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bioRxiv · 10.1101/2021.04.06.438662

Multi-scale predictive modeling discovers Ibudilast as a polypharmacological agent to improve hippocampal dependent spatial learning and memory and mitigate plaque and tangle pathology in a transgenic rat model of Alzheimer's disease

Oliveros, G.·Wallace, C. H.·Chaudry, O.·Liu, Q.·Qiu, Y.·Xie, L.·Rockwell, P.·Figueiredo-Pereira, M.·Serrano, P. A.
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Abstract

Alzheimers disease (AD) is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. We developed a multi-scale predictive modeling strategy that integrates machine learning with biophysics and systems pharmacology to model drug actions from molecular interactions to phenotypic responses. We predicted that ibudilast (IBU), a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist, inhibited multiple kinases (e.g., IRAK1 and GSG2) as off-targets, modulated multiple AD-associated pathways, and reversed AD molecular phenotypes. We address for the first time the efficacy of ibudilast (IBU) in a transgenic rat model of AD. IBU-treated transgenic rats showed improved cognition and reduced hallmarks of AD pathology. RNA sequencing analyses in the hippocampus showed that IBU affected the expression of pro-inflammatory genes in the TLR signaling pathway. Our results identify IBU as a potential therapeutic to be repurposed for reducing neuroinflammation in AD by targeting TLR signaling.

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Oliveros, G., Wallace, C. H., Chaudry, O., Liu, Q., Qiu, Y., Xie, L., Rockwell, P., Figueiredo-Pereira, M., Serrano, P. A.. 2021-04-08. Multi-scale predictive modeling discovers Ibudilast as a polypharmacological agent to improve hippocampal dependent spatial learning and memory and mitigate plaque and tangle pathology in a transgenic rat model of Alzheimer's disease. https://doi.org/10.1101/2021.04.06.438662

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