Abstract and Introduction
Abstract
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 modelling framework that integrates machine learning with biophysics and systems pharmacology to screen drugs for Alzheimer's disease using patients' tissue samples. Our predictive modelling 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 analysed 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 signalling pathway, our RNA sequencing 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 signalling and the ubiquitin/proteasome pathway to reduce cognitive deficits and pathology relevant to Alzheimer's disease.
Introduction
Alzheimer's disease currently affects about 5.8 million Americans, the majority of whom are over the age of 65 years, with a subpopulation of patients under this age cut-off. Experts speculate that by 2050, expenditure set aside for Alzheimer's disease will exceed $2.8 trillion and over 14 million people over the age of 65 years will be affected.[1] The hallmarks of this disease consist of neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation detected in the brains of Alzheimer's disease patients.[2] In addition, cognitive deficits associated with Alzheimer's disease include loss of memory and progressive impairment of thought and reasoning. However, the catalysts for setting Alzheimer's disease into motion are still being investigated. Aside from the aforementioned changes, evidence has emerged to suggest that there is a genetic predisposition to Alzheimer's disease that leaves vulnerable populations more at risk, with environmental factors and lifestyle possibly playing a role in disease progression.[3]
US Food and Drug Administration-approved drugs designed to treat Alzheimer's disease by targeting plaques or tangles do not halt disease progression, having a reported 99.6% failure rate.[4] This failure can be attributed to the complexity of Alzheimer's disease, not just the plaques or tangles.[2] One such factor contributing to the pathology of Alzheimer's disease is neuroinflammation. This occurs when there is cellular damage in the brain, leading to recruitment of microglia and astrocytes to clear the damage and restore neuronal function.[5] However, chronic neuroinflammation resulting from persistent activation of pro-inflammatory responses in combination with delays in anti-inflammatory responses causes buildup of cellular debris and neuronal damage. Selecting neuroinflammation as a therapeutic strategy to combat Alzheimer's disease may prove beneficial, considering the promise this strategy has shown in treating other neurodegenerative disorders such as amyotrophic lateral sclerosis and multiple sclerosis.[6,7] Therapeutics aimed at these two disorders showed promise by amplifying the neuroprotective properties of glial cells, a strategy that could be mimicked by targeting Alzheimer's disease pathology from the neuroinflammatory perspective.
To identify existing drugs that can modulate neuroinflammation in Alzheimer's disease, we applied a predictive modelling framework that integrates machine learning with biophysics and systems pharmacology to repurpose approved drugs for Alzheimer's disease treatment. Our model predicted that ibudilast (IBU), a nonselective phosphodiesterase inhibitor and a TLR4 antagonist,[8–12] inhibits IRAK1 as an off-target, modulates multiple Alzheimer's disease-associated pathways and reverses the molecular phenotypes of Alzheimer's disease patients. The primary mode of action of IBU is by way of neuroprotection and anti-inflammatory effects. In a clinical trial for opioid withdrawal, IBU was shown to be a TLR4 antagonist and to reduce glial cell activation.[13] Moreover, IBU has shown promise in clinical trials against multiple sclerosis and amyotrophic lateral sclerosis.[14]
Results from a phase-two clinical trial for progressive multiple sclerosis reported that after 96 weeks of treatment, IBU-treated patients exhibited less brain atrophy than placebo-treated patients.[15] IBU effects were significant, provided that intervention occurred in the early stages of the disease.[16] Based on this study, we evaluated the effects of long-term (6 month) treatment of IBU in an age-dependent transgenic rat model of Alzheimer's disease for improvement in spatial memory and mitigation of hippocampal Alzheimer's disease pathology.
Brain. 2023;146(3):898-911. © 2023 Oxford University Press