Preclinical iPSC drug screen and machine learning predict neuroprotective agents for a form of childhood dementia — The Association Specialists
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Preclinical iPSC drug screen and machine learning predict neuroprotective agents for a form of childhood dementia (21402)

Zarina Greenberg 1 , Ella McDonald 1 2 , Alejandra Norena-Puerta 1 , Manam Inushi De Silva 1 2 , Cade Christensen 1 , Robert Adams 1 , Jenne Tran 1 2 , Paris Mazzachi 1 2 , Sebastian Loskarn 1 , Siti Mubarokah 2 , Megan Maack 3 4 , Kristina Elvidge 3 4 , Mark Hutchinson 5 6 , Kim Hemsley 2 , Lisa Melton 4 , Nicholas Smith 7 8 , Cedric Bardy 1 2
  1. Hopwood Centre for Neurobiology, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
  2. College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
  3. Childhood Dementia Initiative , Sydney, NSW
  4. Sanfilippo Children’s Foundation, Sydney, NSW
  5. Institute for Photonics and Advanced Sensing, University of Adelaide, Adelaide, SA
  6. School of Biomedicine, University of Adelaide, Adelaide, South Australia
  7. Department of Neurology and Clinical Neurophysiology, Women’s and Children’s Health Network, Adelaide, South Australia
  8. Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia

Childhood dementia is an umbrella term for rare paediatric neurological conditions with symptoms and pathophysiology overlapping the common adult forms of dementia. Sanfilippo syndrome or Mucopolysaccharidosis type IIIA (MPSIIIA) is a progressive neurodegenerative form of childhood dementia caused by a genetic mutation in lysosomal enzyme sulfamidase. Currently, there is no cure for MPSIIIA with the primary area of research focusing on treating enzyme deficiency, however evidence demonstrates that downstream neurophysiological impairments can drive neurological symptoms and accelerate neurodegeneration. Here, we developed a patient-derived iPSC preclinical model to screen 63 repurposed drugs used in adult neurodegenerative and CNS disorders and target downstream neuropathological pathways. We found that our iPSC-derived patient neurons revealed lysosomal dysfunction and heparan sulfate build-up, characteristics of MPSIIIA. Furthermore, patient-derived neurons were particularly prone to stress, astrocytic reactivity, and chronic neurodegeneration. Combining machine learning, high-content imaging and single-cell transcriptomics, we found nine compounds that ameliorated MPSIIIA phenotypes within two weeks of treatment in vitro. Our study introduces a novel preclinical model for childhood dementia and reveals repurposable agents that may help slow neurodegeneration and neuroimmune dysregulation in children with MPSIIIA and related disorders.