Real-World Adherence with Pegcetacoplan in Paroxysmal Nocturnal Hemoglobinuria Compared with Previously Reported Oral Medication Adherence Rates
Notice bibliographique
Résumé
Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare and severe hematologic disorder caused by somatic mutation(s) in hematopoietic stem cells, which results in dysregulation of complement activation and complement-mediated hemolysis. Several complement inhibitors are approved for PNH, including the intravenous or subcutaneous C5 inhibitors (C5is) eculizumab, ravulizumab, and crovalimab; the subcutaneous self-administered C3 inhibitor pegcetacoplan; and the oral factor B inhibitor iptacopan and factor D inhibitor danicopan (as add-on therapy to a C5i). Although oral self-administered medications would be expected to lighten treatment burden over injectable medications in patients with PNH, adherence to anticomplement therapies is essential to maintain stable hematologic response while avoiding adverse events of breakthrough hemolysis and thrombosis. Real-world adherence to pegcetacoplan in patients with PNH was evaluated and compared with reported real-world adherence to long-term oral therapies for chronic conditions. Methods: A descriptive review of recent literature was conducted to summarize real-word oral medication adherence rates in chronic conditions. The Medical Subject Headings “compliance, medication” and “administration, oral” were applied in PubMed, and yielded reports on oral antidiabetic medications (OADs) in type 2 diabetes, oral anticoagulants (OACs) in atrial fibrillation (AF), and oral oncolytics in hematologic cancers; observational, real-world studies reporting medication adherence rates were reviewed and summarized. Adherence to pegcetacoplan therapy among patients with PNH in the US postmarketing setting was calculated as the percentage of patients who take pegcetacoplan as prescribed (adherence definition) based on the number of days patients had pegcetacoplan vials in their possession (dispensed) divided by the total number of days in a dispensing period (proportion of days covered) using central pharmacy prescription refill data. Results: In general, patients who take ≥80% of prescribed doses are considered adherent; using this definition, adherence rates among patients receiving long-term therapies for chronic conditions are typically 50% to 60% but vary greatly [Kleinsinger F. Perm J. 2018;22:18-033]. For example, reported OAD adherence rates in type 2 diabetes vary across countries (42% in Switzerland [Huber CA et al. Medicine (Baltimore). 2016;95:e3994] to 67% in Canada [Simard P et al. Acta Diabetol. 2015;52:547]) and by medication types (36.7% with thiazolidinediones to 47.3% with dipeptidyl peptidase 4-inhibitors [Farr AM et al. Adv Ther. 2014;31:1287]). OAC adherence rates in AF range widely (~40% to ~90%), differing across countries, patient populations (incident AF vs. post cardiovascular event), OAC types (warfarin, direct OACs), and follow-up period lengths. OAC adherence is also influenced by patient demographic and clinical characteristics and regional determinants in US Medicare beneficiaries [Hernandez I et al. J Am Heart Assoc. 2019;8:e011427]. Interestingly, an inverse relationship between oral medication adherence and dosing frequency is observed in chronic diseases in general (progressively decreasing adherence rates from once-daily to twice, 3-times, and 4-times daily dosing regimens [Coleman CI et al. J Manag Care Pharm. 2012;18:527]), and with OACs in AF [Vrijens B, Heidbuchel H. Europace. 2015;17:514]. As with other chronic conditions, nonadherence to oral oncolytics in hematologic cancers is more prevalent than suspected (32.7%) and associated with poorer clinical response [Noens L et al. Blood. 2009;113:5401], which can further increase the economic burden on patients [Dashputre AA et al. J Manag Care Spec Pharm. 2020;26:186]. Pegcetacoplan adherence for PNH in the US postmarketing setting from launch (2021) to date (2024) was estimated at 97%, well above the 80% threshold defining medication adherence in the literature. Conclusions: Real-world patients with PNH who self-administer pegcetacoplan subcutaneously have adherence rates exceeding the reported real-world adherence rates for oral medications in chronic conditions, especially those with high dosing frequencies. Retaining a stable hematologic response and preventing potentially life-threatening complications in PNH relies on continuous patient adherence to lifelong anticomplement therapies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».