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Enregistrement W4405034536 · doi:10.1182/blood-2024-207139

Olverembatinib (HQP1351) Overcomes Resistance/Intolerance to Asciminib and Ponatinib in Patients (pts) with Heavily Pretreated Chronic-Phase Chronic Myeloid Leukemia (CP-CML): A 1.5-Year Follow-up Update with Comprehensive Exposure-Response (E-R) Analyses

2024· article· en· W4405034536 sur OpenAlexaff
Elias Jabbour, Omer Jamy, Paul Koller, Vivian G. Oehler, Maria R. Baer, Elza Lomaia, Anthony M. Hunter, Olga Uspenskaya, Svetlana Samarina, Jörge E. Cortes, Sudipto Mukherjee, Dennis Dong Hwan Kim, Vera Zherebtsova, Vasily Shuvaev, Anna Turkina, И. Л. Давыдкин, Huanshan Guo, Zi Chen, Hengbang Wang, Tommy Fu, Lixin Jiang, Zhihong Yang, Cunlin Wang, Dajun Yang, Yifan Zhai, H. Kantarjian

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésPonatinibMedicineInternal medicineMyeloid leukemiaNilotinibGastroenterologyImatinib

Résumé

récupéré en direct d'OpenAlex

Introduction New treatment options are needed for pts with CP-CML resistant/intolerant to third-generation (3G) TKI ponatinib and/or asciminib, a specifically targeting the ABL myristoyl pocket (STAMP) inhibitor. Olverembatinib is a well-tolerated TKI with potential to overcome resistance. This update presents efficacy and safety data from a phase 1b study of olverembatinib in pts with heavily pretreated CP-CML. Methods Adults with CP-CML previously treated with ≥2 TKIs and/or a STAMP inhibitor, adequate organ function, and no major molecular response (MMR) were eligible. Pts were randomly allocated to receive olverembatinib at doses of 30, 40, or 50 mg orally every other day (QOD) in 28-day cycles, with stratification based on T315I mutation status. Comprehensive E-R analyses were performed. Results As of July 28, 2024, 67 pts with CP-CML were enrolled; median (range) follow-up was 74.3 (0.1-217.1) weeks; median (range) age, 50 (21-80) years; and 38 (56.7%) were male. At baseline, 19 (28.4%) pts had the T315I mutation; 34 (50.7%) had cardiovascular comorbidities; and BCR::ABL1 IS levels were ≥10% in 49 (73.1%) pts, 1% to 10% in 13 (19.4%), and <1% in 4 (6%); data for 1 pt were missing. The median (range) time from CML diagnosis to first olverembatinib dose was 6.3 (0.4-24.0) years. A total of 21 (31.3%) and 34 (50.7%) pts had received 3 or ≥4 TKIs, respectively. In pts treated with ponatinib (n = 32; 47.8%), 23 (71.9%) were resistant; 8 (25%), intolerant; and 1 (3.1%), uncategorized. In pts treated with asciminib (n = 20; 29.9%), 15 (75%) had resistance; 4 (20%), intolerance; and 1 (5%), uncategorized; 12 pts with CP-CML were resistant to both ponatinib and asciminib. Among 66 dosed subjects, a total of 62 (93.9%) reported TEAEs of any grade, with 44 (66.7%) experiencing ≥G3 TEAEs and 30 (45.5%) serious TEAEs. In addition, 60 (90.9%) pts reported TRAEs of any grade, with 30 (45.5%) experiencing ≥G3 TRAEs and 11 (16.7%) SAEs; 4 (6.1%) pts discontinued olverembatinib due to TRAEs (none fatal). Common TRAEs (≥20%) were elevated CPK (37.9%), thrombocytopenia (24.2%), and increased ALT (22.7%). Common ≥G3 TRAEs (≥10% incidence) included thrombocytopenia (16.7%), neutropenia (13.6%), and elevated CPK (12.1%). Treatment-related SAEs occurring in ≥2 (3%) pts included anemia, febrile neutropenia, and increased troponin (in 2 pts each). There was no report of treatment-related SAEs associated with arterial occlusive events. After a median (range) treatment duration of 59.4 (0.1-190.6) weeks, 30 (45.5%) pts required dose reductions, 42 (63.6%) had dose interruptions, and 22 (33.3%) discontinued treatment. Reasons for discontinuation included intolerance (n = 6), disease progression (n = 3), and other factors (n = 13), such as noncompliance, withdrawal, lack of response, and/or switching to transplantation. Thirty-five of 60 (58.3%) evaluable pts achieved CCyR and 29/64 (45.3%) MMR. The median (range) time to MMR was 91 (29-489) days. At 12 months, the MMR rate was 61.4% (27/44). Comparable response rates were observed regardless of T315I mutation status, with CCyR achieved by 66.7% of pts with the T315I mutation vs 54.8% without it, and MMR achieved by 50.0% vs 43.5%, respectively. MMR rates for pts treated with 2, 3, or ≥4 TKIs were 66.7%, 40.0%, and 40.6%, respectively. Of 28 cytogenetic response-evaluable pts with ponatinib-failed CP-CML, 15 (53.6%) achieved CCyR, including 12/23 (52.2%) with prior ponatinib resistance and 3/4 (75.0%) with intolerance. A total of 12/30 (40.0%) evaluable pts previously treated with ponatinib achieved MMR, including those with prior resistance (11/23 [47.8%]) or intolerance (1/6 [16.7%]). In evaluable pts with asciminib treatment failure, 37.5% (6/16) achieved CCyR and 30% (6/20) MMR, including those with prior resistance (4/13 [30.8%] in CCyR, 4/15 [26.7%] in MMR) or intolerance (1/2 [50.0%] in CCyR, 1/4 [25.0%] in MMR). CCyR and MMR rates in pts previously treated with both ponatinib and asciminib were 30% and 25%, respectively. The comprehensive E-R analyses show a positive exposure-efficacy correlation with no significant exposure-safety relationship for Grade 3+ TRAEs or TEAEs, serious TEAE, serious TRAEs, and neutropenia. Conclusions Olverembatinib was well tolerated and showed strong and durable antileukemic activity in pts with heavily pretreated CP-CML. The registrational study (POLARIS-2, NCT06423911) is recruiting.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,001
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
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 savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,017
Tête enseignante GPT0,294
Écart entre enseignants0,277 · 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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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