The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells
Open accessAbstract
Liver toxicity is a leading systemic toxicity of drugs and chemicals demanding more human-relevant, high throughput, cost effective in vitro solutions. In addition to contributing to animal welfare, in vitro techniques facilitate exploring and understanding the molecular mechanisms underlying toxicity. New 'omics technologies can provide comprehensive information on the toxicological mode of action of compounds, as well as quantitative information about the multi-parametric metabolic response of cellular systems in normal and patho-physiological conditions. Here, we combined mass-spectroscopy metabolomics with an in vitro liver toxicity model. Metabolite profiles of HepG2 cells treated with 35 test substances resulted in 1114 cell supernatants and 3556 intracellular samples analyzed by metabolomics. Control samples showed relative standard deviations of about 10-15%, while the technical replicates were at 5-10%. Importantly, this procedure revealed concentration-response effects and patterns of metabolome changes that are consistent for different liver toxicity mechanisms (liver enzyme induction/inhibition, liver toxicity and peroxisome proliferation). Our findings provide evidence that identifying organ toxicity can be achieved in a robust, reliable, human-relevant system, representing a non-animal alternative for systemic toxicology.
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.
Integrity signals
Registry
Cites
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent reviews and experimental studies directly interpret HepG2 as a model suitable for solving certain toxicological problems, in particular for studying general mechanisms of cytotoxicity and cellular stress response (e.g., activation of transcription factors activator protein-1 (AP1), p53, nuclear factor-erythroid 2 related factor 2 (Nrf2), and nuclear factor kappa B (NF-κB)) [109,110,111],”
From The HepG2 Cell Line as a Model for Studying Metabolic Dysfunction-Associated Steatotic Liver Disease (2026) · cites this paper as [110]
Experimental — accuracy not yet measured
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
Concordance of the toxicity of pharmaceuticals in humans and in animals
Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., et al. (2000) · Regulatory Toxicology and Pharmacology · doi:10.1006/rtph.2000.1399 · Cited by 2,050
Wilkening, S., Stahl, F., & Bader, A. (2003) · Drug Metabolism and Disposition · doi:10.1124/dmd.31.8.1035 · Cited by 794
Gerets, H. H. J., Tilmant, K., Gerin, B., Chanteux, H., Depelchin, B. O., Dhalluin, S., & Atienzar, F. A. (2012) · Cell Biology and Toxicology · doi:10.1007/s10565-011-9208-4 · Cited by 698
Toxicology for the twenty-first century
Härtung, T. (2009) · Nature · doi:10.1038/460208a · Cited by 686
PPARα Agonist-Induced Rodent Tumors: Modes of Action and Human Relevance
Klaunig, J. E., Babich, M. A., Baetcke, K. P., Cook, J. C., Corton, J. C., David, R. M., DeLuca, J. G., Lai, D. Y., McKee, R. H., Peters, J. M., et al. (2003) · Critical Reviews in Toxicology · doi:10.1080/713608372 · Cited by 614
Acetaminophen activation by human liver cytochromes P450IIE1 and P450IA2
Raucy, J. L., Lasker, J. M., Lieber, C. S., & Black, M. (1989) · Archives of Biochemistry and Biophysics · doi:10.1016/0003-9861(89)90278-6 · Cited by 478
Regulation of bile acid synthesis
Chiang, J. Y. L. (1998) · Frontiers in bioscience · doi:10.2741/a273 · Cited by 400
Metabolomics in toxicology and preclinical research
Ramı́rez, T. (2013) · ALTEX · doi:10.14573/altex.2013.2.209 · Cited by 221
Self-citationThe use of metabolomics for the discovery of new biomarkers of effect
van Ravenzwaay, B., Cunha, G. C.-P., Leibold, E., Looser, R., Mellert, W., Prokoudine, A., Walk, T. B., & Wiemer, J. C. (2007) · Toxicology Letters · doi:10.1016/j.toxlet.2007.05.021 · Cited by 207
Self-citationBouhifd, M., Härtung, T., Högberg, H. T., Kleensang, A., & Zhao, L. (2013) · Journal of Applied Toxicology · doi:10.1002/jat.2874 · Cited by 200
Flagged first, then by citations
Research works printed but not linked: 1
- HSE (1992) Evaluation on: fluoroglycofen-ethyl. May 1992. Issue No. 50. Department for Environment, Food and Rural Affairs. Pesticides Safety Directorate, UK
Cited by
Sorted by citations
Items 101–163 of 163
Zhang, Z., Chen, D., Yu, J., Su, X., & Li, L. (2022) · Analytical Biochemistry · doi:10.1016/j.ab.2022.114887 · Cited by 6
Hareng, L., Schuster, P., Haake, V., Walk, T., Herold, M. J., Laue, H., & Natsch, A. (2022) · Archives of Toxicology · doi:10.1007/s00204-022-03379-y · Cited by 6
Self-citationCell Dome as an Evaluation Platform for Organized HepG2 Cells
Kazama, R., Fujita, S., & Sakai, S. (2022) · Cells · doi:10.3390/cells12010069 · Cited by 6
Trisubstituted 1,3,5-Triazines as Histamine H4 Receptor Antagonists with Promising Activity In Vivo
Olejarz‐Maciej, A., Mogilski, S., Karcz, T. P., Werner, T., Kamińska, K., Kupczyk, J., Honkisz-Orzechowska, E., Latacz, G., Stark, H., Kieć‐Kononowicz, K., et al. (2023) · Molecules · doi:10.3390/molecules28104199 · Cited by 8
Proteomic Comparison of Various Hepatic Cell Cultures for Preclinical Safety Pharmacology
Hurrell, T., Segeritz, C., Vallier, L., Lilley, K. S., & Cromarty, D. (2018) · Toxicological Sciences · doi:10.1093/toxsci/kfy084 · Cited by 8
The HepG2 Cell Line as a Model for Studying Metabolic Dysfunction-Associated Steatotic Liver Disease
Kotlyarova, A., Iskrina, A., & Kotlyarov, S. (2026) · International journal of molecular sciences · doi:10.3390/ijms27083399 · Cited by 10
Diethyl phthalate and dibutyl phthalate disrupt sirtuins expression in the HepG2 cells
Gutiérrez-García, A. K., Torres-García, D. A., & León‐Rodríguez, A. D. (2024) · Toxicology Research · doi:10.1093/toxres/tfae103 · Cited by 5
Birk, B., Haake, V., Sperber, S., Herold, M. J., Wallisch, S. K., Huener, H.-A., Verlohner, A., Amma, M. M., Walk, T. B., Hernandez, T. R., et al. (2021) · Toxicology Letters · doi:10.1016/j.toxlet.2021.09.011 · Cited by 5
Self-citationMurali, A., Zickgraf, F. M., Ternes, P., Giri, V., Cameron, H. J., Sperber, S., Haake, V., Driemert, P., Kamp, H., Funk-Weyer, D., et al. (2023) · Microorganisms · doi:10.3390/microorganisms11020533 · Cited by 5
Self-citationRolland, N. C., Graslin, F., Schorsch, F., Pourcher, T., & Blanck, O. (2024) · Toxicology in Vitro · doi:10.1016/j.tiv.2024.105911 · Cited by 5
Albenque-Rubio, S., Guillon, J., Agnamey, P., Damiani, C., Savrimoutou, S., Mustière, R., Pinaud, N., Moreau, S., Mergny, J., Ronga, L., et al. (2024) · Drugs and Drug Candidates · doi:10.3390/ddc3030035 · Cited by 5
Jie, B., Chen, L., Weng, R., Liu, J., Yin, X. Q., Lu, Y. S., Qiu, J., Qian, Y. Z., & Xu, Y. (2024) · eFood · doi:10.1002/efd2.70024 · Cited by 5
Profiling biological effects of microbiome metabolites via machine learning
Chung, H. A., Fralish, Z., Tu, T., & Reker, D. (2026) · iScience · doi:10.1016/j.isci.2026.115282 · Cited by 4
Optimizing lipopeptide bioactivity: The impact of non-ionic surfactant dressing
Ábrahám, Á., Gyulai, G., Mihály, J., Horváth, A., Dobay, O., Varga, Z., Kiss, É., & Horváti, K. (2024) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2024.101020 · Cited by 4
Hibino, Y., Iguchi, A., & Zaitsu, K. (2022) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2022.116316 · Cited by 4
Metabolomics in plant protection product research and development
Aliferis, K. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818196-6.00006-6 · Cited by 4
Untargeted metabolomics for the study of antiinfective plants
Kellogg, J. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90999-0.00017-3 · Cited by 3
Liver Function in Novel Coronavirus Disease (COVID-19): A Systematic Review and Meta-AnalysisPreprint
Zahedi, M., Yousefi, M. H., Abounoori, M., Malekan, M., Tajik, F., Heydari, K., Mortazavi, P., Ghazaeian, M., Sheydaee, F., Nasirzadeh, A., et al. (2020) · medRxiv · doi:10.1101/2020.05.20.20108357 · Cited by 4
Ventura‐Hunter, C., Pretzel, D., Kellner, C., Hoeppener, S., Roesner, N. C., Quintana, P., Schubert, U. S., Ayora‐Talavera, G., Saldívar‐Guerra, E., Fedeli, S., et al. (2025) · European Polymer Journal · doi:10.1016/j.eurpolymj.2025.113876 · Cited by 3
Güvenç, D., Gülbahar, M. Y., Yavuz, O., & Güvenç, T. (2019) · Kafkas Universitesi Veteriner Fakultesi Dergisi · doi:10.9775/kvfd.2019.22925 · Cited by 3
Systems biology approaches to interpreting genomic data
van den Beucken, T. (2019) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2019.02.004 · Cited by 3
Kurup, L. B., & Vijayan, S. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.05.256 · Cited by 3
Brenner, D., Bergo, A. M., Řehůřková, E., Havlík, J., Babica, P., & Sovadinová, I. (2026) · Toxicology · doi:10.1016/j.tox.2026.154538 · Cited by 2
Liu, Y., Su, B., & Wu, B. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176358 · Cited by 2
Sibiya, T., Xaba, L., Mthethwa, L., Chuturgoon, A. A., & Msomi, N. (2025) · Viruses · doi:10.3390/v17081057 · Cited by 2
In vivo and in vitro apoptosis induced by new acaricidal ethyl-carbamates in Rhipicephalus microplus
Escobar-Chavarría, O., Cossío-Bayúgar, R., Ramírez‐Noguera, P., Prado-Ochoa, M. G., Velázquez‐Sánchez, A. M., Muñoz-Guzmán, M. A., Ángeles, E., & Alba-Hurtado, F. (2020) · Ticks and Tick-borne Diseases · doi:10.1016/j.ttbdis.2020.101603 · Cited by 2
Singulani, J. D. L., Scorzoni, L., da Silva, P. B., Nazaré, A. C., Polaquini, C. R., Baveloni, F. G., Chorilli, M., Regasini, L. O., Fusco‐Almeida, A. M., & Mendes‐Giannini, M. J. S. (2022) · Future Microbiology · doi:10.2217/fmb-2021-0095 · Cited by 2
Biomarkers Detection for Toxicity Testing Using Metabolomics
Borts, D. J. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814655-2.00062-1 · Cited by 2
Applying Systems Toxicology Methods to Drug Safety
Talikka, M., Belcastro, V., Boué, S., Marescotti, D., Hoeng, J., & Peitsch, M. C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11522-3 · Cited by 2
Hung, S. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/11926234 · Cited by 2
Arnst, T. F., Hemmer, S., Fecher‐Trost, C., Wagmann, L., & Meyer, M. R. (2025) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-025-06235-x · Cited by 2
Maki, M., Vántus, V. B., Kőszegi, B., Szabó, A., Fekete, K., Ali, O., Ali, M. S., Jánosi, T. Z., & Gallyas, F. (2026) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-026-05581-3 · Cited by 1
CYTOTOXICITY AND HEMOCOMPATIBILITY OF DOXORUBICIN-LOADED PLGA NANOPARTICLES
Malinovskaya, J. A., Kovalenko, E. I., Kovshova, T. S., Osipova, N., Максименко, О. О., Balabanyan, V. Y., Razzhivina, V., Grechikhina, M. V., Boyko, A. A., & Gelperina, S. E. (2020) · Russian Journal of Biotherapy · doi:10.17650/1726-9784-2019-19-1-71-80 · Cited by 1
Cuykx, M., Beirnaert, C., Rodrigues, R. M., Laukens, K., Vanhaecke, T., & Covaci, A. (2019) · Metabolites · doi:10.3390/metabo9110265 · Cited by 1
Secretome-Based Prediction of Three-Dimensional Hepatic Microtissue Physiological Relevance
Asthana, A., White, C. M., Ndyabawe, K., Douglass, M. E., & Kisaalita, W. S. (2019) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.9b01446 · Cited by 1
Watafua, M., Ejiofor, J. I., Musa, A., & Ahmad, M. H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.22.497272 · Cited by 1
Gene Influencing in COVID-19 Infection, Disease Severity and its Pharmacotherapy
Sarkar, S., Dasgupta, S., Srivastava, V., Sahoo, S., & Tripathi, S. K. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.sp178
Maheswari, M. M. U., Velayutharaj, A., Saraswathi, R., Prabhushankar, S., & Shivakumar, R. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.sp188
Siricilla, S., Hansen, C. J., Rogers, J. H., Simpson, C. L., Crockett, S. L., Reese, J., Paria, B. C., & Herington, J. L. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.13.444040
Nählstedt, P., Donthamsetty, S., Sterchele, P., Huang, X., Ladics, G. S., Na, M., Lee, I., Api, A. M., Gradin, R., Johansson, H., et al. (2023) · Toxicology Letters · doi:10.1016/s0378-4274(23)00876-7
Protective Effects of Codium fragile Extract against Acetaminophen-Induced Liver Injury
Lee, Y.-L., Lee, J.-Y., Park, J.-W., Lee, J., Lee, H.-H., & Lee, D.-H. (2024) · Journal of Microbiology and Biotechnology · doi:10.4014/jmb.2409.09061
Al-Mekhlafi, F. A., Almohssin, A. Z. M., Abutaha, N., Al-Qahtni, A. H., Wadaan, M. A., & Al-Khalifa, M. S. (2024) · Revista Colombiana de Entomología · doi:10.25100/socolen.v50i2.13350
Advantages of ethylene glycol diacetate as a potential solvent for herbicide formulations
Li, M., Jin, L., Liu, B., Qian, Y., Hu, H., Wang, X., Zhang, Y., & Xu, W. (2025) · Journal of Pesticide Science · doi:10.1584/jpestics.d24-051
Comparison of popular enrichment methods for untargeted in vitro metabolomics
Schermer, Y., Wagner, F., Stegmüller, S., & Richling, E. (2025) · Metabolomics · doi:10.1007/s11306-025-02309-0
Albenque-Rubio, S., Guillon, J., Agnamey, P., Damiani, C., Savrimoutou, S., Ronga, L., Hanot, M., Zangmo, T., Pinaud, N., Moreau, S., et al. (2025) · Drugs and Drug Candidates · doi:10.3390/ddc4030039
Alonge, O., Alagbe, O. V., Wang, B., Pearson, K., Goodrich, S., & Kuila, D. (2025) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2025.138366
Potunuru, U. R., Gupta, N., Tiwari, S., Shah, I. A., Patel, D. K., & Shetty, P. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.29.685250
El‐Mansy, M. F., Soares, P., dos Remedios, J. R. D., Nowar, R., Fox, S. G., Yu, A., Klein, W. L., Morimoto, R. I., & Silverman, R. B. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.109190
Rybczyńska‐Tkaczyk, K., Skóra, B., & Szychowski, K. A. (2026) · Toxicology Letters · doi:10.1016/j.toxlet.2026.111854
Ungur, D. T., Pătraș, L., Pop, M. M., Iordache, C., Tămaş, T., Mija, A., & Banciu, M. (2026) · Journal of Pharmacy and Pharmacology · doi:10.1093/jpp/rgag019
Morisseau, L. (2023) · theses.fr (ABES) · doi:10.70675/cfa5953ez697dz47bez9768zde0845d5a7b8
Makanyane, D. M., Majebe, M., Mabuza, L. P., Ngubane, P. S., Khathi, A., Mambanda, A., & Booysen, I. N. (2026) · Colloids and Surfaces B Biointerfaces · doi:10.1016/j.colsurfb.2026.115733
Alimba, C. G., Adesida, S. O., Gandhi, D., Sivanesan, S., & Krishnamurthi, K. (2026) · Mutation Research/Genetic Toxicology and Environmental Mutagenesis · doi:10.1016/j.mrgentox.2026.503941
Li, T., Lv, L., Zheng, Y., Guan, J., Zhao, W., Han, L., Wang, P., & Zhao, Y. (2026) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2026.110026
Assessment of the Potential Impact of Aqueous and Ethanolic Malt Extracts on HepG2 Liver Cells
Santos, D. L. P., Pereira, M. J., Jesus, A. M. C., Oliveira, R. F., Oliveira, A. I., & Pinho, C. (2026) · Food Technology and Biotechnology · doi:10.17113/ftb.64.03.26.9286
Moon, J. H., Bong, S.-H., Kim, S., Park, Y. J., & Lee, D. Y. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.129017
Balachandran, S., Arumugam, M. K., & Kandy, R. R. K. (2026) · Journal of Xenobiotics · doi:10.3390/jox16050170
New approach methodologies for in vitro detection of chemical-induced cholestatic liver injury
Maerten, A., Avîrvarei, A.-C., & Vinken, M. (2026) · Archives of Toxicology · doi:10.1007/s00204-026-04541-6
Stagkos-Georgiadis, A., Deepika, D., Kamp, H., Grottker, J., Kumar, V., Marx‐Stoelting, P., Martin, S., Ritz, V., Tralau, T., & Bloch, D. (2026) · Toxicology · doi:10.1016/j.tox.2026.154611
Self-citation
Metadata
- Authors
- Ramirez, T., Strigun, A., Verlohner, A., Huener, H.-A., Peter, E., Herold, M., Bordag, N., Mellert, W., Walk, T., Spitzer, M., Jiang, X., Sperber, S., Hofmann, T., Hartung, T., Kamp, H., & van Ravenzwaay, B.
- Affiliations
- BASF (Germany) (DE)
- Corresponding author
- Hofmann, Thomas
- ORCID
- Venue
- Archives of Toxicology
- Published
- 2017-09-30
- Volume
- 92
- Issue
- 2
- Pages
- 893-906
- ISSN
- 0340-5761 (ISSN-L)
- 1432-0738
- PMC
- PMC5818600
- OpenAlex
- W2759884959
- Cited by
- 164
- References
- 39
- Citations by year
- 2026: 14 · 2025: 13 · 2024: 14 · 2023: 26 · 2022: 25 · 2021: 25 · 2020: 23 · 2019: 13 · 2018: 10 · 2017: 1
- FWCI
- 13.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Archives of Toxicology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- hepatotoxicity, metabolomics, HepG2 cells, in vitro toxicity models, mechanism of action, non-animal alternatives, liver enzyme induction, liver enzyme inhibition, peroxisome proliferation, concentration-dependent effects, intracellular metabolites, mass spectrometry metabolomics
- MeSH terms
- Animal Testing Alternatives, Enzyme Induction, Humans, Liver, Toxicity Tests, Metabolomics, Metabolome, Hep G2 Cells
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