Drug screen and machine learning predict neuroprotective agents in a preclinical human model of childhood dementia

Childhood dementias are a group of paediatric neurodegenerative disorders characterised by neurocognitive decline, and in many cases underpinned by pathophysiological mechanisms similar to adult-onset dementias. In this study, we use patient-derived induced pluripotent stem cells (iPSCs) from children with one of the most prevalent childhood dementias, Mucopolysaccharidosis Type IIIA (MPS IIIA), also known as Sanfilippo syndrome. The derived cortical cultures exhibit lysosomal dysfunction, heparan sulfate accumulation, progressive neurodegeneration and astrocytic reactivity, recapitulating prototypical in-vivo phenotypes. Using a multimodal drug screening platform that integrates machine learning, high-content confocal imaging, single-nuclei transcriptomics and electrophysiology, we identify at least nine repurposed compounds that significantly mitigate these adverse effects within two weeks of treatment in vitro, demonstrating potential for rapid clinical translation. This human preclinical model for MPS IIIA, coupled with a robust multimodal therapeutic interrogation platform, serves as an exemplar for advancing drug discovery for childhood dementias and the broader neurodegenerative disease spectrum. Here authors show patient-derived neural models of childhood dementia exhibit key disease features. A drug screening platform integrating machine learning, imaging, transcriptomics and electrophysiology, identifies repurposed compounds that mitigate adverse effects.