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Enregistrement W3213000652 · doi:10.1182/blood-2021-147317

Mitapivat Improves Ineffective Erythropoiesis and Reduces Iron Overload in Patients with Pyruvate Kinase Deficiency

2021· article· en· W3213000652 sur OpenAlexaff
Eduard J. van Beers, Hanny Al‐Samkari, Rachael F. Grace, Wilma Barcellini, Andreas Glenthoej, Malia P. Judge, Penelope A. Kosinski, Emily Xu, Vanessa Beynon, Bryan McGee, John B. Porter, Kevin H.M. Kuo

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueErythrocyte Function and Pathophysiology
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicinePyruvate kinase deficiencyTransferrin saturationAnemiaIneffective erythropoiesisSoluble transferrin receptorInternal medicineFerritinErythropoiesisHepcidinGastroenterologyIron deficiencyPyruvate kinase

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Pyruvate kinase (PK) deficiency is a rare hereditary disease resulting in chronic hemolytic anemia, which is associated with serious complications, including iron overload, regardless of transfusion status. Ineffective erythropoiesis is linked to iron overload in patients (pts) with hemolytic anemias. Mitapivat is a first-in-class, oral, allosteric activator of the red blood cell PK enzyme (PKR) that has demonstrated improvement in hemoglobin (Hb), hemolysis, and transfusion burden in pts with PK deficiency. This analysis assessed the effect of mitapivat on markers of erythropoiesis and iron overload in pts with PK deficiency enrolled in 2 phase 3 studies, ACTIVATE (NCT03548220) and ACTIVATE-T (NCT03559699), and the long-term extension (LTE) study (NCT03853798). Methods: In ACTIVATE (double-blind, placebo-controlled study), 80 pts (age ≥ 18 years [yrs]) with a confirmed diagnosis of PK deficiency who were not regularly transfused (≤ 4 transfusion episodes in the prior yr; none in the prior 3 months) were randomized to receive mitapivat or placebo. In ACTIVATE-T (open-label, single-arm study), 27 pts (age ≥ 18 yrs) with a confirmed diagnosis of PK deficiency who were regularly transfused (≥ 6 transfusion episodes in the prior yr) were treated with mitapivat. Pts who completed either trial (24 weeks [wks] [ACTIVATE], 40 wks [ACTIVATE-T]) were eligible to continue in the LTE. Erythropoiesis markers included erythropoietin (EPO), erythroferrone, reticulocytes, and soluble transferrin receptor (sTfR). Markers of iron overload included hepcidin, iron, transferrin saturation (TSAT), ferritin, and liver iron concentration (LIC) by magnetic resonance imaging (MRI). In the LTE all pts received mitapivat. Pts from ACTIVATE were categorized into either the mitapivat-to-mitapivat arm (M/M) or the placebo-to-mitapivat arm (P/M). The ACTIVATE-T/LTE analysis includes pts who achieved transfusion-free status in ACTIVATE-T. The ACTIVATE/LTE analysis assessed change in markers from baseline (BL) over time in both study arms. Results: Eighty pts were included in the ACTIVATE/LTE analysis (M/M = 40; P/M = 40). Pts in both arms had abnormal BL erythropoiesis markers consistent with underlying ineffective erythropoiesis, and BL abnormal markers of iron overload. In the M/M arm, mean (SD) EPO, erythroferrone, reticulocytes, and sTfR decreased from BL to Wk 24 of mitapivat treatment by -32.9 IU/L (62.47), -9834.9 ng/L (13081.15), -202.0 10 9/L (246.97), and -56.0 nmol/L (82.57), respectively, while they remained stable or increased in the P/M arm on placebo (Figure). Twenty-four wks after starting mitapivat in the LTE (Wk 48 post BL), pts in the P/M arm had comparable beneficial decreases in mean (SD) EPO, erythroferrone, reticulocytes, and sTfR of -11.6 IU/L (30.74), -9246.1 ng/L (8314.17), -283.7 10 9/L (374.27), and -38.7 nmol/L (48.37), respectively. Improvements in hepcidin, iron, TSAT, and LIC were also observed with mitapivat treatment; ferritin remained stable (Table). Mean (SD) hepcidin increased in the M/M arm at Wk 24 and in the P/M arm 24 wks after starting mitapivat (Wk 48 post BL). At Wk 24, mean (SD) iron and TSAT, and median (Q1, Q3) LIC decreased in the M/M arm, while they increased on placebo. In the P/M arm, iron, TSAT, and LIC decreased 24 wks after starting mitapivat (Wk 48 post BL). Transfusion-free responders from ACTIVATE-T (n = 6) also experienced improvements in markers of erythropoiesis and iron overload in the LTE. Conclusions: In addition to improving Hb, hemolysis, and transfusion burden, data from ACTIVATE, ACTIVATE-T, and the LTE study indicate that activation of PKR with mitapivat improves markers of ineffective erythropoiesis and iron homeostasis in PK deficiency, thereby decreasing iron overload in these pts. Mitapivat has the potential to become the first approved therapy in PK deficiency with beneficial effect on iron overload. Figure 1 Figure 1. Disclosures Van Beers: Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; RR Mechatronics: Research Funding; Pfizer: Research Funding. Al-Samkari: Amgen: Research Funding; Argenx: Consultancy; Rigel: Consultancy; Novartis: Consultancy; Dova/Sobi: Consultancy, Research Funding; Agios: Consultancy, Research Funding; Moderna: Consultancy. Grace: Agios: Research Funding; Dova: Membership on an entity's Board of Directors or advisory committees, Research Funding; Principia: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding. Barcellini: Bioverativ: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Alexion Pharmaceuticals: Honoraria; Novartis: Honoraria; Agios: Honoraria, Research Funding. Glenthoej: Bluebird Bio: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Calgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alexion: Research Funding; Novo Nordisk: Honoraria. Judge: Agios Pharmaceuticals: Current Employment, Current holder of stock options in a privately-held company. Kosinski: Agios Pharmaceuticals: Current Employment, Current equity holder in publicly-traded company. Xu: Agios Pharmaceuticals: Current Employment, Current equity holder in publicly-traded company. Beynon: Agios Pharmaceuticals: Current Employment, Current equity holder in publicly-traded company. McGee: Agios Pharmaceuticals: Current Employment, Current equity holder in publicly-traded company. Porter: La Jolla Pharmaceuticals: Honoraria; Protagonism: Honoraria; Agios: Consultancy, Honoraria; bluebird bio, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene (BMS): Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Vifor: Honoraria, Membership on an entity's Board of Directors or advisory committees; Silence Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kuo: Celgene: Consultancy; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Alexion: Consultancy, Honoraria; Bioverativ: Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Research Funding; Bluebird Bio: Consultancy; Apellis: Consultancy.

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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
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)0,0020,000

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 GPT0,209
Écart entre enseignants0,204 · 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'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

Citations3
Publié2021
Routes d'admission1
Résumé présentoui

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