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Discovery of Novel Pain Therapies Without Side‐effects by Combining Targeted Mutagenesis and Phenotype‐based Drug Screening in Larval Zebrafish

2022· article· en· W4225377465 on OpenAlexaff
Yara Zayed, Vivianne Gao, Prachi Ray, Gaspard Montandon

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of TorontoPublic Health OntarioToronto Public Health
Fundersnot available
KeywordsFentanylZebrafishOpioidPharmacologyAddictionMedicineDrug discoveryDrugAdenylyl cyclaseBioinformaticsBiologyPsychiatryInternal medicineGeneticsGeneReceptor

Abstract

fetched live from OpenAlex

Introduction Opioid drugs provide effective pain relief but come with considerable side‐effects including addiction and respiratory depression which can be lethal with overdose. During the COVID19 pandemic, the opioid epidemic reached almost 100,000 deaths in the United States, highlighting the urgent need to identify pain killers with reduced liability so they can be safely prescribed. One approach to develop safe pain killers is to combine existing opioid analgesics with drugs reducing respiratory depression. Using our novel biotechnology platforms in larval zebrafish, we propose to identify new molecular targets and drugs with potent analgesic properties, reduced respiratory liability, and without addictive properties. Methods Combining targeted mutagenesis and phenotype‐based drug screening approaches in larval zebrafish in vivo, we can quickly and effectively identify new molecular targets and screen new drug compounds to either target respiratory depression, analgesia ( Zaig and Montandon, 2021, eLife ), as well as addiction. We first screened customized drug libraries targeting voltage‐gated calcium channels and the adenylyl cyclase pathway, two molecular pathways involved in pain. We are also creating mutants using CRISPR‐cas9 in first generation zebrafish embryos to identify new molecular targets. Using our new models, we quantified respiratory depression by fentanyl in day post‐fertilization 7 larvae, as well as the nociceptive response to formalin. Results To determine the role of calcium channels opioid‐mediated effects, we combined fentanyl and the calcium channel activators nefiracetam (1µM) or FLP‐64176 (1µM). Both nefiractem and FLP‐64176 prevented respiratory depression by fentanyl (1µM). To target the adenylyl cyclase pathway, we combined fentanyl and the phosphodiesterase inhibitors pentoxifylline (50µM), roflumilast (1µM), or dipyridamole (100µM). Pentaxifylline prevented respiratory depression by fentanyl but not roflumilast and dipyridamole. To perform targeted mutagenesis, we injected 4 single guide RNAs targeting the orpm1 gene, i.e. the gene coding for µ‐opioid receptors. Six days later, we measured respiratory depression by fentanyl in knockout larvae or controls. In oprm1 ‐/‐ knockout larvae, respiratory depression by fentanyl was substantially reduced compared to controls, therefore confirming the validity of our targeted mutagenesis approach. We are currently targeting the genes coding for calcium channels and phosphodiesterases to determine whether they can be molecular targets for respiratory depression and/or analgesia. Discussion The successful identification of new drug combinations to prevent respiratory depression while preserving analgesia suggests that our drug discovery platform in larval zebrafish will substantially accelerate the identification of new opioid drugs with reduced morbidity and mortality. Our platform is also being leveraged to discover non‐opioid pain killers with reduced side‐effects.

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 score0.005
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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
Published2022
Admission routes1
Has abstractyes

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