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Record W3117376274 · doi:10.2147/jir.s284471

Repurposing FDA Approved Drugs as JNK3 Inhibitor for Prevention of Neuroinflammation Induced by MCAO in Rats

2020· article· en· W3117376274 on OpenAlexaff
Zeshan Zulfiqar, Fawad Ali Shah, Shagufta Shafique, Abdullah Alattar, Tahir Ali, Arooj Mohsin Alvi, Sajid Rashid, Shupeng Li

Post-publication record

NatureRetraction
ReasonDuplication of/in Image;
Date11/7/2022 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueJournal of Inflammation Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsUniversity of Calgary
FundersRiphah International University
KeywordsMedicinePharmacologyNeuroprotectionDrug repositioningNeuroinflammationDabigatranPitavastatinBrain ischemiaIschemiaAnesthesiaDrugInternal medicineInflammationAtorvastatinWarfarin

Abstract

fetched live from OpenAlex

BACKGROUND: Stress-associated kinases are considered major pathological mediators in several incurable neurological disorders. Importantly, among these stress kinases, the c-Jun NH2-terminal kinase (JNK) has been linked to numerous neuropathological conditions, including oxidative stress, neuroinflammation, and brain degeneration associated with brain injuries such as ischemia/reperfusion injury. In this study, we adopted a drug repurposing/reprofiling approach to explore novel JNK3 inhibitors from FDA-approved medications to supplement existing therapeutic strategies. MATERIALS AND METHODS: We performed in silico docking analysis and molecular dynamics simulation to screen potential candidates from the FDA approved drug library using the standard JNK inhibitor SP600125 as a reference. After the virtual screening, dabigatran, estazolam, leucovorin, and pitavastatin were further examined in ischemic stroke using an animal rodent model of focal cerebral ischemia using transient middle cerebral artery occlusion (t-MCAO). The selected drugs were probed for neuroprotective effectiveness by measuring the infarct area (%) and neurological deficits using a 28-point composite score. Biochemical assays including ELISA and immunohistochemical experiments were performed. RESULTS: We obtained structural insights for dabigatran, estazolam, and pitavastatin binding to JNK3, revealing a significant contribution of the hydrophobic regions and significant residues of active site regions. To validate the docking results, the pharmacological effects of dabigatran, estazolam, leucovorin, and pitavastatin on MCAO were tested in parallel with the JNK inhibitor SP600125. After MCAO surgery, severe neurological deficits were detected in the MCAO group compared with the sham controls, which were significantly reversed by dabigatran, estazolam, and pitavastatin treatment. Aberrant morphological features and brain damage were observed in the ipsilateral cortex and striatum of the MCAO groups. The drugs restored the anti-oxidant enzyme activity and reduced the levels of oxidative stress-induced p-JNK and neuroinflammatory mediators such as NF-kB and TNF-ɑ in rats subjected to MCAO. CONCLUSION: Our results demonstrated that the novel FDA-approved medications attenuate ischemic stroke-induced neuronal degeneration, possibly by inhibiting JNK3. Being FDA-approved safe medications, the use of these drugs can be clinically translated for ischemic stroke-associated brain degeneration and other neurodegenerative diseases associated with oxidative stress and neuroinflammation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.353
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2020
Admission routes1
Has abstractyes

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