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Enregistrement W4388833789 · doi:10.1097/01.cot.0000996488.11406.21

Immunoglobulin May Prevent Infections in Patients With Multiple Myeloma

2023· article· en· W4388833789 sur OpenAlexaboutno aff

Notice bibliographique

RevueOncology Times · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaAntibodyImmunologyMedicineImmunoglobulin G

Résumé

récupéré en direct d'OpenAlex

Multiple Myeloma: Multiple MyelomaBispecific antibodies targeting the BCMA protein are increasingly employed in the treatment of multiple myeloma, with two agents recently approved by the FDA to treat this blood cancer. While anti-BCMA bispecific antibodies have exhibited impressive efficacy against heavily pretreated multiple myeloma, there has been a high rate of serious, sometimes lethal, infections in patients receiving these therapies, explained Guido Lancman, MD, Clinical Associate at the Princess Margaret Cancer Centre of the University Health Network and Adjunct Assistant Professor at the University of Toronto. Prior research from Lancman and colleagues suggested that the increased risk of infection during anti-BCMA therapy may be caused by treatment-induced depletion of the patient's own antibodies, a condition known as hypogammaglobulinemia. “Since antibodies are key components of the immune response, the inability to make antibodies leaves patients vulnerable to all sorts of viral and bacterial infections,” Lancman noted. “As more and more patients start receiving BCMA-targeted bispecific antibodies, it is critical that physicians become aware of this toxicity and learn how to manage it.” Study Details Lancman and colleagues hypothesized that supplementing patient antibody levels through intravenous (IV) delivery of donor antibodies—also known as immunoglobulins (Ig)—might mitigate their risk of infection. To test this hypothesis, they conducted a retrospective analysis of 37 patients with heavily pretreated multiple myeloma who had received treatment with an anti-BCMA bispecific antibody. All patients were enrolled in one of four clinical trials at Mount Sinai Hospital between 2019 and 2022. Among the 26 patients who experienced clinical responses to an anti-BCMA bispecific antibody, 100 percent had severe hypogammaglobulinemia (defined as IgG levels below 200 mg/dL), and approximately 92 percent received IVIg at some point during treatment. During a combined 424 months of follow-up, patients experienced a total of 118 infections, including 26 severe infections (grades 3-5) among 15 patients. The authors found that the rate of severe infection was 90 percent lower during times when patients were receiving IVIg compared to when they were not receiving IVIg. No other significant risk factors for infection were found in this study. “This study demonstrates that IVIg is associated with a substantially reduced risk of serious infections in patients receiving anti-BCMA bispecific antibodies,” Lancman said. “Given the very high rates of serious infections and deaths in patients receiving these treatments, this study supports a proactive rather than a reactive approach, meaning initiation of IVIg prophylaxis from the beginning rather than waiting for patients to experience complications.” Since the patients' own antibodies did not recover while on treatment or during periods off treatment lasting up to 13 months, Lancman suggested that IVIg may need to be given throughout the duration of anti-BCMA bispecific antibody therapy and possibly for some time afterward. However, he noted that alternative strategies will need to be considered if anti-BCMA therapies are used for earlier lines of treatment. “It would not be feasible to maintain every multiple myeloma patient on IVIg indefinitely, so hopefully we will start to see more fixed-duration studies of these bispecific antibodies in order to allow the immune system the opportunity to recover,” he noted. Limitations of the study include the small sample size and the non-random use of IVIg. In addition, since the analysis was conducted on patients enrolled in clinical trials at a single institution, the results may not be representative of the general patient population.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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 recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,017
Tête enseignante GPT0,317
Écart entre enseignants0,301 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
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
Publié2023
Routes d'admission1
Résumé présentoui

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