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Enregistrement W4417015862 · doi:10.1182/blood-2025-5549

Improved long-term tolerability with asciminib (ASC) vs investigator-selected (IS) tyrosine kinase inhibitors (TKIs) in patients (pts) with newly diagnosed chronic myeloid leukemia in chronic phase (CML-CP): Week 96 exploratory analysis of the phase 3 ASC4FIRST trial

2025· article· en· W4417015862 sur OpenAlexaff
Timothy P. Hughes, Jörge E. Cortes, Jennifer E. Vaughn, Kathryn E. Flynn, Naoto Takahashi, Ghayas C. Issa, Felice Bombaci, Jianxiang Wang, Dong‐Wook Kim, Dennis Kim, Jiří Mayer, Yeow Tee Goh, Philipp le Coutre, In Ho Kim, Gabriel Étienne, Rajendra Jinwal, Andrea Damon, David Wei, Gabriel Marquez, Ennan Gu, Gopi Madhav Bommidi, Andreas Hochhaus, Richard A. Larson

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésTolerabilityDiscontinuationAdverse effectRandomized controlled trialRegimenMultivariate analysisExploratory analysis

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Long-term CML treatment (Tx) requires assessing adverse event (AE) burden over time to optimize safety, tolerability, and efficacy. ASC4FIRST (NCT04971226) results demonstrated superior efficacy and favorable safety/tolerability of ASC vs IS-TKIs in newly diagnosed CML-CP. A prior report showed a lower risk of discontinuation due to AEs with ASC vs second-generation (2G) TKIs in a time to Tx discontinuation due to AEs (TTDAE) analysis (hazard ratio, 0.46; 95% CI, 0.215-0.997), suggesting better tolerability with ASC. We report exploratory post hoc analyses, including an innovative analysis of AE-free days, further evaluating the tolerability of ASC vs IS-TKI (imatinib [IMA]/2G TKIs) by the wk 96 analysis cutoff (Oct 22, 2024). Methods Adults with newly diagnosed CML-CP were randomized 1:1 to receive ASC or IS-TKI (at label dose), stratified by ELTS risk category and prerandomization IS-TKI (IMA/2G TKIs). Dose modifications occurred per protocol for ASC and at investigator's discretion for IS-TKIs. All reported AEs occurred on Tx or ≤30 days after last dose. Exploratory tolerability analyses included frequency/grade/type of AEs; relative dose intensity; rate of dose adjustment, interruption, or discontinuation; percentage of AE-free days (number of days pt was on Tx without any-grade AEs divided by Tx duration in days, censored at the wk 96 visit; a value of 100% indicates no AEs were experienced on Tx); and pt-reported outcome (PRO) measures (PRO-CTCAE and FACIT GP5). Results Safety analyses included all pts who received Tx in IMA (ASCIMA [n=100]; IS-TKIIMA [n=99]) and 2G TKI (ASC2G [n=100]; IS-TKI2G [n=102]) strata. Median follow-up was similar across groups (ASCIMA [25.1 mo]; IS-TKIIMA [24.3 mo]; ASC2G [28.2 mo]; IS-TKI2G [27.7 mo]). More pts were continuing Tx at cutoff with ASCIMA (83.0%) vs IS-TKIIMA (52.5%) and ASC2G (81.0%) vs IS-TKI2G (69.6%). More pts had relative dose intensity of >90% with ASCIMA (86.0%) vs IS-TKIIMA (79.8%) and ASC2G (88.0%) vs IS-TKI2G (68.6%). The proportion of pts with dose reductions was lower with ASCIMA (n=19, 19.0%) vs IS-TKIIMA (n=23, 23.2%) and ASC2G (n=18, 18.0%) vs IS-TKI2G (n=56, 54.9%). A similar number of pts with ASCIMA (47.0%) vs IS-TKIIMA (47.5%) and fewer pts with ASC2G (46.0%) vs IS-TKI2G (63.7%) had dose interruptions for any reason (included AEs, dosing/dispensing error, physician/pt decision, and technical problems); interruptions due to AEs were fewer with ASC vs IS-TKIs (ASCIMA [33.0%] vs IS-TKIIMA [37.4%]; ASC2G [33.0%] vs IS-TKI2G [51.0%]). Less pts with ASC vs IS-TKIs had dose adjustment and/or interruption (ASCIMA [13.0%] vs IS-TKIIMA [18.2%]; ASC2G [14.0%] vs IS-TKI2G [31.4%]) and discontinuation (ASCIMA [3.0%] vs IS-TKIIMA [6.1%]; ASC2G [1.0%] vs IS-TKI2G [7.8%]) due to grade 1/2 nonhematologic AEs, most common (≥3%) being diarrhea, fatigue, COVID-19, nausea, and pleural effusion. A lower proportion of pts with ASC vs IS-TKIs discontinued due to grade ≥3 nonhematologic AEs (ASCIMA [2.0%] vs IS-TKIIMA [4.0%]; ASC2G [2.0%] vs IS-TKI2G [3.9%]). Dose adjustment and/or interruption due to grade ≥3 nonhematologic AEs occurred in 17.0% vs 8.1% of pts with ASCIMA vs IS-TKIIMA and 11.0% vs 17.6% with ASC2G vs IS-TKI2G. Pts with ASC vs IS-TKIs had a higher median percentage of AE-free days by wk 96 (ASCIMA [15.7%] vs IS-TKIIMA [3.5%]; ASC2G [22.6%] vs IS-TKI2G [4.3%]) and vs individual 2G TKIs (nilotinib [13.5%]; dasatinib [4.2%]; bosutinib [0.1%]) in this arm. Pts with ASC had a higher proportion of symptomatic (nonhematologic, nonlaboratory AEs) AE–free days (ASCIMA [31.2%] vs IS-TKIIMA [9.9%]; ASC2G [34.2%] vs IS-TKI2G [15.7%]; and vs individual 2G TKIs: nilotinib [30.3%]; dasatinib [8.5%]; bosutinib [0.1%]). When analyzing maximum scores in PRO-CTCAE reported from baseline to wk 96, symptoms reported with ASC vs IS-TKIs were generally less frequent and less severe, with a smaller impact on daily life. At wk 96, more pts reported being not at all bothered by Tx side effects with ASC vs IS-TKIs per FACIT GP5 (67.0% vs 46.1%). Conclusions Pts with ASC had more days free from AEs, especially symptomatic AEs, and reported a lower symptom burden. Effective relative dose intensity (>90%) was higher with ASC vs IS-TKIs, with generally fewer pts requiring dose reduction, interruption, or discontinuation. These results from ASC4FIRST further support ASC’sfavorable tolerability vs all current frontline TKIs.

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)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,013
Tête enseignante GPT0,263
Écart entre enseignants0,250 · 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'étudeEssai randomisé
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
Publié2025
Routes d'admission1
Résumé présentoui

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