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Enregistrement W4400569874 · doi:10.3389/fpsyt.2024.1443304

Editorial: Discovery, development and implementation of improved options for treating opioid overdose in the synthetic opioid era

2024· editorial· en· W4400569874 sur OpenAlexaffabout
Christian Heidbreder, Mark K. Greenwald, Bernard Le Foll, Phil Skolnick

Notice bibliographique

RevueFrontiers in Psychiatry · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesnon disponible
Mots-clésOpioid overdoseOpioidMedicineOpioid abuseOpioid use disorderIntensive care medicine(+)-NaloxoneInternal medicineReceptor

Résumé

récupéré en direct d'OpenAlex

Although Naloxone HCl, which the US Food and Drug Administration (FDA) first approved in 1971, remains the mainstay for opioid overdose treatment, this Research Topic provides new insights into the urgency and importance of discovering, developing and implementing improved options for treating opioid overdose in the synthetic opioid era. Four manuscripts submitted to the journal were deemed suitable for publication after undergoing a thorough peer review process. The following summarizes the main results for each manuscript.In the first article, "Brain oxygen responses induced by opioids: focus on heroin, fentanyl, and their adulterants", Kiyatkin and Choi chronically implanted oxygen sensors in the rat nucleus accumbens and coupled them with highspeed amperometry to directly monitor brain oxygen responses induced by heroin and fentanyl. Both heroin and fentanyl were shown to produce a biphasic pattern of brain oxygen response: an initial transient decrease (hypoxia) followed by a subsequent weaker but more prolonged increase (hyperoxia), indicating the involvement of posthypoxic compensatory vascular response. The hypoxic effects of heroin and fentanyl were potentiated or otherwise altered by the addition of alcohol, ketamine, and xylazine, further supporting a link between adulterated drug supply and heightened health complications. Finally, the authors provided new insights into the time-sensitive brain hypoxia induced by fentanyl and its relation to the timing of reversal by naloxone.In the second article, "Are carfentanil and acrylfentanyl naloxone resistant?", Feasel et al. applied in vitro techniques to establish the median effective inhibitory concentrations for fentanyl, acrylfentanyl, and carfentanil, and to evaluate naloxone's efficacy in reversing agonist-receptor interactions. They first demonstrated that agonist actions of acrylfentanyl and carfentanil could be reversed with naloxone. Strikingly, although acrylfentanyl had approximately one-half the potency of fentanyl, this compound required nearly double the concentration of naloxone to reverse its agonist activity relative to fentanyl. Carfentanil, with potency ≈100× greater than fentanyl, required a significantly higher concentration of naloxone to antagonize a challenge of its EC90.In the third article, "Evaluating the rate of reversal of fentanyl-induced respiratory depression using a novel longacting naloxone nanoparticle, cNLX-NP", Averick et al. characterized the efficacy of a novel opioid reversal agent based on covalent naloxone nanoparticles (cNLX-NP) to reverse fentanyl-induced respiratory effects, and the duration of its protective effects. The authors showed that cNLX-NP extended the terminal half-life of naloxone beyond that of naloxone alone or nalmefene, blocked fentanyl-induced respiratory depression up to 48 hours and rapidly reversed fentanyl-induced respiratory depression when combined 1:1 with free naloxone.In the fourth article, "Comparison of intranasal naloxone and intranasal nalmefene in a translational model assessing the impact of synthetic opioid overdose on respiratory depression and cardiac arrest", Laffont et al. used a validated translational model, which quantitatively predicts opioid-induced respiratory depression and cardiac arrest, to compare rates of fentanyl-and carfentanil-induced cardiac arrest events following rescue by intranasal (IN) administration of the mu-receptor antagonists naloxone and nalmefene. This model (Mann et al., 2022), developed by the FDA's Division of Applied Regulatory Science, offers an unbiased approach to evaluating the effectiveness of these agents following a potentially lethal dose of synthetic opioid. Following simulated fentanyland carfentanil-induced overdoses in chronic opioid users, IN nalmefene substantially reduced the incidence of cardiac arrest compared to IN naloxone. Nalmefene also produced large and clinically meaningful reductions in the incidence of cardiac arrests in opioid-naïve subjects (see Figure ). Across dosing scenarios, simultaneous administration of four doses of IN naloxone were needed to reduce the percentage of cardiac arrest events to levels produced by a single dose of IN nalmefene. editors thank all authors, reviewers, and editorial board members for contributing to this Research Topic. Recent developments in mathematical modeling, computational power, and availability of preclinical and clinical data sets are enabling the development of new mechanistic models to understand pharmacokineticpharmacodynamic interactions of new, fast-acting and potent opioid overdose reversal agents that may prevent enduring brain damage or death. We hope this Research Topic inspires innovative and life-saving research approaches in this field.Dr. Christian Heidbreder (CH) is a full-time employee of Indivior Inc.CH is holding shares of Indivior Plc, and is holding the following Indivior patents: Methods for treating schizophrenia (20190015415); Psychiatric treatment for patients with gene polymorphisms (20190046532); Buprenorphine dosing regimens (11000520); Buprenorphine to treat respiratory depression (EP3863712A1).Dr. Phil Skolnick (PS) is a Fellow of Indivior Inc.Dr. Mark Greenwald (MKG) has received consulting and speaker fees from Indivior Inc. unrelated to this project.Dr. Bernard Le Foll (BLF) has obtained funding from Indivior for a clinical trial sponsored by Indivior Inc. unrelated to this project. He has participated in a session of a National Advisory Board Meeting (Emerging Trends BUP-XR) for Indivior Canada and is part of a steering board for a clinical trial for Indivior Inc. unrelated to this project. confidence interval) of simulated subjects experiencing a cardiac arrest. The simulations were conducted using data from opioid-naïve individuals as described by Mann et al. (2022); the data presented here are from Laffont et al. (2024). Legend: filled circle, none (no intervention following fentanyl); filled square, IN NX (4 mg intranasal naloxone); filled triangle, IN NLM (2.7 mg intranasal nalmefene, equivalent to 3 mg of nalmefene hydrochloride). Simultaneous administration of two, three or four doses of IN naloxone was simulated by administering a dose equal to 8 mg (2 x 4 mg), 12 mg (3 x 4 mg), or 16 mg (4 x 4 mg), respectively. The statistical approach for simulation was adapted from the model described by Mann et al. (2022).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,042

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,018
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0020,002
Communication savante0,0060,004
Science ouverte0,0040,001
Intégrité de la recherche0,0120,013
Charge utile insuffisante (le modèle a refusé de juger)0,0130,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,289
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission2
Résumé présentoui

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