Tolfenamic Acid: A Modifier of the Tau Protein and its Role in Cognition and Tauopathy

Leso, Allison; Subaiea, Gehad M.; Lahouel, Asma; Masoud, Anwar; Mushtaq, Foqia; Deeb, Reem; Eid, Aseel; Dash, Miriam; Bihaqi, Syed Waseem

doi:10.2174/1567205015666180119104036

Cited by 18 publications

(24 citation statements)

References 29 publications

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“…28-30 As a potential modifier of tau protein, Tolfenamic acid is under investigation as a potential drug to prevent and modify the progression of AD. 31 The results of this study support the above experimental result and show that tolfenamic acid may be a potential anti-dementia medication.…”

Section: Discussionsupporting

confidence: 87%

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

Park

Cho

Wall

et al. 2019

Preprint

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AbstractNationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: “definite AD” with diagnostic codes and dementia medication (n=614) and “probable AD” with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

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Section: Discussionsupporting

confidence: 87%

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

Park

Cho

Wall

et al. 2019

Preprint

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“…However, these findings should be compared against the results of the same trials reported in a previous study by Chang et al. , 29 in which h-Tau carriers treated with TA performed significantly better compared to their untreated, control counterparts.…”

Section: Resultsmentioning

confidence: 74%

“…Recently, Chang et al. 29 reported that TA-treated h-Tau knock-in mice displayed a significant improvement in cognitive function and long-term memory compared to their non-treated counterparts. In addition, TA was shown to reduce site-specific hyperphosphorylation of tau at threonine-181 and serine-396, known sites of hyperphosphorylation in AD.…”

Section: Discussionmentioning

confidence: 99%

Loss in efficacy measures of tolfenamic acid in a tau knock-out model: Relevance to Alzheimer’s disease

Leso

Bihaqi

Masoud

et al. 2019

Exp Biol Med (Maywood)

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In the healthy human brain, the protein tau serves the essential function of stabilizing microtubules. However, in a diseased state, tau becomes destabilized and aggregates into a pathogenic form that ultimately creates one of the two major hallmarks of Alzheimer’s disease (AD), tau tangles. Multiple neurodegenerative diseases, termed tauopathies, such as Pick’s disease, and progressive supranuclear palsy, are also linked to mutations in tau. While AD does include a second hallmark in the form of amyloid beta (Aβ) plaques, to date all therapeutics aimed at these hallmark features have failed. The nonsteroidal anti-inflammatory drug tolfenamic acid (TA) has been shown to reduce the levels of multiple neurodegenerative endpoints viz amyloid precursor protein (APP), Aβ, tau, phosphorylated tau (p-tau) and improve cognitive function, in various murine models, via a new mechanism that targets specificity protein 1 ( SP1). Sp1 is a zinc-finger transcription factor essential for the regulation of tau and CDK5 genes (among others). The impact of TA on these neurodegenerative endpoints occurred in animal models and systems in which both the tau and the APP genes were present. The experimental model utilized in this paper tested whether the same beneficial outcomes of TA can take place after the removal of endogenous murine tau. We found that the impact of TA, both molecular and behavioral, was no longer significant in the absence of the tau gene. This ability of TA occurred independently of its action on anti-inflammatory targets. Therefore, these findings suggest the essentiality of tau for the novel mechanism of action of TA. Impact statement The number of people suffering from Alzheimer’s disease (AD) is expected to increase exponentially in the coming decades. It is estimated to cost the economy about $200 billion annually. With the failure of standard therapeutic approaches, there is a need to develop new drugs in order to avoid an “epidemic crisis” in the future. We have discovered that tolfenamic acid (TA) lowers the levels of proteins associated with AD, by targeting common transcriptional mechanisms that regulate genes involved in common pathogenic pathways. Here, we investigated whether TA had effects on both the amyloid and tau pathways, or whether it selectively targets one of these pathways which impacted the other. Behavioral and molecular studies revealed that TA loses its AD therapeutic potential when tau gene is removed. This ability of TA occurred independently of its action on anti-inflammatory targets. These findings suggest that tau is essential for the new action of TA.

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“…It is known to lower the gene expression of Amyloid precursor protein 1(APP1) and betasite APP cleaving enzyme 1(BACE1) by promoting the degradation of specificity protein 1(Sp1) [27][28][29] . As a potential modifier of tau protein, Tolfenamic acid is under investigation as a potential drug to prevent and modify the progression of AD 30 . The results of this study support the above experimental result and show that tolfenamic acid may be a potential anti-dementia medication.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

et al. 2020

View full text Add to dashboard Cite

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer's disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: "definite AD" with diagnostic codes and dementia medication (n = 614) and "probable AD" with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

show abstract

Tolfenamic Acid: A Modifier of the Tau Protein and its Role in Cognition and Tauopathy

Abstract: TA holds the potential as a disease-modifying agent for the treatment of tauopathies including AD.

Cited by 18 publications

References 29 publications

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

Loss in efficacy measures of tolfenamic acid in a tau knock-out model: Relevance to Alzheimer’s disease

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

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