Phelan, A. | Search bioRxiv

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2022

Anemonefish have finer color discrimination in the ultraviolet

In many animals, ultraviolet (UV) vision guides navigation, foraging, and communication, but few studies have addressed the contribution of UV vision to color discrimination, or behaviorally assessed UV discrimination thresholds. Here, we tested UV-color vision in an anemonefish (Amphiprion ocellaris) using a novel five-channel (RGB-V-UV) LED display designed to test UV perception. We first determined that the maximal sensitivity of the A. ocellaris UV cone was at [~]386 nm using microspectrophotometry. Three additional cone spectral sensitivities had maxima at [~]497, 515, and [~]535 nm, which together informed the modelling of the fishs color vision. Anemonefish behavioral discrimination thresholds for nine sets of colors were determined from their ability to distinguish a colored target pixel from grey distractor pixels of varying intensity. We found that A. ocellaris used all four cones to process color information and is therefore tetrachromatic, and fish were better at discriminating colors (i.e., color discrimination thresholds were lower, or more acute) when targets had UV chromatic contrast elicited by greater stimulation of the UV cone relative to other cone types. These findings imply that a UV component of color signals and cues improves their detectability, that likely increases the salience of anemonefish body patterns used in communication and the silhouette of zooplankton prey.

animal behavior and cognition↗