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.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is most prevalent in females. While estrogen provides neuroprotection in females, sex mediated differences in the development of AD pathology are not fully elucidated. Therefore, comparing events between sexes in early-stage AD pathology may reveal more effective therapeutic targets of intervention. To address sex differences, we analyzed early-stage 9-month male and female TgF344-AD (Tg-AD) rats, an AD model carrying the APPswe and Presenilin 1 (PS1ΔE9) mutations that develops progressive age-dependent AD pathology similar to humans. Tg-AD females significantly outperformed Tg-AD males in the active place avoidance (aPAT) test that assesses hippocampal-dependent spatial learning and memory. However, comparisons between Tg-AD male or female rats and their WT counterparts showed significant deficits for female but not male rats. Nevertheless, Tg-AD females experienced significantly less hippocampal neuronal loss with higher GluA2 subunit levels than Tg-AD males. Unexpectedly, Tg-AD females displayed higher levels of hippocampal amyloid plaques than Tg-AD males. Thus, we propose that GluA2 may provide a neuroprotective function for Tg-AD females in our rat model by mitigating cognitive impairment independently of amyloid plaques. Elucidating this protective mechanism in AD could lead to new targets for early intervention.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and is the sixth leading cause of death in the US. AD is more prevalent in females than males. While estrogen provides neuroprotection in females, sex mediated differences in the development of AD pathology are not fully elucidated. Therefore, a comparison of the events that develop between sexes in the early-stage of AD pathology may reveal new potential targets for more effective therapeutic intervention. To address sex differences, we analyzed early stage 9-month male and female TgF344-AD (Tg-AD) rats, an AD model carrying the APPswe and Presenilin 1 (PS1ΔE9) mutations that develops progressive age-dependent AD pathology similar to humans. Using active place avoidance (aPAT) tests that assess hippocampal-dependent spatial learning and memory, we found significant deficits in Tg-AD females compared to wild type females, but no significant difference between the two male genotypes. Moreover, significant sex differences were observed in that Tg-AD females outperformed Tg-AD males in several measures of the aPAT test. Unexpectedly, Tg-AD females displayed higher levels of hippocampal amyloid plaques and amoeboid microglia than their Tg-AD male littermates. Furthermore, Tg-AD females experienced less hippocampal neuronal loss and had higher GluA2 subunit levels than Tg-AD males. Based on our findings, we propose that estrogen may protect females against cognitive impairment at early stages of AD by regulating GluA2 levels independently of amyloid plaque deposition and gliosis. Elucidating this potential protective mechanism of action of estrogen in AD could lead to new targets for early intervention.

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.