Assessment of a 3D neural spheroid model to detect pharmaceutical-induced neurotoxicity

Main Article Content

Qin Wang, Jennifer D. Cohen, Tomoya Yukawa, Heather Estrella, Chris Leonard, Jairo Nunes, Colin Choi, Nibha Mishra, Lauren Lewis, Kevin S. Baker, Kazuhiro Kuga, Yvonne P. Dragan, Matthew P. Wagoner
[show affiliations]


Drug-induced neurotoxicity is a leading cause of safety-related attrition for therapeutics in clinical trials, often driven by poor predictivity of preclinical in vitro and in vivo models of neurotoxicity. Over a dozen different iPSC-derived 3D spheroids have been described in recent years, but their ability to predict neurotoxicity in patients has not been evaluated nor compared with the predictive power of nonclinical species. To assess the predictive capabilities of human iPSCderived neural spheroids (microBrains), we used 84 structurally diverse pharmaceuticals with robust clinical and preclinical datasets with varying degrees of seizurogenic and neurodegenerative liability. Drug-induced changes in neural viability and phenotypic calcium bursts were assessed using 7 endpoints based on calcium oscillation profiles and cellular ATP levels. These endpoints, normalized by therapeutic exposure, were used to build logistic regression models to establish endpoint cutoffs and evaluate probability for clinical neurotoxicity. The neurotoxicity score calculated from the logistic regression model could distinguish neurotoxic from non-neurotoxic clinical molecules with a specificity as high as 93.33% and a sensitivity of 53.49%, demonstrating a very low false positive rate for the prediction of seizures, convulsions, and neurodegeneration. In contrast, nonclinical species showed a higher sensitivity (75%) but much lower specificity (30.4%). The neural spheroids demonstrated higher likelihood ratio positive and inverse likelihood ratio negative values compared with nonclinical safety studies. This assay has the potential to be used as a predictive assay to detect neurotoxicity in early drug discovery, aiding in the early identification of compounds that eventually may fail due to neurotoxicity.

Article Details

How to Cite
Wang, Q. (2022) “Assessment of a 3D neural spheroid model to detect pharmaceutical-induced neurotoxicity”, ALTEX - Alternatives to animal experimentation, 39(4), pp. 560–582. doi: 10.14573/altex.2112221.

Accardi, M. V., Huang, H. and Authier, S. (2018). Seizure liability assessments using the hippocampal tissue slice: Comparison of non-clinical species. J Pharmacol Toxicol Methods 93, 59-68. doi:10.1016/j.vascn.2017.11.003

Ben-Ari, Y. (2001). Developing networks play a similar melody. Trends Neurosci 24, 353-360. doi:10.1016/s0166-2236(00)01813-0

Bradley, J. A., Luithardt, H. H., Metea, M. R. et al. (2018). In vitro screening for seizure liability using microelectrode array technology. Toxicol Sci 163, 240-253. doi:10.1093/toxsci/kfy029

Chaicharoenaudomrung, N., Kunhorm, P. and Noisa, P. (2019). Three-dimensional cell culture systems as an in vitro platform for cancer and stem cell modeling. World J Stem Cells 11, 1065-1083. doi:10.4252/wjsc.v11.i12.1065

Chen, M., Borlak, J. and Tong, W. (2013). High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology 58, 388-396. doi:10.1002/hep.26208

Chen, Y., Lun, A. T. and Smyth, G. K. (2016). From reads to genes to pathways: Differential expression analysis of RNA-seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 5, 1438. doi:10.12688/f1000research.8987.2

Cohen, E., Ivenshitz, M., Amor-Baroukh, V. et al. (2008). Determinants of spontaneous activity in networks of cultured hippocampus. Brain Res 1235, 21-30. doi:10.1016/j.brainres.2008.06.022

Cook, D., Brown, D., Alexander, R. et al. (2014). Lessons learned from the fate of Astrazeneca’s drug pipeline: A five-dimensional framework. Nat Rev Drug Discov 13, 419-431. doi:10.1038/nrd4309

Czapinska-Ciepiela, E. (2017). The risk of epileptic seizures during antibiotic therapy [Article in Polish]. Wiad Lek 70, 820-826.

Dolphin, A. C. (2018). Voltage-gated calcium channel α (2)δ subunits: An assessment of proposed novel roles. F1000Res 7, doi:10.12688/f1000research.16104.1

Drever, B. D., Riedel, G. and Platt, B. (2011). The cholinergic system and hippocampal plasticity. Behav Brain Res 221, 505-514. doi:10.1016/j.bbr.2010.11.037

Easter, A., Sharp, T. H., Valentin, J. P. et al. (2007). Pharmacological validation of a semi-automated in vitro hippocampal brain slice assay for assessment of seizure liability. J Pharmacol Toxicol Methods 56, 223-233. doi:10.1016/j.vascn.2007.04.008

Easter, A., Bell, M. E., Damewood, J. R., Jr. et al. (2009). Approaches to seizure risk assessment in preclinical drug discovery. Drug Discov Today 14, 876-884. doi:10.1016/j.drudis.2009.06.003

Fan, J., Thalody, G., Kwagh, J. et al. (2019). Assessing seizure liability using multi-electrode arrays (MEA). Toxicol In Vitro 55, 93-100. doi:10.1016/j.tiv.2018.12.001

Fang, H., Harris, S. C., Liu, Z. et al. (2016). FDA drug labeling: Rich resources to facilitate precision medicine, drug safety, and regulatory science. Drug Discov Today 21, 1566-1570. doi:10.1016/j.drudis.2016.06.006

Federal Register (2001). S7A Safety Pharmacology Studies for Human Pharmaceuticals.

Frank, C. L., Brown, J. P., Wallace, K. et al. (2017). From the cover: Developmental neurotoxicants disrupt activity in cortical networks on microelectrode arrays: Results of screening 86 compounds during neural network formation. Toxicol Sci 160, 121-135. doi:10.1093/toxsci/kfx169

Grimm, F. A., Iwata, Y., Sirenko, O. et al. (2015). High-content assay multiplexing for toxicity screening in induced pluripotent stem cell-derived cardiomyocytes and hepatocytes. Assay Drug Dev Technol 13, 529-546. doi:10.1089/adt.2015.659

Gulyas, A. I., Acsady, L. and Freund, T. F. (1999). Structural basis of the cholinergic and serotonergic modulation of GABAergic neurons in the hippocampus. Neurochem Int 34, 359-372. doi:10.1016/s0197-0186(99)00041-8

Gunness, P., Mueller, D., Shevchenko, V. et al. (2013). 3D organotypic cultures of human HepaRG cells: A tool for in vitro toxicity studies. Toxicol Sci 133, 67-78. doi:10.1093/toxsci/kft021

Heblich, F., Tran Van Minh, A., Hendrich, J. et al. (2008). Time course and specificity of the pharmacological disruption of the trafficking of voltage-gated calcium channels by gabapentin. Channels (Austin) 2, 4-9. doi:10.4161/chan.2.1.6045

Ishii, M. N., Yamamoto, K., Shoji, M. et al. (2017). Human induced pluripotent stem cell (hiPSC)-derived neurons respond to convulsant drugs when co-cultured with hiPSC-derived astrocytes. Toxicology 389, 130-138. doi:10.1016/j.tox.2017.06.010

Kanat, O., Ertas, H. and Caner, B. (2017). Platinum-induced neurotoxicity: A review of possible mechanisms. World J Clin Oncol 8, 329-335. doi:10.5306/wjco.v8.i4.329

Khazipov, R. and Luhmann, H. J. (2006). Early patterns of electrical activity in the developing cerebral cortex of humans and rodents. Trends Neurosci 29, 414-418. doi:10.1016/j.tins.2006.05.007

Kilic, O., Pamies, D., Lavell, E. et al. (2016). Brain-on-a-chip model enables analysis of human neuronal differentiation and chemotaxis. Lab Chip 16, 4152-4162. doi:10.1039/c6lc00946h

Kim, J. B. (2005). Three-dimensional tissue culture models in cancer biology. Semin Cancer Biol 15, 365-377. doi:10.1016/j.semcancer.2005.05.002

Kreir, M., Van Deuren, B., Versweyveld, S. et al. (2018). Do in vitro assays in rat primary neurons predict drug-induced seizure liability in humans? Toxicol Appl Pharmacol 346, 45-57. doi:10.1016/j.taap.2018.03.028

Kreir, M., De Bondt, A., Van den Wyngaert, I. et al. (2019). Role of Kv7.2/Kv7.3 and M1 muscarinic receptors in the regulation of neuronal excitability in hiPSC-derived neurons. Eur J Pharmacol 858, 172474. doi:10.1016/j.ejphar.2019.172474

Kuijlaars, J., Oyelami, T., Diels, A. et al. (2016). Sustained synchronized neuronal network activity in a human astrocyte co-culture system. Sci Rep 6, 36529. doi:10.1038/srep36529

Lancaster, M. A., Renner, M., Martin, C. A. et al. (2013). Cerebral organoids model human brain development and microcephaly. Nature 501, 373-379. doi:10.1038/nature12517

Lancaster, M. A. and Knoblich, J. A. (2014). Generation of cerebral organoids from human pluripotent stem cells. Nat Protoc 9, 2329-2340. doi:10.1038/nprot.2014.158

Leite, P. E. C., Pereira, M. R., Harris, G. et al. (2019). Suitability of 3D human brain spheroid models to distinguish toxic effects of gold and poly-lactic acid nanoparticles to assess biocompatibility for brain drug delivery. Part Fibre Toxicol 16, 22. doi:10.1186/s12989-019-0307-3

Lenz, M., Muller, F. J., Zenke, M. et al. (2016). Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data. Sci Rep 6, 25696. doi:10.1038/srep25696

Lukk, M., Kapushesky, M., Nikkila, J. et al. (2010). A global map of human gene expression. Nat Biotechnol 28, 322-324. doi:10.1038/nbt0410-322

Luo, C., Lancaster, M. A., Castanon, R. et al. (2016). Cerebral organoids recapitulate epigenomic signatures of the human fetal brain. Cell Rep 17, 3369-3384. doi:10.1016/j.celrep.2016.12.001

Mark, R. B. and Don, J. M. (2007). Drug-induced disorders of the nervous system. Clin Med (Lond) 7, 170-176. doi:10.7861/clinmedicine.7-2-170

Mathiasen, J. R. and Moser, V. C. (2018). The Irwin test and functional observational battery (FOB) for assessing the effects of compounds on behavior, physiology, and safety pharmacology in rodents. Curr Protoc Pharmacol 83, e43. doi:10.1002/cpph.43

Maurer, T. S., Debartolo, D. B., Tess, D. A. et al. (2005). Relationship between exposure and nonspecific binding of thirty-three central nervous system drugs in mice. Drug Metab Dispos 33, 175-181. doi:10.1124/dmd.104.001222

Mead, A. N., Amouzadeh, H. R., Chapman, K. et al. (2016). Assessing the predictive value of the rodent neurofunctional assessment for commonly reported adverse events in phase I clinical trials. Regul Toxicol Pharmacol 80, 348-357. doi:10.1016/j.yrtph.2016.05.002

Mongan, M., Tan, Z., Chen, L. et al. (2008). Mitogen-activated protein kinase kinase kinase 1 protects against nickel-induced acute lung injury. Toxicol Sci 104, 405-411. doi:10.1093/toxsci/kfn089

Monticello, T. M., Jones, T. W., Dambach, D. M. et al. (2017). Current nonclinical testing paradigm enables safe entry to first-in-human clinical trials: The IQ consortium nonclinical to clinical translational database. Toxicol Appl Pharmacol 334, 100-109. doi:10.1016/j.taap.2017.09.006

Moscardo, E., Maurin, A., Dorigatti, R. et al. (2007). An optimised methodology for the neurobehavioural assessment in rodents. J Pharmacol Toxicol Methods 56, 239-255. doi:10.1016/j.vascn.2007.03.007

Nagayama, T. (2015). Adverse drug reactions for medicine newly approved in japan from 1999 to 2013: Syncope/loss of consciousness and seizures/convulsions. Regul Toxicol Pharmacol 72, 572-577. doi:10.1016/j.yrtph.2015.05.030

Nielsen, E. and Brant, J. (2002). Chemotherapy-induced neurotoxicity: Assessment and interventions for patients at risk. Am J Nurs 102, Suppl 4, 16-19; quiz 49-52.

Olson, H., Betton, G., Robinson, D. et al. (2000). Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32, 56-67. doi:10.1006/rtph.2000.1399

Plotkin, S. R. and Wen, P. Y. (2003). Neurologic complications of cancer therapy. Neurol Clin 21, 279-318. doi:10.1016/s0733-8619(02)00034-8

Proctor, W. R., Foster, A. J., Vogt, J. et al. (2017). Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury. Arch Toxicol 91, 2849-2863. doi:10.1007/s00204-017-2002-1

Przedborski, S., Chen, Q., Vila, M. et al. (2001). Oxidative post-translational modifications of alpha-synuclein in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of Parkinson’s disease. J Neurochem 76, 637-640. doi:10.1046/j.1471-4159.2001.00174.x

Qian, X., Nguyen, H. N., Song, M. M. et al. (2016). Brain-region-specific organoids using mini-bioreactors for modeling ZIKV exposure. Cell 165, 1238-1254. doi:10.1016/j.cell.2016.04.032

Qian, X., Jacob, F., Song, M. M. et al. (2018). Generation of human brain region-specific organoids using a miniaturized spinning bioreactor. Nat Protoc 13, 565-580. doi:10.1038/nprot.2017.152

Renner, M., Lancaster, M. A., Bian, S. et al. (2017). Self-organized developmental patterning and differentiation in cerebral organoids. EMBO J 36, 1316-1329. doi:10.15252/embj.201694700

Robinson, M. D. and Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11, R25. doi:10.1186/gb-2010-11-3-r25

Ross, J. F. (2000). ECOs, FOBs, and UFOs: Making sense of observational data. Toxicol Pathol 28, 132-136. doi:10.1177/019262330002800116

Ryan, K. R., Sirenko, O., Parham, F. et al. (2016). Neurite outgrowth in human induced pluripotent stem cell-derived neurons as a high-throughput screen for developmental neurotoxicity or neurotoxicity. Neurotoxicology 53, 271-281. doi:10.1016/j.neuro.2016.02.003

Schwartz, M. P., Hou, Z., Propson, N. E. et al. (2015). Human pluripotent stem cell-derived neural constructs for predicting neural toxicity. Proc Natl Acad Sci U S A 112, 12516-12521. doi:10.1073/pnas.1516645112

Shah, F., Leung, L., Barton, H. A. et al. (2015). Setting clinical exposure levels of concern for drug-induced liver injury (DILI) using mechanistic in vitro assays. Toxicol Sci 147, 500-514. doi:10.1093/toxsci/kfv152

Silva, L. R., Amitai, Y. and Connors, B. W. (1991). Intrinsic oscillations of neocortex generated by layer 5 pyramidal neurons. Science 251, 432-435. doi:10.1126/science.1824881

Sirenko, O., Crittenden, C., Callamaras, N. et al. (2013). Multiparameter in vitro assessment of compound effects on cardiomyocyte physiology using iPSC cells. J Biomol Screen 18, 39-53. doi:10.1177/1087057112457590

Sirenko, O., Mitlo, T., Hesley, J. et al. (2015). High-content assays for characterizing the viability and morphology of 3S cancer spheroid cultures. Assay Drug Dev Technol 13, 402-414. doi:10.1089/adt.2015.655

Sirenko, O., Hancock, M. K., Hesley, J. et al. (2016). Phenotypic characterization of toxic compound effects on liver spheroids derived from iPSC using confocal imaging and three-dimensional image analysis. Assay Drug Dev Technol 14, 381-394. doi:10.1089/adt.2016.729

Sirenko, O., Parham, F., Dea, S. et al. (2019). Functional and mechanistic neurotoxicity profiling using human iPSC-derived neural 3D cultures. Toxicol Sci 167, 58-76. doi:10.1093/toxsci/kfy218

Slavin, I., Dea, S., Arunkumar, P. et al. (2021). Human iPSC-derived 2D and 3D platforms for rapidly assessing developmental, functional, and terminal toxicities in neural cells. Int J Mol Sci 22, doi:10.3390/ijms22041908

Smith, S. J. (2005). EEG in the diagnosis, classification, and management of patients with epilepsy. J Neurol Neurosurg Psychiatry 76, Suppl 2, ii2-7. doi:10.1136/jnnp.2005.069245

Stone, J. B. and DeAngelis, L. M. (2016). Cancer-treatment-induced neurotoxicity – Focus on newer treatments. Nat Rev Clin Oncol 13, 92-105. doi:10.1038/nrclinonc.2015.152

Sul, J. K. and Deangelis, L. M. (2006). Neurologic complications of cancer chemotherapy. Semin Oncol 33, 324-332. doi:10.1053/j.seminoncol.2006.03.006

Switzer, R. C. (2011). Fundamentals of neurotoxicity testing. In B. Bolon and M. T. Butt (eds.), Fundamental Neuropathology for Pathologists and Toxicologists: Principles and Techniques. Hoboken, NJ, USA: John Wiley & Sons, Inc.

Tontodonati, M., Fasdelli, N., Moscardo, E. et al. (2007). A canine model used to simultaneously assess potential neurobehavioural and cardiovascular effects of candidate drugs. J Pharmacol Toxicol Methods 56, 265-275. doi:10.1016/j.vascn.2007.03.005

Valdivia, P., Martin, M., LeFew, W. R. et al. (2014). Multi-well microelectrode array recordings detect neuroactivity of toxcast compounds. Neurotoxicology 44, 204-217. doi:10.1016/j.neuro.2014.06.012

Valentin, J. P. and Hammond, T. (2008). Safety and secondary pharmacology: Successes, threats, challenges and opportunities. J Pharmacol Toxicol Methods 58, 77-87. doi:10.1016/j.vascn.2008.05.007

van Esbroeck, A. C. M., Janssen, A. P. A., Cognetta, A. B., 3rd et al. (2017). Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474. Science 356, 1084-1087. doi:10.1126/science.aaf7497

Verstraelen, P., Pintelon, I., Nuydens, R. et al. (2014). Pharmacological characterization of cultivated neuronal networks: Relevance to synaptogenesis and synaptic connectivity. Cell Mol Neurobiol 34, 757-776. doi:10.1007/s10571-014-0057-6

Walker, A. L., Imam, S. Z. and Roberts, R. A. (2018). Drug discovery and development: Biomarkers of neurotoxicity and neurodegeneration. Exp Biol Med (Maywood) 243, 1037-1045. doi:10.1177/1535370218801309

Winter, M. J., Redfern, W. S., Hayfield, A. J. et al. (2008). Validation of a larval zebrafish locomotor assay for assessing the seizure liability of early-stage development drugs. J Pharmacol Toxicol Methods 57, 176-187. doi:10.1016/j.vascn.2008.01.004

Woodruff, G., Phillips, N., Carromeu, C. et al. (2020). Screening for modulators of neural network activity in 3D human iPSC-derived cortical spheroids. PLoS One 15, e0240991. doi:10.1371/journal.pone.0240991

Most read articles by the same author(s)