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58. The Epidermal Growth Factor Receptor (EGFR) Inhibitor Gefitinib Enhances In Vitro and In Vivo Peripheral Nerve Regeneration in a Mouse Median Nerve Injury Model

2023· article· en· W4377103226 on OpenAlexaff
Max Topley, Anne‐Marie Crotty, Michael D. Kawaja, J. Michael Hendry

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsQueen's University
Fundersnot available
KeywordsRegeneration (biology)In vivoGefitinibNerve growth factorPharmacologyEpidermal growth factor receptorNerve injuryMedicineNeuriteEGFR inhibitorsBiologyCancer researchReceptorIn vitroInternal medicineCell biologyAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE: Peripheral nerve injuries have the capacity for spontaneous recovery, but clinical outcomes are limited by many factors that antagonize the biology of nerve regeneration. These antagonistic factors include repulsive cues, such as chondroitin sulphate proteoglycan (CSPG), within regenerative milieu that are encountered by axons that are elongating towards their targets. Currently, no FDA approved pharmacological methods exist to augment the rate or extent of peripheral nerve regeneration. Recent work has implicated the epidermal growth factor receptor (EGFR), a member of the ErbB family of receptor tyrosine kinases, as an inhibitor of peripheral nerve regeneration known to be expressed by peripheral motor and sensory neurons. This study investigates the impact of EGFR blockade using the small molecular inhibitor ‘gefitinib’, an FDA approved cancer drug, on nerve regeneration in vitro and in vivo using a mouse forelimb median nerve injury model. METHODS:In vitro cell culture assays of neurite outgrowth on inhibitory CSPG substrate were carried out assess the response of adult dorsal root ganglia (DRG) neurons of EGFR inhibition with gefitinib. DRG neurons were harvested from adult C57Bl/6 mice and cultured in the presence of CSPG (200nM), with or without gefinitib (10μM). In vivo experimentation involved ten C57BL/6 mice that underwent right median nerve transection and immediate microsurgical repair. Animals were orally administered gefitinib or vehicle daily for five days from the time of surgery. On day 5, regenerated neurons were labeled using Fluorogold neurotracer 5 mm distal to the repair site, followed by tissue collection 1 week later. In a separate cohort of mice, Western blot analysis and grip strength assessment were carried out to assess the signalling and functional impact of gefitinib administration. RESULTS: Neurite extension assays revealed that culturing adult DRG neurons in the presence of CSPG effectively arrested neurite outgrowth. Administration of gefitinib to cultured DRGs in the presence of CSPG restored neurite length to control levels and demonstrated significantly increased mean neurite length compared to CSPG alone. Retrograde labeling 5 days after median nerve injury revealed significantly greater numbers of sensory DRG neurons (738±174 vs. 280±107) but not motoneurons (60±31 vs 18±4; p=0.15) in mice that received gefitinib compared with vehicle, respectively. CONCLUSION: The EGFR receptor is a regulator of peripheral nerve regeneration that has several FDA approved, commercially available inhibitors at market. In this study, selective inhibition of EGFR using the drug gefitinib rescued in vitro neurite outgrowth back to control levels in the presence of inhibitory CSPG. Furthermore, gefitinib increased the number of DRG neurons extending 5 mm beyond the repair site after 5 days compared with vehicle, suggesting an increased rate of sensory neuron regeneration. A focus of future work will be examining what role EGFR may have in mediating the inhibitory cues in the regenerative milieu, such as those from CSPG.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.306
Teacher spread0.275 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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