Development of a Novel Microphysiological System for Peripheral Neurotoxicity Prediction Using Human iPSC-Derived...

Area of Science:

• Neuroscience
• Toxicology
• Biotechnology

Background:

• Microphysiological systems (MPS) mimic human physiology for drug screening.
• Drug-induced neurotoxicity, particularly chemotherapy-induced peripheral neuropathy (CIPN), poses a significant clinical challenge.
• Accurate in vitro models are needed to predict and assess neurotoxic effects.

Purpose of the Study:

• To develop and validate a microphysiological system (MPS) for structured culture of human iPSC-derived sensory neurons.
• To predict drug-induced neurotoxicity using morphological deep learning on neurite images.
• To evaluate the platform's efficacy in assessing chemotherapy-induced peripheral neuropathy.

Main Methods:

• Development of an MPS for culturing human induced pluripotent stem cell (iPSC)-derived sensory neurons.
• Administration of anti-cancer drugs to the MPS model.
• Morphological analysis of neuronal soma and axons using a deep learning AI model.
• Measurement of neurofilament light chain expression.

Main Results:

• The AI model successfully detected significant toxicity from anti-cancer drugs.
• The system differentiated between toxic effects on neuronal soma versus axons.
• Observed changes in neurofilament light chain expression correlated with clinical reports of CIPN.
• The MPS-AI platform demonstrated effective prediction of chemotherapy-induced peripheral neuropathy.

Conclusions:

• The developed MPS combined with morphological deep learning is a valuable platform for in vitro peripheral neurotoxicity assessment.
• This approach offers a promising method for early detection and evaluation of drug-induced neurotoxicity.
• The findings support the use of this integrated system for preclinical drug screening and understanding neurotoxic mechanisms.