Skip to content
2000
Volume 5, Issue 1
  • ISSN: 2950-5704
  • E-ISSN: 2950-5712

Abstract

Conducting repeated-dose toxicity testing is essential in the health risk assessment process. Currently, evaluating human safety relies heavily on animal studies to identify toxicity endpoints due to the absence of suitable human cell systems designed for regulatory purposes. However, reliance on animal models exhibiting inter-species variations often results in inaccurate predictions of toxicity in humans, resulting in the late-stage elimination of tested substances. Consequently, the cosmetic industry is actively searching for dependable human cell systems for repeated-dose toxicity assessments. Due to boundless human pluripotent cell’s ability to differentiate and proliferate into diverse cell types, these cells are considered a valuable and cost-effective resource for the development of organotypic cells. These cells are crucial for assessing long-term human organ toxicity. The recent advancements in high-throughput screening platforms and artificial intelligence present a promising avenue for the development and exploration of human biomarkers for repeated-dose toxicity in cellular models.

Loading

Article metrics loading...

/content/journals/jctv/10.2174/0129505704335182241218112943
2025-01-29
2025-10-01
Loading full text...

Full text loading...

References

  1. Van NormanG.A. Limitations of animal studies for predicting toxicity in clinical trials.JACC Basic Transl. Sci.20194784585410.1016/j.jacbts.2019.10.00831998852
    [Google Scholar]
  2. TaylorK. AlvarezL.R. An estimate of the number of animals used for scientific purposes worldwide in 2015.Altern. Lab. Anim.2019475-619621310.1177/026119291989985332090616
    [Google Scholar]
  3. HammJ. SullivanK. ClippingerA.J. StricklandJ. BellS. BhhataraiB. BlaauboerB. CaseyW. DormanD. ForsbyA. Garcia-ReyeroN. GehenS. GraepelR. HotchkissJ. LowitA. MathesonJ. ReavesE. ScaranoL. SprankleC. TunkelJ. WilsonD. XiaM. ZhuH. AllenD. Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing.Toxicol. In Vitro20174124525910.1016/j.tiv.2017.01.00428069485
    [Google Scholar]
  4. RedfernW.S. EwartL.C. LainéeP. PinchesM. RobinsonS. ValentinJ.P. Functional assessments in repeat-dose toxicity studies: The art of the possible.Toxicol. Res.20132420923410.1039/c3tx20093k
    [Google Scholar]
  5. FerdinandyP. BaczkóI. BencsikP. GiriczZ. GörbeA. PacherP. VargaZ.V. VarróA. SchulzR. Definition of hidden drug cardiotoxicity: Paradigm change in cardiac safety testing and its clinical implications.Eur. Heart J.201940221771177710.1093/eurheartj/ehy36529982507
    [Google Scholar]
  6. RedfernW. CarlssonL. DavisA. LynchW. MacKenzieI. PalethorpeS. SieglP. StrangI. SullivanA. WallisR. CammA.J. HammondT.G. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: Evidence for a provisional safety margin in drug development.Cardiovasc. Res.2003581324510.1016/S0008‑6363(02)00846‑512667944
    [Google Scholar]
  7. Van SummerenA. RenesJ. van DelftJ.H.M. KleinjansJ.C.S. MarimanE.C.M. Proteomics in the search for mechanisms and biomarkers of drug-induced hepatotoxicity.Toxicol. In vitro201226337338510.1016/j.tiv.2012.01.01222274661
    [Google Scholar]
  8. NingJ. ChenL. StrikwoldM. LouisseJ. WesselingS. RietjensI.M.C.M. Use of an in vitro–in silico testing strategy to predict inter-species and inter-ethnic human differences in liver toxicity of the pyrrolizidine alkaloids lasiocarpine and riddelliine.Arch. Toxicol.201993380181810.1007/s00204‑019‑02397‑730661089
    [Google Scholar]
  9. AtkinsJ.T. GeorgeG.C. HessK. Marcelo-LewisK.L. YuanY. BorthakurG. KhozinS. LoRussoP. HongD.S. Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trials.Br. J. Cancer2020123101496150110.1038/s41416‑020‑01033‑x32868897
    [Google Scholar]
  10. PistollatoF. MadiaF. CorviR. MunnS. GrignardE. PainiA. WorthA. Bal-PriceA. PrietoP. CasatiS. BerggrenE. BoppS.K. ZuangV. Current EU regulatory requirements for the assessment of chemicals and cosmetic products: Challenges and opportunities for introducing new approach methodologies.Arch. Toxicol.20219561867189710.1007/s00204‑021‑03034‑y33851225
    [Google Scholar]
  11. BaigM.W. MajidM. NasirB. HassanS.S. BungauS. HaqI. Toxicity evaluation induced by single and 28-days repeated exposure of withametelin and daturaolone in Sprague Dawley rats.Front. Pharmacol.20221399907810.3389/fphar.2022.99907836225589
    [Google Scholar]
  12. PingaleT.D. GuptaG.L. Acute and sub-acute toxicity study reveals no dentrimental effect of formononetin in mice upon repeated i.p. dosing.Toxicol. Mech. Methods202333868869710.1080/15376516.2023.223402637415263
    [Google Scholar]
  13. ElferinkM. OlingaP. DraaismaA. MeremaM. BauerschmidtS. PolmanJ. SchoonenW. GroothuisG. Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity.Toxicol. Appl. Pharmacol.2008229330030910.1016/j.taap.2008.01.03718346771
    [Google Scholar]
  14. VatakutiS. OlingaP. PenningsJ.L.A. GroothuisG.M.M. Validation of precision-cut liver slices to study drug-induced cholestasis: A transcriptomics approach.Arch. Toxicol.20179131401141210.1007/s00204‑016‑1778‑827344345
    [Google Scholar]
  15. MaestriE. The 3Rs principle in animal experimentation: A legal review of the state of the art in Europe and the case in Italy.BioTech2021102910.3390/biotech1002000935822819
    [Google Scholar]
  16. SuterL. SchroederS. MeyerK. GautierJ.C. AmbergA. WendtM. GmuenderH. MallyA. BoitierE. Ellinger-ZiegelbauerH. MatheisK. PfannkuchF. EU framework 6 project: Predictive toxicology (PredTox)--overview and outcome.Toxicol. Appl. Pharmacol.20112522738410.1016/j.taap.2010.10.00820955723
    [Google Scholar]
  17. GochtT. BerggrenE. AhrH.J. CotgreaveI. CroninM.T. DastonG. HardyB. HeinzleE. HeschelerJ. KnightD.J. MahonyC. PeschanskiM. SchwarzM. ThomasR.S. VerfaillieC. WhiteA. WhelanM. The SEURAT-1 approach towards animal free human safety assessment.Altern. Anim. Exp.201532192410.14573/altex.140804125372315
    [Google Scholar]
  18. GriesingerC. DesprezB. CoeckeS. CaseyW. ZuangV. Validation of alternative in vitro methods to animal testing: Concepts, challenges, processes and tools.Adv. Exp. Med. Biol.20168566513210.1007/978‑3‑319‑33826‑2_4
    [Google Scholar]
  19. CiallellaH.L. RussoD.P. SharmaS. LiY. SloterE. SweetL. HuangH. ZhuH. Predicting prenatal developmental toxicity based on the combination of chemical structures and biological data.Environ. Sci. Technol.20225695984599810.1021/acs.est.2c0104035451820
    [Google Scholar]
  20. SelvestrelG. RobinoF. BadernaD. ManganelliS. AsturiolD. ManganaroA. Zanotti RussoM. LavadoG. TomaC. RoncaglioniA. BenfenatiE. SpheraCosmolife: A new tool for the risk assessment of cosmetic products.Altern. Anim. Exp.202138456557910.14573/altex.201022133963416
    [Google Scholar]
  21. VogelR. SeidleT. SpielmannH. A modular one-generation reproduction study as a flexible testing system for regulatory safety assessment.Reprod. Toxicol.201029224224510.1016/j.reprotox.2009.09.00619808091
    [Google Scholar]
  22. RazaviS.M. SalariA. JamalpoorZ. Comparative evaluation of pathways and gene expression profile similarity in differentiated stem cells versus normal adult cells in seven human tissues.Gene Rep.20212410124210.1016/j.genrep.2021.101242
    [Google Scholar]
  23. LynchS. PridgeonC.S. DuckworthC.A. SharmaP. ParkB.K. GoldringC.E.P. Stem cell models as an in vitro model for predictive toxicology.Biochem. J.201947671149115810.1042/BCJ2017078030988136
    [Google Scholar]
  24. GnecchiM. SalaL. SchwartzP.J. Precision Medicine and cardiac channelopathies: When dreams meet reality.Eur. Heart J.202142171661167510.1093/eurheartj/ehab00733686390
    [Google Scholar]
  25. TakahashiK. TanabeK. OhnukiM. NaritaM. IchisakaT. TomodaK. YamanakaS. Induction of pluripotent stem cells from adult human fibroblasts by defined factors.Cell2007131586187210.1016/j.cell.2007.11.01918035408
    [Google Scholar]
  26. PangL. Toxicity testing in the era of induced pluripotent stem cells: A perspective regarding the use of patient-specific induced pluripotent stem cell–derived cardiomyocytes for cardiac safety evaluation.Curr. Opin. Toxicol.202023-24505510.1016/j.cotox.2020.04.001
    [Google Scholar]
  27. KumarD. BaligarP. SrivastavR. NaradP. RajS. TandonC. TandonS. Stem cell based preclinical drug development and toxicity prediction.Curr. Pharm. Des.202127192237225110.2174/138161282666620101910471233076801
    [Google Scholar]
  28. FritscheE. Haarmann-StemmannT. KaprJ. GalanjukS. HartmannJ. MertensP.R. KämpferA.A.M. SchinsR.P.F. TiggesJ. KochK. Stem cells for next level toxicity testing in the 21st century.Small20211715200625210.1002/smll.20200625233354870
    [Google Scholar]
  29. PericD. BarraganI. Giraud-TriboultK. EgesipeA.L. Meyniel-SchicklinL. CousinC. LotteauV. PetitV. TouhamiJ. BattiniJ.L. SitbonM. PinsetC. Ingelman-SundbergM. LaustriatD. PeschanskiM. Cytostatic effect of repeated exposure to simvastatin: A mechanism for chronic myotoxicity revealed by the use of mesodermal progenitors derived from human pluripotent stem cells.Stem Cells201533102936294810.1002/stem.210726184566
    [Google Scholar]
  30. BrancoM.A. NunesT.C. CabralJ.M.S. DiogoM.M. Developmental toxicity studies: The path towards humanized 3D stem cell-based models.Int. J. Mol. Sci.2023245485710.3390/ijms2405485736902285
    [Google Scholar]
  31. TralauT. LuchA. Drug-mediated toxicity: Illuminating the ‘bad’ in the test tube by means of cellular assays?Trends Pharmacol. Sci.201233735336410.1016/j.tips.2012.03.01522554615
    [Google Scholar]
  32. FullerH.R. MandefroB. ShirranS.L. GrossA.R. KausA.S. BottingC.H. MorrisG.E. SareenD. Spinal muscular atrophy patient iPSC-derived motor neurons have reduced expression of proteins important in neuronal development.Front. Cell. Neurosci.2016950610.3389/fncel.2015.0050626793058
    [Google Scholar]
  33. NohH. ShaoZ. CoyleJ.T. ChungS. Modeling schizophrenia pathogenesis using patient-derived induced pluripotent stem cells (iPSCs).Biochim. Biophys. Acta Mol. Basis Dis.2017186392382238710.1016/j.bbadis.2017.06.01928668333
    [Google Scholar]
  34. LeeG. PapapetrouE.P. KimH. ChambersS.M. TomishimaM.J. FasanoC.A. GanatY.M. MenonJ. ShimizuF. VialeA. TabarV. SadelainM. StuderL. Modelling pathogenesis and treatment of familial dysautonomia using patient-specific iPSCs.Nature2009461726240240610.1038/nature0832019693009
    [Google Scholar]
  35. SchreiberA.M. MisiorekJ.O. NapieralaJ.S. NapieralaM. Progress in understanding Friedreich’s ataxia using human induced pluripotent stem cells.Expert Opin. Orphan Drugs201972819010.1080/21678707.2019.156233430828501
    [Google Scholar]
  36. ZimmerB. LeeG. BalmerN.V. MeganathanK. SachinidisA. StuderL. LeistM. Evaluation of developmental toxicants and signaling pathways in a functional test based on the migration of human neural crest cells.Environ. Health Perspect.201212081116112210.1289/ehp.110448922571897
    [Google Scholar]
  37. ThonhoffJ. OjedaL. WuP. Stem cell-derived motor neurons: Applications and challenges in amyotrophic lateral sclerosis.Curr. Stem Cell Res. Ther.20094317819910.2174/15748880978905739219492980
    [Google Scholar]
  38. ArdhanareeswaranK. MarianiJ. CoppolaG. AbyzovA. VaccarinoF.M. Human induced pluripotent stem cells for modelling neurodevelopmental disorders.Nat. Rev. Neurol.201713526527810.1038/nrneurol.2017.4528418023
    [Google Scholar]
  39. OpreaD. SanzC.G. BarsanM.M. EnacheT.A. PC-12 cell line as a neuronal cell model for biosensing applications.Biosensors202212750010.3390/bios1207050035884303
    [Google Scholar]
  40. BuzanskaL. SypeckaJ. Nerini-MolteniS. CompagnoniA. HogbergH.T. del TorchioR. Domanska-JanikK. ZimmerJ. CoeckeS. A human stem cell-based model for identifying adverse effects of organic and inorganic chemicals on the developing nervous system.Stem Cells200927102591260110.1002/stem.17919609937
    [Google Scholar]
  41. PamiesD. WiersmaD. KattM.E. ZhaoL. BurtscherJ. HarrisG. SmirnovaL. SearsonP.C. HartungT. HogbergH.T. Human IPSC 3D brain model as a tool to study chemical-induced dopaminergic neuronal toxicity.Neurobiol. Dis.202216910571910.1016/j.nbd.2022.10571935398340
    [Google Scholar]
  42. KubickovaB. MartinkovaS. BohaciakovaD. NezvedovaM. LiuR. BrozmanO. SpáčilZ. HilscherovaK. Effects of all-trans and 9-cis retinoic acid on differentiating human neural stem cells in vitro.Toxicology202348715346110.1016/j.tox.2023.15346136805303
    [Google Scholar]
  43. Di ConsiglioE. PistollatoF. Mendoza-De GyvesE. Bal-PriceA. TestaiE. Integrating biokinetics and in vitro studies to evaluate developmental neurotoxicity induced by chlorpyrifos in human iPSC-derived neural stem cells undergoing differentiation towards neuronal and glial cells.Reprod. Toxicol.20209817418810.1016/j.reprotox.2020.09.01033011216
    [Google Scholar]
  44. BaumannM.H. RothmanR.B. Neural and cardiac toxicities associated with 3,4-methylenedioxymethamphetamine (MDMA).Int. Rev. Neurobiol.20098825729610.1016/S0074‑7742(09)88010‑019897081
    [Google Scholar]
  45. CapelaJ.P. FernandesE. RemiãoF. BastosM.L. MeiselA. CarvalhoF. Ecstasy induces apoptosis via 5-HT2A-receptor stimulation in cortical neurons.Neurotoxicology200728486887510.1016/j.neuro.2007.04.00517572501
    [Google Scholar]
  46. MeamarR. KaramaliF. SadeghiH.M. EtebariM. Nasr-EsfahaniM.H. BaharvandH. Toxicity of ecstasy (MDMA) towards embryonic stem cell-derived cardiac and neural cells.Toxicol. In Vitro20102441133113810.1016/j.tiv.2010.03.00520230888
    [Google Scholar]
  47. VangipuramS.D. LymanW.D. Ethanol alters cell fate of fetal human brain-derived stem and progenitor cells.Alcohol. Clin. Exp. Res.20103491574158310.1111/j.1530‑0277.2010.01242.x20586756
    [Google Scholar]
  48. NashR. KrishnamoorthyM. JenkinsA. CseteM. Human embryonic stem cell model of ethanol-mediated early developmental toxicity.Exp. Neurol.2012234112713510.1016/j.expneurol.2011.12.02222227564
    [Google Scholar]
  49. LiebermanR. LevineE.S. KranzlerH.R. AbreuC. CovaultJ. Pilot study of iPS-derived neural cells to examine biologic effects of alcohol on human neurons in vitro.Alcohol. Clin. Exp. Res.201236101678168710.1111/j.1530‑0277.2012.01792.x22486492
    [Google Scholar]
  50. StratmannG. Review article: Neurotoxicity of anesthetic drugs in the developing brain.Anesth. Analg.201111351170117910.1213/ANE.0b013e318232066c21965351
    [Google Scholar]
  51. AdhikariA. AsdaqS.M.B. Al HawajM.A. ChakrabortyM. ThapaG. BhuyanN.R. ImranM. AlshammariM.K. AlshehriM.M. HarshanA.A. AlanaziA. AlhazmiB.D. SreeharshaN. Anticancer drug-induced cardiotoxicity: Insights and pharmacogenetics.Pharmaceuticals (Basel)2021141097010.3390/ph1410097034681194
    [Google Scholar]
  52. MandeniusC.F. AnderssonT.B. AlvesP.M. Batzl-HartmannC. BjörquistP. CarrondoM.J.T. ChesneC. CoeckeS. EdsbaggeJ. FredrikssonJ.M. GerlachJ.C. HeinzleE. Ingelman-SundbergM. JohanssonI. Küppers-MuntherB. Müller-VieiraU. NoorF. ZeilingerK. Toward preclinical predictive drug testing for metabolism and hepatotoxicity by using in vitro models derived from human embryonic stem cells and human cell lines - A report on the Vitrocellomics EU-project.Altern. Lab. Anim.201139214717110.1177/02611929110390021021639679
    [Google Scholar]
  53. JoukarS. A comparative review on heart ion channels, action potentials and electrocardiogram in rodents and human: Extrapolation of experimental insights to clinic.Lab. Anim. Res.20213712510.1186/s42826‑021‑00102‑334496976
    [Google Scholar]
  54. FortinM.C. LaCroixA.S. GrammatopoulosT.N. TanL. WangQ. MancaD. Lower cardiotoxicity of CPX-351 relative to daunorubicin plus cytarabine free-drug combination in hiPSC-derived cardiomyocytes in vitro.Sci. Rep.20231312105410.1038/s41598‑023‑47293‑438030645
    [Google Scholar]
  55. JohnsonB.B. CossonM.V. TsansiziL.I. HolmesT.L. GilmoreT. HamptonK. SongO.R. VoN.T.N. NasirA. ChabronovaA. DenningC. PeffersM.J. MerryC.L.R. WhitelockJ. TroebergL. RushworthS.A. BernardoA.S. SmithJ.G.W. Perlecan (HSPG2) promotes structural, contractile, and metabolic development of human cardiomyocytes.Cell Rep.202443111366810.1016/j.celrep.2023.11366838198277
    [Google Scholar]
  56. LeeJ. GänsweinT. UlusanH. EmmeneggerV. SagunerA.M. DuruF. HierlemannA. Repeated and on-demand intracellular recordings of cardiomyocytes derived from human-induced pluripotent stem cells.ACS Sens.20227103181319110.1021/acssensors.2c0167836166837
    [Google Scholar]
  57. TangX. LiuH. RaoR. HuangY. DongM. XuM. Modeling drug-induced mitochondrial toxicity with human primary cardiomyocytes.Sci. China Life Sci.202467230131937864082
    [Google Scholar]
  58. MatsaE. RajamohanD. DickE. YoungL. MellorI. StaniforthA. DenningC. Drug evaluation in cardiomyocytes derived from human induced pluripotent stem cells carrying a long QT syndrome type 2 mutation.Eur. Heart J.201132895296210.1093/eurheartj/ehr07321367833
    [Google Scholar]
  59. LahtiA.L. KujalaV.J. ChapmanH. KoivistoA.P. Pekkanen-MattilaM. KerkeläE. HyttinenJ. KontulaK. SwanH. ConklinB.R. YamanakaS. SilvennoinenO. Aalto-SetäläK. Model for long QT syndrome type 2 using human iPS cells demonstrates arrhythmogenic characteristics in cell culture.Dis. Model. Mech.20125222023010.1242/dmm.00840922052944
    [Google Scholar]
  60. MandeniusC.F. SteelD. NoorF. MeyerT. HeinzleE. AspJ. ArainS. KraushaarU. BremerS. ClassR. SartipyP. Cardiotoxicity testing using pluripotent stem cell‐derived human cardiomyocytes and state‐of‐the‐art bioanalytics: A review.J. Appl. Toxicol.201131319120510.1002/jat.166321328588
    [Google Scholar]
  61. HwangM. LeeS.J. LimC.H. ShimE.B. LeeH.A. The three-dimensionality of the hiPSC-CM spheroid contributes to the variability of the field potential.Front. Physiol.202314112319010.3389/fphys.2023.112319037025386
    [Google Scholar]
  62. WangP.H. FangY.H. LiuY.W. YehM.L. Merits of hiPSC-derived cardiomyocytes for in vitro research and testing drug toxicity.Biomedicines20221011276410.3390/biomedicines1011276436359284
    [Google Scholar]
  63. VisoneR. Lozano-JuanF. MarzoratiS. RivoltaM.W. PesentiE. RedaelliA. SassiR. RasponiM. OcchettaP. Predicting human cardiac QT alterations and pro-arrhythmic effects of compounds with a 3D beating heart-on-chip platform.Toxicol. Sci.20231911476010.1093/toxsci/kfac10836226800
    [Google Scholar]
  64. Maria CherianR. PrajapatiC. PenttinenK. HäkliM. KoivistoJ.T. Pekkanen-MattilaM. Aalto-SetäläK. Fluorescent hiPSC-derived MYH6-mScarlet cardiomyocytes for real-time tracking, imaging, and cardiotoxicity assays.Cell Biol. Toxicol.202339114516310.1007/s10565‑022‑09742‑035870039
    [Google Scholar]
  65. LeeS.G. KimJ. OhM.S. RyuB. KangK.R. BaekJ. LeeJ.M. ChoiS.O. KimC.Y. ChungH.M. Development and validation of dual-cardiotoxicity evaluation method based on analysis of field potential and contractile force of human iPSC-derived cardiomyocytes/multielectrode assay platform.Biochem. Biophys. Res. Commun.2021555677310.1016/j.bbrc.2021.03.03933813278
    [Google Scholar]
  66. Hortigon-VinagreM.P. ZamoraV. BurtonF.L. SmithG.L. The use of voltage sensitive dye di-4-anepps and video-based contractility measurements to assess drug effects on excitation–contraction coupling in human-induced pluripotent stem cell–derived cardiomyocytes.J. Cardiovasc. Pharmacol.202177328029010.1097/FJC.000000000000093733109927
    [Google Scholar]
  67. OguntuyoK. SchuftanD. GuoJ. SimmonsD. BhagavanD. MorenoJ.D. KangP.W. MillerE. SilvaJ.R. HuebschN. Robust, automated analysis of electrophysiology in induced pluripotent stem cell-derived micro-heart muscle for drug toxicity.Tissue Eng. Part C Methods202228945746810.1089/ten.tec.2022.005335925789
    [Google Scholar]
  68. DubeyV.K. MadanS. RajputS.K. SinghA.T. JaggiM. MittalA.K. Single and repeated dose (28 days) intravenous toxicity assessment of bartogenic acid (an active pentacyclic triterpenoid) isolated from Barringtonia racemosa (L.) fruits in mice.Curr. Res. Toxicol.2022310005710.1016/j.crtox.2021.10.00436504921
    [Google Scholar]
  69. OlsonH. BettonG. StritarJ. RobinsonD. The predictivity of the toxicity of pharmaceuticals in humans from animal data — An interim assessment.Toxicol. Lett.1998102-10353553810.1016/S0378‑4274(98)00261‑610022308
    [Google Scholar]
  70. AmacherD.E. The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity.Toxicol. Appl. Pharmacol.2010245113414210.1016/j.taap.2010.02.01120219512
    [Google Scholar]
  71. HolmgrenG. SjögrenA.K. BarraganI. SabirshA. SartipyP. SynnergrenJ. BjörquistP. Ingelman-SundbergM. AnderssonT.B. EdsbaggeJ. Long-term chronic toxicity testing using human pluripotent stem cell-derived hepatocytes.Drug Metab. Dispos.20144291401140610.1124/dmd.114.05915424980256
    [Google Scholar]
  72. BellC.C. LauschkeV.M. VorrinkS.U. PalmgrenH. DuffinR. AnderssonT.B. Ingelman-SundbergM. Transcriptional, functional, and mechanistic comparisons of stem cell–derived hepatocytes, heparg cells, and three-dimensional human hepatocyte spheroids as predictive in vitro systems for drug-induced liver injury.Drug Metab. Dispos.201745441942910.1124/dmd.116.07436928137721
    [Google Scholar]
  73. UllrichA. StolzD.B. EllisE.C. StromS.C. MichalopoulosG.K. HengstlerJ.G. RungeD. Long term cultures of primary human hepatocytes as an alternative to drug testing in animals.Altern. Anim. Exp.200926429530210.14573/altex.2009.4.29520383475
    [Google Scholar]
  74. BulutogluB. Rey-BedónC. MertS. TianL. JangY.Y. YarmushM.L. UstaO.B. A comparison of hepato-cellular in vitro platforms to study CYP3A4 induction.PLoS One2020152e022910610.1371/journal.pone.022910632106230
    [Google Scholar]
  75. KaurI. VasudevanA. RawalP. TripathiD.M. RamakrishnaS. KaurS. SarinS.K. Primary hepatocyte isolation and cultures: Technical aspects, challenges and advancements.Bioengineering202310213110.3390/bioengineering1002013136829625
    [Google Scholar]
  76. DolléL. StreetzK.L. van GrunsvenL.A. Sharpen your look on liver progenitor cells.Hepatology201255131932110.1002/hep.2472722190378
    [Google Scholar]
  77. FanizzaF. BoeriL. DonnalojaF. PerottoniS. ForloniG. GiordanoC. AlbaniD. Development of an induced pluripotent stem cell-based liver-on-a-chip assessed with an Alzheimer’s Disease Drug.ACS Biomater. Sci. Eng.2023974415443010.1021/acsbiomaterials.3c0034637318190
    [Google Scholar]
  78. BircsakK.M. DeBiasioR. MiedelM. AlsebahiA. ReddingerR. SalehA. ShunT. VernettiL.A. GoughA. A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®.Toxicology202145015266710.1016/j.tox.2020.15266733359578
    [Google Scholar]
  79. ChoiJ.S. JeongI.S. ParkY.J. KimS.W. HGF and IL-10 expressing ALB:GFP reporter cells generated from iPSCs show robust anti-fibrotic property in acute fibrotic liver model.Stem Cell Res. Ther.202011133210.1186/s13287‑020‑01745‑032746905
    [Google Scholar]
  80. ZhangL. PuK. LiuX. BaeS.D.W. NguyenR. BaiS. LiY. QiaoL. The application of induced pluripotent stem cells against liver diseases: An update and a review.Front. Med.2021864459410.3389/fmed.2021.64459434277651
    [Google Scholar]
  81. KnudsenT.B. KellerD.A. SanderM. CarneyE.W. DoerrerN.G. EatonD.L. FitzpatrickS.C. HastingsK.L. MendrickD.L. TiceR.R. WatkinsP.B. WhelanM. FutureTox II: In vitro data and in silico models for predictive toxicology.Toxicol. Sci.2015143225626710.1093/toxsci/kfu23425628403
    [Google Scholar]
  82. HartungT. ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science.Altern. Anim. Exp.202340455957010.14573/altex.230919137889187
    [Google Scholar]
  83. Blanco-GonzálezA. CabezónA. Seco-GonzálezA. Conde-TorresD. Antelo-RiveiroP. PiñeiroÁ. Garcia-FandinoR. The role of AI in drug discovery: Challenges, opportunities, and strategies.Pharmaceuticals202316689110.3390/ph1606089137375838
    [Google Scholar]
  84. LuechtefeldT. MarshD. RowlandsC. HartungT. Machine learning of toxicological big data enables read-across structure activity relationships (rasar) outperforming animal test reproducibility.Toxicol. Sci.2018165119821210.1093/toxsci/kfy15230007363
    [Google Scholar]
  85. SharmaA.K. SrivastavaG.N. RoyA. SharmaV.K. ToxiM. ToxiM. A toxicity prediction tool for small molecules developed using machine learning and chemoinformatics approaches.Front. Pharmacol.2017888010.3389/fphar.2017.0088029249969
    [Google Scholar]
  86. CañadaA. Capella-GutierrezS. RabalO. OyarzabalJ. ValenciaA. KrallingerM. LimTox: A web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.Nucleic Acids Res.201745W1W484W48910.1093/nar/gkx46228531339
    [Google Scholar]
  87. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b0010425860834
    [Google Scholar]
  88. ChengF. LiW. ZhouY. ShenJ. WuZ. LiuG. LeeP.W. TangY. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties.J. Chem. Inf. Model.201252113099310510.1021/ci300367a23092397
    [Google Scholar]
  89. PatlewiczG. JeliazkovaN. SaffordR.J. WorthA.P. AleksievB. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software.SAR QSAR Environ. Res.2008195-649552410.1080/1062936080208387118853299
    [Google Scholar]
  90. XuY. PeiJ. LaiL. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction.J. Chem. Inf. Model.201757112672268510.1021/acs.jcim.7b0024429019671
    [Google Scholar]
  91. TranT.T.V. Surya WibowoA. TayaraH. ChongK.T. Artificial intelligence in drug toxicity prediction: Recent advances, challenges, and future perspectives.J. Chem. Inf. Model.20236392628264310.1021/acs.jcim.3c0020037125780
    [Google Scholar]
  92. KlambauerG. UnterthinerT. MayrA. HochreiterS. DeepTox: Toxicity prediction using deep learning.Toxicol. Lett.2017280S69S6910.1016/j.toxlet.2017.07.175
    [Google Scholar]
  93. VenkatramanV. FP-ADMET: A compendium of fingerprint-based ADMET prediction models.J. Cheminform.20211317510.1186/s13321‑021‑00557‑534583740
    [Google Scholar]
  94. PuL. NaderiM. LiuT. WuH.C. MukhopadhyayS. BrylinskiM. eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates.BMC Pharmacol. Toxicol.2019201210.1186/s40360‑018‑0282‑630621790
    [Google Scholar]
  95. SuR. YangH. WeiL. ChenS. ZouQ. A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data.PLOS Comput. Biol.2022189e101040210.1371/journal.pcbi.101040236070305
    [Google Scholar]
  96. BanerjeeP. EckertA.O. SchreyA.K. PreissnerR. ProTox-II: A webserver for the prediction of toxicity of chemicals.Nucleic Acids Res.201846W1W257W26310.1093/nar/gky31829718510
    [Google Scholar]
  97. WuL. YanB. HanJ. LiR. XiaoJ. HeS. BoX. TOXRIC: A comprehensive database of toxicological data and benchmarks.Nucleic Acids Res.202351D1D1432D144510.1093/nar/gkac107436400569
    [Google Scholar]
  98. MaddahM. MandegarM.A. DameK. GraftonF. LoewkeK. RibeiroA.J.S. Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method.J. Pharmacol. Toxicol. Methods202010510689510.1016/j.vascn.2020.10689532629158
    [Google Scholar]
  99. TopolE.J. High-performance medicine: The convergence of human and artificial intelligence.Nat. Med.2019251445610.1038/s41591‑018‑0300‑730617339
    [Google Scholar]
/content/journals/jctv/10.2174/0129505704335182241218112943
Loading
/content/journals/jctv/10.2174/0129505704335182241218112943
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test