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Enregistrement W4399481993 · doi:10.1136/annrheumdis-2024-eular.1406

POS0322 GUSELKUMAB BINDING TO CD64+ IL-23–PRODUCING MYELOID CELLS ENHANCES POTENCY FOR NEUTRALIZING IL-23 SIGNALING

2024· article· en· W4399481993 sur OpenAlexaff
Dennis McGonagle, Raja Atreya, María T. Abreu, J.G. Krueger, K. Eyerich, R. Bissonnette, C. Greving, H. Li, Tom C. Freeman, Andrew Hart, Brice E. Keyes, B. Stoveken, J. Hartman, K. Leppard, J. Wertheimer, Indra Sarabia, Kristen Kohler, Christopher T. Ritchlin, I. B. Mc Innes, Matthieu Allez, A. Fourie, K. Sachen

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineImmunology and Microbiology
ThématiqueNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Établissements canadiensInnovaderm (Canada)
Organismes subventionnairesnon disponible
Mots-clésPotencyImmunologyChemistryMedicineBiochemistryIn vitro

Résumé

récupéré en direct d'OpenAlex

Background: IL-23 is implicated in the pathogenesis of psoriasis (PsO), and myeloid cells that express FcγRI, known as CD64, have been identified as the primary cellular source of IL-23 in lesional PsO skin tissue.[1] The incidence and prevalence of psoriatic arthritis increases with the severity of PsO,[2] and joint disease activity is positively correlated with frequency of peripheral CD64+ monocytes.[3] Guselkumab (GUS) and risankizumab (RZB) are monoclonal antibodies (mAbs) specifically directed against the IL-23p19 subunit; however, GUS is a fully human IgG1 mAb with a native Fc region while RZB is a humanized IgG1 mAb with a mutated Fc region. Objectives: Here, we evaluated CD64 and IL-23 expression in PsO patient skin biopsies, binding of GUS and RZB to CD64, and the functional consequences of CD64 binding by IL-23p19 subunit mAbs, in in vitro assays. Methods: Expression of CD64, IL23p19 subunit, and IL23p40 subunit mRNA transcripts were analyzed from bulk and single-cell RNAseq datasets. Binding of mAbs to IFNγ-primed human monocytes, as well as binding to IL-23–secreting inflammatory monocytes and capture of endogenously secreted IL-23, were assessed by flow cytometry. Internalization of IL-23, GUS, and RZB within CD64+ macrophages was evaluated using live cell confocal imaging. Potency of GUS and RZB for inhibiting IL-23 signaling was determined in a co-culture of THP-1 cells (a CD64+ monocyte cell line activated to produce IL-23) and an IL-23 reporter cell line (measuring biologically active IL-23). Expression of IL23p19 mRNA transcript in the co-culture was measured by qPCR. Results: Analyses of RNAseq datasets showed increased expression of CD64, IL23p19, and IL23p40 mRNA transcripts in lesional versus non-lesional PsO skin, and myeloid cell types co-expressing CD64 and IL23p19 mRNA transcripts were increased in lesional skin. In in vitro assays, GUS, but not RZB, showed Fc-mediated binding to CD64 on IFNγ-primed monocytes. Moreover, CD64-bound GUS simultaneously captured IL-23 secreted from the same cells. GUS, but not RZB, bound to the surface of CD64+ macrophages and mediated internalization of IL-23 to low pH intracellular compartments. GUS and RZB demonstrated similar potency for inhibiting signaling by IL-23 present in THP-1–conditioned medium. However, in a co-culture of IL-23–producing THP-1 cells with an IL-23–responsive reporter cell line, GUS demonstrated enhanced potency compared to RZB for inhibiting IL-23 signaling. GUS did not alter expression of IL23p19 mRNA transcript in the co-culture. Conclusion: The results of our transcriptomic analysis were consistent with previous observations of CD64+ myeloid cells as a key source of IL-23 production in lesional PsO skin tissue. GUS binding to CD64 on IL-23–producing cells likely contributed to the enhanced functional potency of GUS compared to RZB for inhibition of IL-23 signaling in the co-culture assay. These in vitro data support a hypothesis for optimal localization of GUS in inflamed tissues, where CD64+ IL-23–producing myeloid cells are increased and in proximity to IL-23–responsive lymphoid cells, enhancing GUS neutralization of IL-23 at its source of production. REFERENCES: [1] Mehta, H. et al. J Invest Dermatol. 2021;141:1707-1718. [2] Merola, J. et al. J Am Acad Dermatol. 2022;86:748-757. [3] Matt, P. et al. Scand J Rheumatol. 2015;44:464-473. Acknowledgements: NIL. Disclosure of Interests: Dennis McGonagle - Speakers bureau: AbbVie, Celgene, Janssen, Merck, Novartis, Pfizer, and UCB, Consultant: Abbvie, Celgene, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support: Abbvie, Celgene, Janssen, Merck, Pfizer, Novartis, Raja Atreya - Consultant: AbbVie, Amgen, Arena Pharmaceuticals, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion Healthcare, Dr. Falk Pharma, Ferring, Fresenius Kabi, Galapagos, Gilead, GlaxoSmithKline, InDex Pharmaceuticals, Janssen, Kliniksa Pharmaceuticals, Merk Sharp & Dohme, Novartis, Pfizer, Roche, Samsung Bioepsis, Stelic, Sterna Biologicals, Takeda, and Tillotts, Maria T. Abreu - Speakers bureau: Alimentiv, Janssen Pharmaceuticals, Prime CME, and WebMD Global LLC, Consultant and/or advisory board: AbbVie Inc, Arena Pharmaceuticals Inc (now Pfizer), Bristol Myers Squibb, Celsius Therapeutics, Eli Lilly and Company, Gilead Sciences Inc, Janssen Pharmaceuticals, Janssen Global Services, Pfizer Pharmaceutical, Prometheus Biosciences, and UCB Biopharma SRL, James G. Krueger - Consultant: AbbVie, Aclaris, Allergan, Almirall, Amgen, Arena, Aristea, Asana, Aurigene, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Escalier, Galapagos, Janssen, MoonLake, Nimbus, Novartis, Pfizer, Sanofi, Sienna, Sun, Target-Derm, UCB, Valeant, and Ventyx, Kilian Eyerich - Speakers bureau: AbbVie, Almirall, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Hexal, Janssen, Leo Pharma, Pfizer, Novartis, Sanofi, and UCB, Advisory board: AbbVie, Almirall, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Hexal, Janssen, LEO Pharma, Pfizer, Novartis, Sanofi, and UCB, Robert Bissonnette - Speakers bureau: AbbVie, Alumis, Amgen, AnaptysBio, Bausch Health, Boston, Bristol Myers Squibb, Celgene, Dermavant, Eli Lilly, Janssen, Leo Pharma, Nimbus, Novartis, Pfizer, Regeneron, UCB, VentyxBio and Xencor, Shareholder: Innovaderm Research, Employee: Innovaderm Research, Consultant and/or advisory board: AbbVie, Alumis, Amgen, AnaptysBio, Bausch Health, Boston, Bristol Myers Squibb, Celgene, Dermavant, Eli Lilly, Janssen, Leo Pharma, Nimbus, Novartis, Pfizer, Regeneron, UCB, VentyxBio and Xencor, Grant/research support: AbbVie, Alumis, Amgen, AnaptysBio, Bausch Health, Boston, Bristol Myers Squibb, Celgene, Dermavant, Eli Lilly, Janssen, Leo Pharma, Nimbus, Novartis, Pfizer, Regeneron, UCB, VentyxBio and Xencor, Carrie Greving - Shareholder: Johnson & Johnson, Employee: Janssen, He (Hurley) Li - Shareholder: Johnson & Johnson, Employee: Janssen, Tom C. Freeman - Shareholder: Johnson & Johnson, Employee: Janssen, Amy Hart - Shareholder: Johnson & Johnson, Employee: Janssen, Brice Keyes - Shareholder: Johnson & Johnson, Employee: Janssen, Brian Stoveken - Shareholder: Johnson & Johnson, Employee: Janssen, John Hartman - Shareholder: Johnson & Johnson, Employee: Janssen, Kristin Leppard - Shareholder: Johnson & Johnson, Employee: Janssen, Joshua Wertheimer - Shareholder: Johnson & Johnson, Employee: Janssen, Indra Sarabia - Shareholder: Johnson & Johnson, Employee: Janssen, Janise Deming - Shareholder: Johnson & Johnson, Employee: Janssen, Kristen Kohler - Shareholder: Johnson & Johnson, Employee: Janssen, Christopher T. Ritchlin - Consultant: AbbVie, Amgen, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support: AbbVie, Amgen, and UCB, Iain B. Mc Innes - Shareholder: Causeway and Evelo Compugen, board member: NHS GGC, board of directors: Evelo, trustee: Versus Arthritis, Consultant: AbbVie, Amgen, Astra Zeneca, Bristol Myers Squibb, Cabaletta, Compugen, Eli Lilly, Gilead, Glaxo Smith Kline, Janssen, Novartis, Pfizer, Roche, Sanofi, and UCB, Grant/research support: Amgen, Astra Zeneca, Bristol Myers Squibb, Eli Lilly, Glaxo Smith Kline, Janssen, Novartis, Roche, and UCB, Matthieu Allez - Speakers bureau: AbbVie, Amgen, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Ferring, Genentech, Gilead, IQVIA, Janssen, Novartis, Pfizer, Roche, Takeda, and Tillots, Consultant: AbbVie, Amgen, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Ferring, Genentech, Gilead, IQVIA, Janssen, Novartis, Pfizer, Roche, Takeda, and Tillots, Grant/research support: Genentech/Roche, Innate, Janssen and Takeda, Anne Fourie - Shareholder: Johnson & Johnson, Employee: Janssen, Kacey Sachen - Shareholder: Johnson & Johnson, Employee: Janssen.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,021
Tête enseignante GPT0,263
Écart entre enseignants0,242 · 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'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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