Repurposing ibudilast to mitigate Alzheimer’s disease by targeting inflammation

Oliveros, Giovanni; Wallace, Charles H; Chaudry, Osama; Li, Qiao; Qiu, Yue; Xie, Lei; Rockwell, Patricia; Serrano, Peter A.

doi:10.1093/brain/awac136

Cited by 27 publications

(25 citation statements)

References 53 publications

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“…Understanding pre- and/or early-stages of AD is paramount, as treating AD at these stages would be the best approach to preventing severe progression ( Sun et al, 2018 ). We established that at 11 mo of age, the Tg-AD rats exhibit impaired hippocampal-dependent spatial learning and memory, as well as molecular markers of AD, such as amyloid plaques, microglial activation, neuronal loss, and early signs of τ-PHF, the latter reported by us ( Oliveros et al, 2022 ).…”

Section: Discussionmentioning

confidence: 56%

Timapiprant, a prostaglandin D2 receptor antagonist, ameliorates pathology in a rat Alzheimer’s model

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 56%

Timapiprant, a prostaglandin D2 receptor antagonist, ameliorates pathology in a rat Alzheimer’s model

et al. 2022

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“…Recently, neuroinflammation has been recognized to play a predominant role in AD etiology and can be a potential AD drug target [ 59 ]. We have shown that anti-inflammation agent ibudilast can mitigate AD in the transgenic AD rat model [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

“…and can be a potential AD drug target [59]. We have shown that anti-inflammation agent ibudilast can mitigate AD in the transgenic AD rat model [60].…”

Section: Plos Computational Biologymentioning

confidence: 99%

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

Qiu

et al. 2022

PLoS Comput Biol

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Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, although it is well known that drug responses depend on a cellular context. Thus, the existing approach has limitations when applied to personalized medicine, especially for many understudied diseases whose molecular profiles are dramatically different from those characterized in the training data. Besides the gene expression, dose-dependent cell viability is another important phenotype readout and is more informative than conventional summary statistics (e.g., IC50) for characterizing clinical drug efficacy and toxicity. However, few computational methods can reliably predict the dose-dependent cell viability. To address the challenges mentioned above, we designed a new deep learning model, MultiDCP, to predict cellular context-dependent gene expressions and cell viability on a specific dosage. The novelties of MultiDCP include a knowledge-driven gene expression profile transformer that enables context-specific phenotypic response predictions of novel cells or tissues, integration of multiple diverse labeled and unlabeled omics data, the joint training of the multiple prediction tasks, and a teacher-student training procedure that allows us to utilize unreliable data effectively. Comprehensive benchmark studies suggest that MultiDCP outperforms state-of-the-art methods with unseen cell lines that are dissimilar from the cell lines in the supervised training in terms of gene expressions. The predicted drug-induced gene expressions demonstrate a stronger predictive power than noisy experimental data for downstream tasks. Thus, MultiDCP is a useful tool for transcriptomics-based drug repurposing and compound screening that currently rely on noisy high-throughput experimental data. We applied MultiDCP to repurpose individualized drugs for Alzheimer’s disease in terms of efficacy and toxicity, suggesting that MultiDCP is a potentially powerful tool for personalized drug discovery.

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“…Might broadly anti-inflammatory agents targeting microglia also be used as putative HAND therapeutics? microglia [53], such as the phosphodiesterase inhibitor ibudilast [54], could be explored as potential complementary strategies to treat HAND.…”

Section: Outstanding Questionsmentioning

confidence: 99%

The potential role of HIV-1 latency in promoting neuroinflammation and HIV-1-associated neurocognitive disorder

Sreeram

García-Mesa

et al. 2022

Trends in Immunology

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Repurposing ibudilast to mitigate Alzheimer’s disease by targeting inflammation

Cited by 27 publications

References 53 publications

Timapiprant, a prostaglandin D2 receptor antagonist, ameliorates pathology in a rat Alzheimer’s model

Timapiprant, a prostaglandin D2 receptor antagonist, ameliorates pathology in a rat Alzheimer’s model

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

The potential role of HIV-1 latency in promoting neuroinflammation and HIV-1-associated neurocognitive disorder

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