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Enregistrement W3217194789 · doi:10.1182/blood-2021-151761

Real-Life Use of Nilotinib for Chronic Phase CML Demonstrates Similar Efficacy and Rate of Cardiovascular Events As Enestnd

2021· article· en· W3217194789 sur OpenAlexaffabout
Olivier Del Corpo, Michaël Harnois, Lambert Busque, Sarit Assouline

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensJewish General HospitalHôpital Maisonneuve-RosemontQuebec - Clinical Research Organization in CancerCentre Hospitalier de l’Université de MontréalMcGill University
Organismes subventionnairesnon disponible
Mots-clésNilotinibMedicineInternal medicineImatinibMyeloid leukemiaDasatinibImatinib mesylate

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Nilotinib is a second-generation BCR-ABL1 tyrosine kinase inhibitor (TKI) widely used for the treatment of Philadelphia chromosome positive chronic myeloid leukemia (CML). ENESTnd, which compared two doses of nilotinib to imatinib for patients with newly diagnosed CML, demonstrated superior rates of major molecular responses (MMRs) at 12 months in patients treated with nilotinib. However, a relative increase in cardiovascular events (CVE) was observed, including ischemic heart disease, stroke, and/or peripheral artery disease among nilotinib-treated patients. Notably, at a median 5-year follow up, 7.5% of patients on nilotinib 300mg twice daily experienced a CVE (Hochaus, Leukemia, 2016) and by 10-years of follow up, approximately 20% of patients treated with nilotinib developed a CVE (Kantarjian, Leukemia, 2021). The baseline Framingham general cardiovascular risk scores and total dose exposure were predictive of patients' risk of developing CVE. In the province of Quebec, Canada, the GQR LMC-NMP (Groupe Québécois de recherche en leucémie myéloide chronique et néoplasie myéloproliférative) has maintained a registry of nearly all patients with CML since 2011. Using this 900+-patient registry, we examined molecular responses and the rate of CVE among patients receiving nilotinib as frontline therapy for CML, to determine if they were similar to those observed in ENESTnd. Methods: We identified all patients with chronic phase CML in the registry who were initiated on nilotinib for chronic phase CML. Baseline patient characteristics included were age, sex, Sokal index, baseline comorbidities, molecular response, cardiovascular complications, duration of exposure to nilotinib, dose adjustment, dose discontinuation and CVE on therapy. Results: In total, 94 patients received nilotinib as frontline treatment starting in 2008 (Table 1). The median age of the population was 58 years, 50.5% were male. The most common cardiovascular comorbidity prior to nilotinib treatment was hypertension (23.4%) followed by elevated cholesterol (21.3%), and prior CVE (12%). Seventeen patients had a low Sokal index, 26 intermediate and 24 high, 25 missing. Sixty-five patients received nilotinib at a total of 600 mg per day, 29 patients had a dose reduction at some point in their treatment and 18 patients were on either 300 or 400 mg daily. The median follow-up time was 67.0 months (range 0.5 to 159), and median nilotinib exposure time was 38.5 months (range 0.5 to 144). Forty-six patients (48.9%) discontinued nilotinib, 6 for a treatment free remission (TFR). While on nilotinib, 63 patients achieved MMR (72.4%) and 43 achieved MR 4.5 (49.6%) by 5 years of treatment, with 52 (55.3%) and 22 (23.4%) patients achieving MMR or MMR4.5 within a year, respectively. There was a total of 7 CVE in 6 patients: six myocardial infarctions and one ischemic stroke (Table 2). No patients developed peripheral artery disease. The mean time of exposure to nilotinib in these 6 patients was 28.6 months. Two patients had a high Sokal index, 3 intermediate and 1 low. One patient received dose-reduced nilotinib. These patients were co-morbid with diabetes (3), hypertension (3), elevated cholesterol (1) and one had a myocardial infarct prior to starting nilotinib, two patients did not have any risk factors. Only one of these patients has remained on nilotinib. All patients with a CVE were alive at the time of this report. Conclusion: We present real world data on the efficacy and safety of nilotinib in the frontline treatment of CML. Our data indicate that molecular responses and toxicities are similar to the ENESTnd trial. Of the 94 treated patients, the rate of MMR and CVE were 72% and 7.4% at 5.5 years of follow up, respectively, which is similar to that observed in ENESTnd, where 77% of patients experienced MMR and 7.5% CVE at this timepoint (Hochaus, Leukemia, 2016). All but one patient with a CVE in our study continued nilotinib, suggesting a reluctance among physicians to continue in the face of CVE. While there were 11 patients with prior CVE, only one of these developed a CVE on nilotinib. Current GQR-LMC CML management guidelines recommend estimation of Framingham score and management of cardiovascular risk factors for all patients with CML. Future follow up of these registry data may demonstrate a decrease in the rate of CVE among nilotinib treated patients, reflecting local adherence to these guidelines. Figure 1 Figure 1. Disclosures Busque: Novartis: Consultancy. Assouline: Eli Lilly: Research Funding; Jewish General Hospital, Montreal, Quebec: Current Employment; Novartis: Honoraria, Research Funding; Amgen: Current equity holder in publicly-traded company, Research Funding; Gilead: Speakers Bureau; Johnson&Johnson: Current equity holder in publicly-traded company; Roche/Genentech: Research Funding; Takeda: Research Funding; BeiGene: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria.

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)0,002
score de la tête « metaresearch » (Gemma)0,004
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
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 savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,036
Tête enseignante GPT0,303
Écart entre enseignants0,267 · 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'admission2
Résumé présentoui

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