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Zebrafish Models to Understand Respiratory Depression and Analgesia by Opioids and to Identify Safe Opioid Pain Therapies

2019· article· en· W3176804726 on OpenAlexaff
Shenhab Zaig, Carolina da Silveira Scarpellini, Xiao‐Yan Wen, Gaspard Montandon

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineOpioidFentanylPharmacologyRespiratory systemZebrafishAnesthesiaReceptorInternal medicineBiology

Abstract

fetched live from OpenAlex

Introduction Opioid drugs are the mainstay of pain management, but their use is limited by their severe side‐effects that can be lethal with overdose. Indeed, opioid drugs induce respiratory depression, that can lead to severe hypoxemia and respiratory arrest when opioids are abused. The current antidote naloxone (Narcan) is a life‐saving therapy, but its use is limited because it can only be given after the overdose occurs, so it is not a preventive treatment. The main challenge in opioid drug discovery is therefore to develop new opioid therapies with potent analgesia but reduced respiratory depression, so opioids can be safely prescribed. Objectives To accelerate drug discovery, we established phenotype‐based approaches using in vivo zebrafish models of respiratory depression and analgesia. Zebrafish is an amenable model to study respiratory depression because its respiratory circuits are similar to mammalian circuits. Also, zebrafish μ‐opioid receptors have 70% homology of amino acids with their mammalian counterparts. Our aim was to developed a high‐throughput screening platform that combines drug screening and behavioural profiling so new preventive therapies can be identified. Methods To determine respiratory depression, we assessed buccal movements, as an index of respiratory activity, in zebrafish larvae (day post‐fertilization 14), and its response to the μ‐opioid receptor analgesic fentanyl. We used a video‐recording system to assess zebrafishes in multi‐well plate. To assess opioid analgesia, we induced mild pain in zebrafish larvae by submerging it in a solution of formalin, or formalin/fentanyl, and measuring its subsequent locomotor or swimming response. Results Fentanyl (0.02 μM) significantly decreased the rate of buccal movements by 84% (baseline 41.7 breath/min, fentanyl 6.71 breath/min, n=11, p=0.038), a depression reversed by naloxone (5 μM, 44.3 breath/min, p=0.01). Similarly, respiratory depression by fentanyl was reversed by the AMPA receptor modulator ampakine CX‐614 (50 μM, p=0.04, n=10) and the 5‐HT4 agonist BIMU‐8 (10 μM, n=6, p=0.035). Formalin (0.05%) increased increased locomotion, and this response was significantly reduced by fentanyl (1 μM), an analgesic effect blocked by naloxone (5 μM). Discussion Our novel and unique zebrafish models mimicked well the effects of opioid drugs on respiratory activity and nociception observed in mammals. This proof‐of‐principle study suggests that zebrafish can be used for phenotype‐based high‐throughput drug and gene screening. Using these assays, we will knock‐down key‐genes involved in opioid inhibition using morpholino oligonucleotides, and test chemical screens to identify preventive therapies to minimize respiratory depression by opioids while preserving their analgesic properties. Support or Funding Information St. Michael's Hospital Foundation This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.038
GPT teacher head0.286
Teacher spread0.248 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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