66. Smith, 2006, XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification, Anal. Chem., 78, 779, 10.1021/ac051437y · DOI
  67. Monge, 2019, Challenges in Identifying the Dark Molecules of Life, Annu. Rev. Anal. Chem., 12, 177, 10.1146/annurev-anchem-061318-114959 · DOI
  68. Ten-Doménech, I., Martínez-Sena, T., Moreno-Torres, M., Sanjuan-Herráez, J.D., Castell, J.V., Parra-Llorca, A., Vento, M., Quintás, G., and Kuligowski, J. (2020). Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC–MS Metabolomics. Metabolites, 10. · DOI
  69. Zelena, 2009, Development of a Robust and Repeatable UPLC−MS Method for the Long-Term Metabolomic Study of Human Serum, Anal. Chem., 81, 1357, 10.1021/ac8019366 · DOI
  70. Vento, 2018, Model selection for within-batch effect correction in UPLC-MS metabolomics using quality control—Support vector regression, Anal. Chim. Acta, 1026, 62, 10.1016/j.aca.2018.04.055 · DOI
  71. Vento, 2018, Evaluation of batch effect elimination using quality control replicates in LC-MS metabolite profiling, Anal. Chim. Acta, 1019, 38, 10.1016/j.aca.2018.02.053 · DOI
  72. Castell, 2021, Extracting consistent biological information from functional results of metabolomic pathway analysis using the Mantel’s test, Anal. Chim. Acta, 1187, 339173, 10.1016/j.aca.2021.339173 · DOI
  73. Andersson, 2009, A systems biology approach to understanding elevated serum alanine transaminase levels in a clinical trial with ximelagatran, Biomarkers, 14, 572, 10.3109/13547500903261354 · DOI
  74. Zhang, 2019, Risk profiling using metabolomic characteristics for susceptible individuals of drug-induced liver injury caused by Polygonum multiflorum, Arch. Toxicol., 94, 245, 10.1007/s00204-019-02595-3 · DOI · Paper page
  75. Sonn, 2021, Metabolomic markers predictive of hepatic adaptation to therapeutic dosing of acetaminophen, Clin. Toxicol., 60, 221, 10.1080/15563650.2021.1925686 · DOI
  76. Bhattacharyya, 2013, Acylcarnitine Profiles in Acetaminophen Toxicity in the Mouse: Comparison to Toxicity, Metabolism and Hepatocyte Regeneration, Metabolites, 3, 606, 10.3390/metabo3030606 · DOI
  77. McGill, 2013, Circulating acylcarnitines as biomarkers of mitochondrial dysfunction after acetaminophen overdose in mice and humans, Arch. Toxicol., 88, 391, 10.1007/s00204-013-1118-1 · DOI
  78. Huo, 2014, An effective assessment of valproate sodium-induced hepatotoxicity with UPLC–MS and 1HNMR-based metabonomics approach, J. Chromatogr. B, 969, 109, 10.1016/j.jchromb.2014.08.011 · DOI
  79. Woolbright, 2014, Glycodeoxycholic Acid Levels as Prognostic Biomarker in Acetaminophen-Induced Acute Liver Failure Patients, Toxicol. Sci., 142, 436, 10.1093/toxsci/kfu195 · DOI
  80. James, L., Yan, K., Pence, L., Simpson, P., Bhattacharyya, S., Gill, P., Letzig, L., Kearns, G., and Beger, R. (2015). Comparison of Bile Acids and Acetaminophen Protein Adducts in Children and Adolescents with Acetaminophen Toxicity. PLoS ONE, 10. · DOI
  81. Wang, 2015, Evidence chain-based causality identification in herb-induced liver injury: Exemplification of a well-known liver-restorative herb Polygonum multiflorum, Front. Med., 9, 457, 10.1007/s11684-015-0417-8 · DOI · Paper page
  82. Bhattacharyya, 2016, Targeted metabolomic profiling indicates structure-based perturbations in serum phospholipids in children with acetaminophen overdose, Toxicol. Rep., 3, 747, 10.1016/j.toxrep.2016.08.004 · DOI
  83. Schnackenberg, L.K., Sun, J., Bhattacharyya, S., Gill, P., James, L.P., and Beger, R.D. (2017). Metabolomics Analysis of Urine Samples from Children after Acetaminophen Overdose. Metabolites, 7. · DOI
  84. Lee, 2016, Exploration of Biomarkers for Amoxicillin/Clavulanate-Induced Liver Injury: Multi-Omics Approaches, Clin. Transl. Sci., 10, 163, 10.1111/cts.12425 · DOI
  85. Cao, 2018, First-line anti-tuberculosis drugs induce hepatotoxicity: A novel mechanism based on a urinary metabolomics platform, Biochem. Biophys. Res. Commun., 497, 485, 10.1016/j.bbrc.2018.02.030 · DOI
  86. Ma, 2019, Serum metabolome and targeted bile acid profiling reveals potential novel biomarkers for drug-induced liver injury, Medicine, 98, e16717, 10.1097/MD.0000000000016717 · DOI
  87. Xie, 2019, Metabolomics and Cytokine Analysis for Identification of Severe Drug-Induced Liver Injury, J. Proteome Res., 18, 2514, 10.1021/acs.jproteome.9b00047 · DOI
  88. Saito, K., Kagawa, T., Tsuji, K., Kumagai, Y., Sato, K., Sakisaka, S., Sakamoto, N., Aiso, M., Hirose, S., and Mori, N. (2020). Plasma Lipid Profiling of Three Types of Drug-Induced Liver Injury in Japanese Patients: A Preliminary Study. Metabolites, 10. · DOI
  89. Huang, 2020, Metabolomics Profiling and Diagnosis Biomarkers Searching for Drug-Induced Liver Injury Implicated to Polygonum multiflorum: A Cross-Sectional Cohort Study, Front. Med., 7, 858, 10.3389/fmed.2020.592434 · DOI
  90. Chen, 2021, Serum Metabolomic Analysis of Chronic Drug-Induced Liver Injury with or without Cirrhosis, Front. Med., 8, 640799, 10.3389/fmed.2021.640799 · DOI
  91. Conde, 2021, Metabolomic analysis to discriminate drug-induced liver injury (DILI) phenotypes, Arch. Toxicol., 95, 3049, 10.1007/s00204-021-03114-z · DOI
  92. Xie, Z., Zhang, L., Chen, E., Lu, J., Xiao, L., Liu, Q., Zhu, D., Zhang, F., Xu, X., and Li, L. (2021). Targeted Metabolomics Analysis of Bile Acids in Patients with Idiosyncratic Drug-Induced Liver Injury. Metabolites, 11. · DOI
  93. Soga, 2011, Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease, J. Hepatol., 55, 896, 10.1016/j.jhep.2011.01.031 · DOI
  94. Kim, 2013, Pattern Recognition Analysis for Hepatotoxicity Induced by Acetaminophen Using Plasma and Urinary 1H NMR-Based Metabolomics in Humans, Anal. Chem., 85, 11326, 10.1021/ac402390q · DOI
  95. Prescott, 1973, The effects of hepatic and renal damage on paracetamol metabolism and excretion following overdosage.: A pharmacokinetic study, J. Cereb. Blood Flow Metab., 49, 602
  96. Fukuhara, 2011, A 1H NMR-based Metabolomics Approach for Mechanistic Insight into Acetaminophen-induced Hepatotoxicity, Drug Metab. Pharmacokinet., 26, 399, 10.2133/dmpk.DMPK-11-RG-005 · DOI
  97. Coen, 2003, An Integrated Metabonomic Investigation of Acetaminophen Toxicity in the Mouse Using NMR Spectroscopy, Chem. Res. Toxicol., 16, 295, 10.1021/tx0256127 · DOI
  98. Dargue, 2020, Metabolism and Effects on Endogenous Metabolism of Paracetamol (Acetaminophen) in a Porcine Model of Liver Failure, Toxicol. Sci., 175, 87, 10.1093/toxsci/kfaa023 · DOI
  99. Bernal, 2002, Blood lactate as an early predictor of outcome in paracetamol-induced acute liver failure: A cohort study, Lancet, 359, 558, 10.1016/S0140-6736(02)07743-7 · DOI
  100. Hemming, 2001, The hippurate ratio as an indicator of functional hepatic reserve for resection of hepatocellular carcinoma in cirrhotic patients, J. Gastrointest. Surg., 5, 316, 10.1016/S1091-255X(01)80054-8 · DOI
  101. Ledermann, 2000, Relationship between hepatic mitochondrial functions in vivo and in vitro in rats with carbon tetrachloride-induced liver cirrhosis, J. Hepatol., 33, 216, 10.1016/S0168-8278(00)80362-1 · DOI
  102. Sun, 2008, Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS, J. Chromatogr. B, 871, 328, 10.1016/j.jchromb.2008.04.008 · DOI
  103. Ji, 2010, Uric acid potentially links fatty liver and high blood pressure, Hepatology, 52, 1518, 10.1002/hep.23855 · DOI
  104. Wang, 2017, The role of sphingosine 1-phosphate receptor 2 in bile-acid-induced cholangiocyte proliferation and cholestasis-induced liver injury in mice, Hepatology, 65, 2005, 10.1002/hep.29076 · DOI
  105. Azer, 2021, Use of bile acids as potential markers of liver dysfunction in humans: A systematic review, Medicine, 100, e27464, 10.1097/MD.0000000000027464 · DOI
  106. Nguyen, 2019, Identifying significantly impacted pathways: A comprehensive review and assessment, Genome Biol., 20, 203, 10.1186/s13059-019-1790-4 · DOI
  107. Pang, 2021, MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights, Nucleic Acids Res., 49, W388, 10.1093/nar/gkab382 · DOI
  108. Kumar, 2022, A Novel UPLC-MS Metabolomic Analysis-Based Strategy to Monitor the Course and Extent of iPSC Differentiation to Hepatocytes, J. Proteome Res., 21, 702, 10.1021/acs.jproteome.1c00779 · DOI
  109. Castell, 2015, LC-MS untargeted metabolomic analysis of drug-induced hepatotoxicity in HepG2 cells, Electrophoresis, 36, 2294, 10.1002/elps.201500095 · DOI · Paper page

Cited by

Sorted by citations

See all 50

Metadata

Authors
Moreno-Torres, M., Quintás, G., & Castell, J. V.
Venue
Metabolites
Publisher
MDPI AG
Published
2022-06-19
Volume
12
Issue
6
Pages
564
ISSN
2218-1989
Cited by
50
References
1
Language
en
Funders
  • European Consortium EU-ToxRisk (681002, IJC2018-036209-I, FP7-HEALTH-2013-INNOVATION-1, CP-IP 602156-1)
  • Ministerio de Ciencia e Innovación (681002, IJC2018-036209-I, FP7-HEALTH-2013-INNOVATION-1, CP-IP 602156-1)
  • EU project HeCaTos (681002, IJC2018-036209-I, FP7-HEALTH-2013-INNOVATION-1, CP-IP 602156-1)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review