Alzheimer’s disease is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. Hence, a multi-target drug would improve treatment efficacy. We applied a new multi-scale predictive modeling framework that integrates machine learning with biophysics and systems pharmacology to screen drugs for Alzheimer’s disease using patient’s tissue samples. Our predictive modeling framework identified ibudilast as a drug with repurposing potential to treat Alzheimer’s disease. Ibudilast is a multi-target drug, as it is a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist. In addition, we predict that ibudilast inhibits off-target kinases (e.g. IRAK1 and GSG2). In Japan and other Asian countries, ibudilast is approved for treating asthma and stroke due to its anti-inflammatory potential. Based on these previous studies and on our predictions, we tested for the first time the efficacy of ibudilast in Fisher transgenic 344-AD rats. This transgenic rat model is unique as it exhibits hippocampal-dependent spatial learning and memory deficits, and Alzheimer’s disease pathology including hippocampal amyloid plaques, tau paired-helical filaments, neuronal loss and microgliosis, in a progressive age-dependent manner that mimics the pathology observed in Alzheimer’s disease patients. Following long-term treatment with ibudilast, transgenic rats were evaluated at 11 months of age for spatial memory performance and Alzheimer’s disease pathology. We demonstrate that ibudilast-treatment of transgenic rats mitigated hippocampal-dependent spatial memory deficits, as well as hippocampal (hilar subregion) amyloid plaque and tau paired-helical filament load, and microgliosis compared to untreated transgenic rat. Neuronal density analyzed across all hippocampal regions was similar in ibudilast-treated transgenic compared to untreated transgenic rats. Interestingly, RNA sequencing analysis of hippocampal tissue showed that ibudilast-treatment affects gene expression levels of the TLR and ubiquitin/proteasome pathways differentially in male and female transgenic rats. Based on the TLR4 signaling pathway, our RNAsequencing data suggest that ibudilast-treatment inhibits IRAK1 activity by increasing expression of its negative regulator IRAK3, and/or by altering TRAF6 and other TLR-related ubiquitin ligase and conjugase levels. Our results support that ibudilast can serve as a repurposed drug that targets multiple pathways including TLR signaling and the ubiquitin/proteasome pathway to reduce cognitive deficits and pathology relevant to Alzheimer’s disease.

We investigated the relevance of the prostaglandin D2 pathway in Alzheimer’s disease, because prostaglandin D2 is a major prostaglandin in the brain. Thus, its contribution to Alzheimer’s disease merits attention, given the known impact of the prostaglandin E2 pathway in Alzheimer’s disease. We used the TgF344-AD transgenic rat model because it exhibits age-dependent and progressive Alzheimer’s disease pathology. Prostaglandin D2 levels in hippocampi of TgF344-AD and wild-type littermates were significantly higher than prostaglandin E2. Prostaglandin D2 signals through DP1 and DP2 receptors. Microglial DP1 receptors were more abundant and neuronal DP2 receptors were fewer in TgF344-AD than in wild-type rats. Expression of the major brain prostaglandin D2 synthase (lipocalin-type PGDS) was the highest among 33 genes involved in the prostaglandin D2 and prostaglandin E2 pathways. We treated a subset of rats (wild-type and TgF344-AD males) with timapiprant, a potent highly selective DP2 antagonist in development for allergic inflammation treatment. Timapiprant significantly mitigated Alzheimer’s disease pathology and cognitive deficits in TgF344-AD males. Thus, selective DP2 antagonists have potential as therapeutics to treat Alzheimer’s disease.

Alzheimer's 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.