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Enregistrement W2905066790 · doi:10.1182/blood-2018-99-116013

Safety, Efficacy, Pharmacokinetic (PK) and Biomarker Analyses of BCL2 Inhibitor Venetoclax (Ven) Plus MDM2 Inhibitor Idasanutlin (idasa) in Patients (pts) with Relapsed or Refractory (R/R) AML: A Phase Ib, Non-Randomized, Open-Label Study

2018· article· en· W2905066790 sur OpenAlexaff
Naval Daver, Daniel A. Pollyea, Jacqueline S. Garcia, Brian A. Jonas, Karen Yee, Pierre Fenaux, Sarit Assouline, Norbert Vey, Rebecca L. Olin, Gail J. Roboz, Agostino Tafuri, Giuseppe Visani, Whitney P. Kirschbrown, Cherie Green, Connie Ma, Monique Dail, Jue Wang, Marion Ott, Mehrdad Mobasher, Patrick Phuong, Wan‐Jen Hong, Marina Konopleva, Michael Andreeff

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensJewish General HospitalPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésVenetoclaxMedicineRefractory (planetary science)Internal medicinePharmacokineticsVenOncologyPharmacologyLeukemiaChronic lymphocytic leukemiaBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Effective therapies for R/R AML remain limited. MEK or MDM2 inhibition can downregulate MCL1, overcoming resistance to BCL2 inhibition. Preclinical synergy was seen when combining BCL2 inhibitor Ven with MEK inhibitor cobimetinib (cobi) or MDM2 inhibitor idasa (Han et al. ASH 2016; Pan et al. Cancer Cell 2017), supporting clinical evaluation in AML. Preliminary data in a Phase Ib dose-escalation study (NCT02670044) evaluating Ven+cobi/idasa in R/R AML suggested both combinations were tolerable (Daver et al. ASH 2017). However, Ven+cobi was closed due to limited clinical activity. Here we present data for additional pts, longer follow-up and biomarker analyses for Ven+idasa. Methods This ongoing, open-label, multicenter study evaluates safety, tolerability and efficacy of Ven+idasa in R/R AML or secondary AML previously treated for an antecedent hematologic disease. Pts >60 yrs of age and ineligible for cytotoxic therapy/allogeneic stem cell transplant were enrolled. A 2-dimensional dose escalation was used to establish the maximum tolerated dose: pts received doses of Ven orally (PO) daily (400mg or 600mg) + idasa PO daily on Days 1-5 (150mg, 200mg, or 400mg) in 28-day cycles. Plasma samples were taken for PK analysis at Cycles 1 and 2 Days 1 and 5, and Cycle 4 Day 1. BCL2, BCLxL and MCL1 status and minimal residual disease (MRD) were assayed centrally at Covance Laboratories using multicolor flow cytometry. Mutation (mut) sequencing was performed by Foundation Medicine using FoundationOne Heme at screening and from last bone marrow collected on study. Results As of April 6 2018, 34 pts received Ven+idasa across all dose cohorts (Table 1). Median age: 74 (range 64-93) yrs; median prior therapies: 1 (range 1-4); ECOG performance status 2: 18%; refractory: 56%; secondary AML: 53%; adverse cytogenetics: 27%. Pre-therapy mut data were available for 32 pts; most common muts were RUNX1 14 (41%), ASXL1 11 (32%), SRSF2 11 (32%). Other significant pre-therapy muts: TP53 6 (18%), IDH2 7 (21%), IDH1 1 (3%), FLT3 4 (13%). The most common adverse events (AEs) were diarrhea (88%) and nausea (71%); the most common grade (Gr) ≥3 AEs were neutropenia (32%), febrile neutropenia (32%), thrombocytopenia (29%; Table 2). After 2 cases of Gr 3 diarrhea in the Ven 600mg cohorts, mandatory prophylaxis was implemented; no further cases of Gr ≥3 diarrhea were seen in the following 10 pts. Laboratory tumor lysis syndrome occurred in 3 pts (9%); none required treatment discontinuation. There was no apparent PK drug-drug interaction between Ven and idasa. PK was dose-proportional over the ranges tested for Ven and idasa. The recommended Phase II dose (RP2D) has not been identified yet. Across all dose cohorts, 30/34 pts were response-evaluable; the remaining 4 were still on study treatment without post-baseline response assessment. The anti-leukemic response rate (CR+CRp+CRi+MLFS+PR) was 37% (11/30). Across the 2 Ven 600mg cohorts, which are being considered for RP2D, the anti-leukemic response rate was 9/18 (50%) (Table 1, Figure 1). MRD negativity (<0.1%) was achieved in 43% (3/7) of pts with CR+CRp+CRi (Table 3). The median time to CR+CRp+CRi+PR (all pts) was 1.8 mo (range 0.8-2.7), with median response duration of 8.1 mo (range 0.3-9.7). Median overall survival in all pts and in the Ven 600mg cohorts was 3.9 mo and 5.3 mo (range 0.2-17.6), respectively; median follow-up was 2.9 mo (range 0-18). The anti-leukemic response rate was 86% in pts with IDH2 mut and 57% in pts with a RUNX1 mut, but only 20% in pts with a TP53 mut (Table 4). 8/20 pts with end-of-treatment mut data had either new TP53 muts or an increase in mut TP53 allele frequency (Figure 2). In 14 evaluable pts, those with AML blasts with a high ratio of BCL2:BCLxL or BCL2:MCL1 had a response rate of 100% (5/5) versus 11% (1/9) in pts with low ratios (Table 4). Conclusion Ven+idasa has a tolerable safety profile with appropriate prophylaxis in this R/R AML population. An anti-leukemic response rate of 50% was seen at dose levels being considered for RP2D (Ven 600mg + idasa 150/200mg). Overall, responses appeared deep and durable. Preliminary biomarker data indicate that the relative ratio of BCL2 to BCLxL and MCL1 may be important for Ven+idasa activity, whereas pts with baseline TP53 muts had lower response rates. To confirm the clinical benefit and safety of Ven+idasa, the combination will be further evaluated in an expansion arm, after confirmation of the RP2D. Disclosures Daver: Kiromic: Research Funding; ImmunoGen: Consultancy; Sunesis: Research Funding; Pfizer: Research Funding; Novartis: Research Funding; Novartis: Consultancy; Incyte: Research Funding; Daiichi-Sankyo: Research Funding; Sunesis: Consultancy; Karyopharm: Research Funding; Alexion: Consultancy; Pfizer: Consultancy; ARIAD: Research Funding; BMS: Research Funding; Otsuka: Consultancy; Incyte: Consultancy; Karyopharm: Consultancy. Pollyea:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Curis: Membership on an entity's Board of Directors or advisory committees; Argenx: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; Gilead: Consultancy; Celyad: Consultancy, Membership on an entity's Board of Directors or advisory committees. Garcia:Celgene: Consultancy. Jonas:Genentech/Roche: Research Funding; AbbVie: Consultancy, Research Funding; Amgen: Consultancy; Glycomimetics: Research Funding; Esanex: Research Funding; Pharmacyclics: Research Funding; Incyte: Research Funding; LP Therapeutics: Research Funding; Tolero: Consultancy; Forma: Research Funding; Celgene: Consultancy, Research Funding; Accelerated Medical Diagnostics: Research Funding; Kalobios: Research Funding; Daiichi Sankyo: Research Funding. Yee:Agensys, Astex, GSK, Onconova, Genentech/Roche: Research Funding; Celgene, Novartis, Otsuka: Membership on an entity's Board of Directors or advisory committees. Fenaux:Otsuka: Honoraria, Research Funding; Roche: Honoraria; Jazz: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Assouline:Roche: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Research Funding, Speakers Bureau; Pfizer: Honoraria, Research Funding, Speakers Bureau; Novartis: Research Funding. Olin:Daiichi Sankyo, Astellas, Genentech: Research Funding. Roboz:Cellectis: Research Funding; Janssen Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Roche/Genentech: Consultancy; Novartis: Consultancy; Astex Pharmaceuticals: Consultancy; Bayer: Consultancy; Orsenix: Consultancy; Pfizer: Consultancy; Argenx: Consultancy; Otsuka: Consultancy; Bayer: Consultancy; Aphivena Therapeutics: Consultancy; Roche/Genentech: Consultancy; Celgene Corporation: Consultancy; Jazz Pharmaceuticals: Consultancy; Otsuka: Consultancy; Jazz Pharmaceuticals: Consultancy; AbbVie: Consultancy; Novartis: Consultancy; Sandoz: Consultancy; Argenx: Consultancy; Eisai: Consultancy; Aphivena Therapeutics: Consultancy; Celgene Corporation: Consultancy; Orsenix: Consultancy; AbbVie: Consultancy; Astex Pharmaceuticals: Consultancy; Pfizer: Consultancy; Daiichi Sankyo: Consultancy; Eisai: Consultancy; Celltrion: Consultancy; Cellectis: Research Funding; Celltrion: Consultancy; Sandoz: Consultancy. Kirschbrown:Roche: Other: Ownership interests PLC; Genentech: Employment. Green:Genentech: Employment. Ma:Genentech: Employment. Dail:Genentech: Employment, Equity Ownership. Wang:Genentech Inc: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. Ott:Roche: Other: Ownership interests PLC. Mobasher:Genentech Inc: Employment; F. Hoffmann-La Roche Ltd: Other: Ownership interests non-PLC. Phuong:Genentech Inc: Employment, Equity Ownership, Other: Ownership interests PLC. Hong:Genentech Inc/Roche: Employment, Other: Ownership interests PLC. Konopleva:Stemline Therapeutics: Research Funding. Andreeff:Aptose: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Eutropics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncolyze: Equity Ownership; United Therapeutics: Patents & Royalties: GD2 inhibition in breast cancer ; SentiBio: Equity Ownership; Daiichi-Sankyo: Consultancy, Patents & Royalties: MDM2 inhibitor activity patent, Research Funding; Astra Zeneca: Research Funding; Reata: Equity Ownership; Amgen: Consultancy, Research Funding; Jazz Pharma: 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)0,001
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: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
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 Opus0,056
Tête enseignante GPT0,392
Écart entre enseignants0,336 · 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 non 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

Citations25
Publié2018
Routes d'admission1
Résumé présentoui

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