Choosing the Optimal Model for Assessing In Vitro Neurotoxicity

The CNS is a complex organ system and neurotoxicity can be challenging to detect during preclinical development in animal toxicology studies. Highly sensitive cell-based models are now available and being routinely used as an early assessment of neurotoxic liability. Through microelectrode array (MEA) analysis, firing and burst patterns of neurons are monitored and these can be translated into specific in vivo responses.

At the 61st Annual Society of Toxicology Meeting (SOT) and Tox Expo from March 27-31, 2022, we presented our poster titled ‘Identifying Seizures with MEA: Complementary Human and Rat Neuronal Models Enhance Predictivity’. Our research has focused on the comparison between rat cortical neurons and a co-culture of human iPSC-derived glutamatergic neurons with iPSC-derived astrocytes in their response to various known neurotoxic and seizurogenic compounds using MEA analysis.

We found the rat cortical neuron model to be highly effective at detecting sub-cytotoxic neurotoxic and seizurogenic responses with specific patterns observed for particular compound classes with potential for identifying target driven responses. However, one limitation of the rat model is its inability to detect the muscarinic receptor agonist pilocarpine when testing up at 14-18 DIV.

In comparison, the human iPSC-derived co-culture model demonstrates a highly active and organised network and, like the rat model, provides a valuable insight into neurotoxic and seizurogenic potential at sub cytotoxic levels. However, the human model matures differently to the rat model with spontaneous spike activity being observed much earlier. Interestingly, the human co-culture model is able to detect chemical agents targeting the muscarinic receptor, unlike the rat model, but fails to identify GABAA antagonists definitively which are consistently picked up by the rat model.

It is important to note that over the two models, response patterns are typically complementary and, when both models are used together, a robust and accurate assessment of CNS liability can be achieved.

To learn more, download our poster.

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