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Review

Refining in vitro and in silico neurotoxicity approaches by accounting for interspecies and interindividual differences in toxicodynamics

& ORCID Icon
Pages 1007-1017 | Received 03 Nov 2020, Accepted 01 Feb 2021, Published online: 14 Feb 2021

References

  • Ferretti MT, Iulita MF, Cavedo E, et al. Sex differences in Alzheimer disease — the gateway to precision medicine. Nat Rev Neurol. 2018;14(8):457–469.
  • Espay AJ, Brundin P, Lang AE. Precision medicine for disease modification in Parkinson disease. Nat Rev Neurol. 2017;13(2):119–126.
  • Zou Z-Y, Liu C-Y, Che C-H, et al. Toward precision medicine in amyotrophic lateral sclerosis. Ann Transl Med. 2016;4(2):27.
  • Ashley EA. Towards precision medicine. Nat Rev Genet. 2016;17(9):507–522.
  • Strafella C, Caputo V, Galota MR, et al. Application of precision medicine in neurodegenerative diseases. Front Neurol. 2018;9:701.
  • Raikwar SP, Kikkeri NS, Sakuru R, et al. Next generation precision medicine: CRISPR-mediated genome editing for the treatment of neurodegenerative disorders. J Neuroimmune Pharmacol. 2019;14(4):608–641.
  • Dorne JLCM, Renwick AG. The refinement of uncertainty/safety factors in risk assessment by the incorporation of data on toxicokinetic variability in humans. Toxicol Sci. 2005;86(1):20–26.
  • Lehman AJ, Fitzhugh OG. 100-fold margin of safety. Quarterly Bulletin Association of Food & Drug Officials of the United State 1954;18:33–35. s
  • Martin OV, Martin S, Kortenkamp A. Dispelling urban myths about default uncertainty factors in chemical risk assessment – sufficient protection against mixture effects? Environ Health. 2013;12(1):53.
  • EFSA Panel on Contaminants in the Food Chain, Knutsen HK, Alexander J, et al. Risks for public health related to the presence of tetrodotoxin (TTX) and TTX analogues in marine bivalves and gastropods. Efsa J. 2017;15(4):e04752.
  • IPCS. Chemical-specific adjustment factors for interspecies differences and human variability: guidance document for use of data in dose/concentration-response assessment. Geneva: World Health Organization; 2005.
  • Renwick AG. Data‐derived safety factors for the evaluation of food additives and environmental contaminants. Food Addit Contam. 1993;10(3):275–305.
  • Renwick AG, Lazarus NR. Human variability and noncancer risk assessment— an analysis of the default uncertainty factor. Regul Toxicol Pharmacol. 1998;27(1):3–20.
  • Renwick AG. Safety factors and establishment of acceptable daily intakes. Food Addit Contam. 1991;8(2):135–149.
  • Bhat VS, Meek ME, Valcke M, et al. Evolution of chemical-specific adjustment factors (CSAF) based on recent international experience; increasing utility and facilitating regulatory acceptance. Crit Rev Toxicol. 2017;47(9):733–753.
  • Meek ME, Lipscomb JC. Gaining acceptance for the use of in vitro toxicity assays and QIVIVE in regulatory risk assessment. Toxicology. 2015;332:112–123.
  • Hartung T. Evolution of toxicological science: the need for change. IJRAM. 2017;20(1–3):21–45.
  • Meigs L, Smirnova L, Rovida C, et al. Animal testing and its alternatives - the most important omics is economics. ALTEX. 2018;35(3):275–305.
  • Bottini AA, Hartung T. Food for thought … on the economics of animal testing. ALTEX. 2009;26(1):3–16.
  • Hartung T. Food for thought … on animal tests. ALTEX. 2008;25(1):3–16.
  • Leist M, Hartung T, Nicotera P. The dawning of a new age of toxicology. ALTEX. 2008;25(2):103–114.
  • Williams ES, Panko J, Paustenbach DJ. The European union’s REACH regulation: a review of its history and requirements. Crit Rev Toxicol. 2009;39(7):553–575.
  • Punt A, Bouwmeester H, Blaauboer BJ, et al. New approach methodologies (NAMs) for human-relevant biokinetics predictions: meeting the paradigm shift in toxicology towards an animal-free chemical risk assessment. ALTEX. 2020;37(4):607–622.
  • Kasteel EEJ, Westerink RHS. Comparison of the acute inhibitory effects of Tetrodotoxin (TTX) in rat and human neuronal networks for risk assessment purposes. Toxicol Lett. 2017;270:12–16.
  • Hondebrink L, Kasteel EEJ, Tukker AM, et al. Neuropharmacological characterization of the new psychoactive substance methoxetamine. Neuropharmacology. 2017;123:1–9.
  • Grskovic M, Javaherian A, Strulovici B, et al. Induced pluripotent stem cells — opportunities for disease modelling and drug discovery. Nat Rev Drug Discov. 2011;10(12):915–929.
  • Scott CW, Peters MF, Dragan YP. Human induced pluripotent stem cells and their use in drug discovery for toxicity testing. Toxicol Lett. 2013;219(1):49–58.
  • Anson BD, Kolaja KL, Kamp TJ. Opportunities for use of human iPS cells in predictive toxicology. Clin Pharmacol Ther. 2011;89(5):754–758.
  • Caballero MV, Espinoza-Lewis RA, Candiracci M. Application of stem cells and ips cells in toxicology. In: Sahu SC, editor. Stem cells in toxicology and medicine. Chichester, UK; Hoboken: NJ: John WIley & Sons; 2016. p. 5–25.
  • JAG A, Jiménez-Jiménez FJ, Alonso-Navarro H, et al. Drug and xenobiotic biotransformation in the blood-brain barrier: a neglected issue. Front Cell Neurosci. 2014;8:335.
  • MWGDM DG, Westerink RHS, Dingemans MML. Don’t judge a neuron only by its cover: neuronal function in in vitro developmental neurotoxicity testing. Toxicol Sci. 2013;132(1):1–7.
  • Bal-Price A, Hogberg HT, Crofton KM, et al. Recommendation on test readiness criteria for new approach methods in toxicology: exemplified for developmental neurotoxicity. ALTEX. 2018;35(3):306–352.
  • Westerink RHS. Do we really want to REACH out to in vitro? Neurotoxicology. 2013;39:169–172.
  • Tukker AM, MWGDM DG, Wijnolts FMJ, et al. Is the time right for in vitro neurotoxicity testing using human iPSC-derived neurons? ALTEX. 2016;33(3):261–271.
  • Johnstone AFM, Gross GW, Weiss DG, et al. Microelectrode arrays: a physiologically based neurotoxicity testing platform for the 21st century. Neurotoxicology. 2010;31(4):331–350.
  • Tukker AM, Wijnolts FMJ, de Groot A, et al. Human iPSC-derived neuronal models for in vitro neurotoxicity assessment. Neurotoxicology. 2018;67:215–225.
  • Dingemans MML, Schütte MG, Wiersma DMM, et al. Chronic 14-day exposure to insecticides or methylmercury modulates neuronal activity in primary rat cortical cultures. Neurotoxicology. 2016;57:194–202.
  • Tukker AM, Wijnolts FMJ, de Groot A, et al. Applicability of hiPSC-derived neuronal co-cultures and rodent primary cortical cultures for in vitro seizure liability assessment. Toxicol Sci. 2020;178(1):71-87
  • Hondebrink L, Verboven AHA, Drega WS, et al. Neurotoxicity screening of (illicit) drugs using novel methods for analysis of microelectrode array (MEA) recordings. Neurotoxicology. 2016;55:1–9.
  • Tukker AM, Bouwman LMS, van Kleef R, et al. Perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) acutely affect human α(1)β(2)γ(2L) GABA(A) receptor and spontaneous neuronal network function in vitro. Sci Rep. 2020;10(1):5311.
  • Tukker AM, Van Kleef R, Wijnolts FMJ, et al. Towards animal-free neurotoxicity screening: applicability of hiPSC-derived neuronal models for in vitro seizure liability assessment. ALTEX. 2020;37(1):121–135.
  • Hibell AD, Kidd EJ, Chessell IP, et al. Apparent species differences in the kinetic properties of P2X7 receptors. Br J Pharmacol. 2000;130(1):167–173.
  • Tuan EW, Horti AG, Olson TT, et al. AT-1001 is a partial agonist with high affinity and selectivity at human and rat α3β4 nicotinic cholinergic receptors. Mol Pharmacol. 2015;88(4):640–649.
  • Lee S-H, Rhee J, Koh J-K, et al. Species differences in functions of dopamine transporter: paucity of MPP+ uptake and cocaine binding in bovine dopamine transporter. Neurosci Lett. 1996;214(2):199–201.
  • Vermeulen RJ, Jongenelen CAM, Langeveld CH, et al. Dopamine D1 receptor agonists display a different intrinsic activity in rat, monkey and human astrocytes. Eur J Pharmacol. 1994;269(1):121–125.
  • Goldoni M, Vittoria Vettori M, Alinovi R, et al. Models of neurotoxicity: extrapolation of benchmark doses in vitro. Risk Anal. 2003;23(3):505–514.
  • McPartland JM, Glass M, Pertwee RG. Meta-analysis of cannabinoid ligand binding affinity and receptor distribution: interspecies differences. Br J Pharmacol. 2007;152(5):583–593.
  • Axelrad DA, Setzer RW, Bateson TF, et al. Methods for evaluating variability in human health dose–response characterization. Hum Ecol Risk Assess. 2020;26(7):1755–1778.
  • Abdo N, Xia M, Brown CC, et al. Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study. Environ Health Perspect. 2015;123(5):458–466.
  • Kumar KK, Aboud AA, Bowman AB. The potential of induced pluripotent stem cells as a translational model for neurotoxicological risk. Neurotoxicology. 2012;33(3):518–529.
  • Woodard Chris M, Campos Brian A, Kuo S-H, et al. iPSC-derived dopamine neurons reveal differences between monozygotic twins discordant for parkinson’s disease. Cell Rep. 2014;9(4):1173–1182.
  • Chung SY, Kishinevsky S, Mazzulli JR, et al. Parkin and PINK1 patient iPSC-derived midbrain dopamine neurons exhibit mitochondrial dysfunction and α-synuclein accumulation. Stem Cell Rep. 2016;7(4):664–677.
  • Kasteel EEJ, Nijmeijer SM, Darney K, et al. Acetylcholinesterase inhibition in electric eel and human donor blood: an in vitro approach to investigate interspecies differences and human variability in toxicodynamics. Arch Toxicol. 2020;94:4055-4065
  • Harrill AH, McAllister KA. New rodent population models may inform human health risk assessment and identification of genetic susceptibility to environmental exposures. Environ Health Perspect. 2017;125(8):086002.
  • Terrazzino S, Argyriou AA, Cargnin S, et al. Genetic determinants of chronic oxaliplatin-induced peripheral neurotoxicity: a genome-wide study replication and meta-analysis. J Peripher Nerv Syst. 2015;20(1):15–23.
  • Cronin MTD. Quantitative structure-Activity relationship (QSAR) analysis of the acute sublethal neurotoxicity of solvents. Toxicol In Vitro. 1996;10(2):103–110.
  • Worth A, Fuart-Gatnik M, Lapenna S. et al. Applicability of QSAR analysis in the evaluation of developmental and neurotoxicity effects for the assessment of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment. EFSA Supp Publ. 2011;8(6):169E.
  • Chushak YG, Shows HW, Gearhart JM, et al. In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox. Toxicol Res. 2018;7(3):423–431.
  • Antanasijević D, Antanasijević J, Trišović N, et al. From classification to regression multitasking QSAR modeling using a novel modular neural network: simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides. Mol Pharm. 2017;14(12):4476–4484.
  • Bal-Price A, Pistollato F, Sachana M, et al. Strategies to improve the regulatory assessment of developmental neurotoxicity (DNT) using in vitro methods. Toxicol Appl Pharmacol. 2018;354:7–18.
  • Estrada E, Molina E, Uriarte E. Quantitative structure-toxicity relationships using tops-mode. 2. neurotoxicity of a non-congeneric series of solvents. SAR QSAR Environ Res. 2001;12(5):445–459.
  • Crofton KM. A structure-activity relationship for the neurotoxicity of triazole fungicides. Toxicol Lett. 1996;84(3):155–159.
  • Pessah IN, Hansen LG, Albertson TE, et al. Structure−activity relationship for noncoplanar polychlorinated biphenyl congeners toward the ryanodine receptor-Ca2+ channel complex type 1 (RyR1). Chem Res Toxicol. 2006;19(1):92–101.
  • Stenberg M, Hamers T, Machala M, et al. Multivariate toxicity profiles and QSAR modeling of non-dioxin-like PCBs – an investigation of in vitro screening data from ultra-pure congeners. Chemosphere. 2011;85(9):1423–1429.
  • Yazal JE, Rao SN, Mehl A, et al. Prediction of organophosphorus acetylcholinesterase inhibition using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Toxicol Sci. 2001;63(2):223–232.
  • Das RN, Roy K, Popelier PLA. Interspecies quantitative structure–toxicity–toxicity (QSTTR) relationship modeling of ionic liquids. Toxicity of ionic liquids to V. fischeri, D. magna and S. vacuolatus. Ecotoxicol Environ Saf. 2015;122:497–520.
  • Hao Y, Sun G, Fan T, et al. In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse. J Hazard Mater. 2020;399:122981.
  • Kamal NNSBNM, Lim TS, Tye GJ, et al. The effect of cyp2b6, cyp2d6, and cyp3a4 alleles on methadone binding: a molecular docking study. J Chem. 2013;2013:249642.
  • Choughule KV, Joswig-Jones CA, Jones JP. Interspecies differences in the metabolism of methotrexate: an insight into the active site differences between human and rabbit aldehyde oxidase. Biochem Pharmacol. 2015;96(3):288–295.
  • Vinken M. The adverse outcome pathway concept: a pragmatic tool in toxicology. Toxicology. 2013;312:158–165.
  • Ankley GT, Bennett RS, Erickson RJ, et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 2010;29(3):730–741.
  • Vinken M. Taking adverse outcome pathways to the next level. Toxicol In Vitro. 2018;50:A1–A2.
  • Villeneuve DL, Crump D, Garcia-Reyero N, et al. Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci. 2014;142(2):312–320.
  • Conolly RB, Ankley GT, Cheng W, et al. Quantitative adverse outcome pathways and their application to predictive toxicology. Environ Sci Technol. 2017;51(8):4661–4672.
  • Zgheib E, Gao W, Limonciel A, et al. Application of three approaches for quantitative AOP development to renal toxicity. Comput Toxicol. 2019;11:1–13.
  • OECD. Guidance Document for the Use of Adverse Outcome Pathways in Developing Integrated Approaches to Testing and Assessment (IATA). 2017.
  • Bal-Price A, Lein PJ, Keil KP, et al. Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity. Neurotoxicology. 2017;59:240–255.
  • Spinu N, Bal-Price A, Cronin MTD, et al. Development and analysis of an adverse outcome pathway network for human neurotoxicity. Arch Toxicol. 2019;93(10):2759–2772.
  • Sewell F, Gellatly N, Beaumont M, et al. The future trajectory of adverse outcome pathways: a commentary. Arch Toxicol. 2018;92(4):1657–1661.
  • Leist M, Ghallab A, Graepel R, et al. Adverse outcome pathways: opportunities, limitations and open questions. Arch Toxicol. 2017;91(11):3477–3505.
  • Dent M, Amaral RT, Da Silva PA, et al. Principles underpinning the use of new methodologies in the risk assessment of cosmetic ingredients. Comput Toxicol. 2018;7:20–26.
  • Fay KA, Villeneuve DL, Swintek J, et al. Differentiating pathway-specific from nonspecific effects in high-throughput toxicity data: a foundation for prioritizing adverse outcome pathway development. Toxicol Sci. 2018;163(2):500–515.
  • Calabrese EJ. Uncertainty factors and interindividual variation. Regul Toxicol Pharmacol. 1985;5(2):190–196.
  • Dankovic DA, Naumann BD, Maier A, et al. The scientific basis of uncertainty factors used in setting occupational exposure limits. J Occup Environ Hyg. 2015;12(Suppl 1(sup1)):S55–68.
  • Baird SJS, Cohen JT, Graham JD, et al. Noncancer risk assessment: a probabilistic alternative to current practice. Hum Ecol Risk Assess. 1996;2(1):79–102.
  • Gellatly N, Sewell F. Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances. Comput Toxicol. 2019;11:82–89.
  • Punt A. Toxicokinetics in risk evaluations. Chem Res Toxicol. 2018;31(5):285–286.
  • Dybing E, Søderlund EJ. Situations with enhanced chemical risks due to toxicokinetic and toxicodynamic factors. Regul Toxicol Pharmacol. 1999;30(2):S27–S30.
  • Hahn MK, Blakely RD. Monoamine transporter gene structure and polymorphisms in relation to psychiatric and other complex disorders. Pharmacogenomics J. 2002;2(4):217–235.
  • Willeit M, Praschak-Rieder N. Imaging the effects of genetic polymorphisms on radioligand binding in the living human brain: a review on genetic neuroreceptor imaging of monoaminergic systems in psychiatry. Neuroimage. 2010;53(3):878–892.