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bioRxiv · 10.1101/2025.03.06.641147

Artificial intelligence-augmented drug discovery identifies gefitinib as a potential treatment for ALS

Myszczynska, M.·Stopford, M.·Markus, N.·Nyberg, S.·Stone, N.·Granger, S.·Shaw, A.·Martins, R.·Allen, C.·Keerie, A.·Wells, T.·Thomas, R.·Brown-Wright, H.·Sheppard, D.·Phelan, A.·Smith, D.·Richardson, P.·Mead, R.·Ferraiuolo, L.
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Abstract

Amyotrophic lateral sclerosis (ALS) is characterised by motor neuron (MN) death; however, astrocytes play a key role in disease pathogenesis. Developments in the field of artificial intelligence (AI) have the potential to impact drug discovery in multiple ways, including the rapid identification of drug repurposing candidates. A combination of natural language processing and deep learning algorithms was used to generate a knowledge graph based on scientific literature, omics and chemical databases, and other public sources with the aim to identify drug repurposing candidates for ALS. The aim of the study was to determine the effect of a cancer compound identified by AI, gefitinib, on MN survival, and to decipher its mode of action inin vitroandin vivomodels of ALS. We used co-cultures of healthy motor neurons with ALS patient-derived astrocytes (iAstrocytes), obtained through a semi-direct conversion protocol, to assess the neuroprotective properties of gefitinib. Compound treatment led to a significant rescue of MNs cultured with ALS iAstrocytes and a significant reduction in the levels of cleaved TDP-43 fragments in ALS iAstrocytes. Our data suggest that gefitinib-mediated activation of autophagy decreased the 35 kDa fragments of TDP-43. In a proof-of-conceptin vivostudy in SOD1G93Amice, gefitinib treatment significantly delayed the onset of neurological symptoms, thus showing the potential of AI-augmented drug discovery for neurodegenerative disorders.Significance StatementThis study presents an AI-augmented method of identifying potential repurposing candidates for disease with an unprecedented speed. The AI’s results were validatedin vitrousing iAstrocytes differentiated from induced neuronal progenitor cells (iNPCs), which are pathophysiologically relevant models suitable for studying neurodegeneration. iNPCs recapitulate many pathological hallmarks of the disease and they retain the ageing phenotype of the patient that they are obtained from. TDP-43 proteinopathy is one of the disease hallmarks observed in patients and is present in 97% of ALS patients. Here, we show gefitinib, a repurposing candidate identified by AI, improves survival of MNs in a co-culture with patient-derived astrocytes and can modulate TDP-43 proteinopathy.

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Myszczynska, M., Stopford, M., Markus, N., Nyberg, S., Stone, N., Granger, S., Shaw, A., Martins, R., Allen, C., Keerie, A., Wells, T., Thomas, R., Brown-Wright, H., Sheppard, D., Phelan, A., Smith, D., Richardson, P., Mead, R., Ferraiuolo, L.. 2025-03-10. Artificial intelligence-augmented drug discovery identifies gefitinib as a potential treatment for ALS. https://doi.org/10.1101/2025.03.06.641147

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