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Record W3213621509 · doi:10.1182/blood-2021-153170

A Phase I Study of the Combination of Venetoclax and Azacitidine in Relapse/Refractory Higher Risk Myelodysplastic Syndrome (MDS)

2021· article· en· W3213621509 on OpenAlexaboutno aff
Sai Prasad Desikan, Guillermo Montalban‐Bravo, Maro Ohanian, Naval Daver, Musa Yılmaz, Marina Konopleva, Tapan M. Kadia, Sangeetha Venugopal, Heather Schneider, Kelly S. Chien, Rashmi Kanagal‐Shamanna, Hagop M. Kantarjian, Guillermo Garcia‐Manero

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsVenetoclaxAzacitidineMedicineInternal medicineRegimenConcomitantMyelodysplastic syndromesOncologyRefractory (planetary science)CohortGastroenterologyLeukemiaBone marrow

Abstract

fetched live from OpenAlex

Abstract Background: The prognosis of patients with HMA refractory MDS is poor with a median OS of 6 months. [Lancet Oncology Apr 2016] The combination of Azacitidine and the Bcl-2 inhibitor Venetoclax has shown significant activity in patients with previously untreated higher risk MDS (Garcia et. al ASH 2019). We hypothesize that addition of venetoclax to Azacitidine will improve outcomes of R/R higher risk MDS. Methods: The phase I study (NCT04550442) is enrolling patients aged ≥ 18 years with adequate organ function, higher risk MDS with ≥5% blasts, and R/R to HMA therapy. Patients who are R/R to HMA therapy include those who progressed on HMA after 4 cycles or those who had an initial response with subsequent relapse. Prior BCL2 inhibitor therapy and patients with lower risk disease per IPSS-R were excluded. Azacitidine was administered on Days 1-5 at a dose of 75mg/m 2 IV. Venetoclax was administered daily on days 1-14. Cytoreduction was permitted to lower the white count to ≤ 10,000/µl prior to initiation of venetoclax. A 3+3 study design was applied to the regimen as demonstrated in Table 1. Doses were adjusted based on toxicity and concomitant CYP3A4 inhibitors. Results: Ten patients have been enrolled in this study to date. Baseline characteristics are shown in Table 2. In this entire cohort, the median age was 77 years (range 67 - 81) with a median bone marrow blast of 9%. The entire cohort was enriched with adverse risk mutations such as ASXL1(60%), TP53(40%), and RUNX1(30%) with a median number of 3 mutations (range, 2-12). Median hemoglobin was 7.5mg/dL, median platelet count was 40.5K/µL, median absolute neutrophil count (ANC) of 1.05K/µL, Cr 0.98mg/dL , and Bili 0.5mg/dL. No new safety signals were observed. No tumor lysis syndrome was observed. The most common grade ≥ 3 adverse events were cytopenias, predominantly neutropenia (40%) and thrombocytopenia (20%) that did not warrant dose reduction of venetoclax. Among the 10 patients enrolled, 1 patient is too early for response assessment. Among the 9 evaluable patients, the overall response rate (ORR) was 56%(n=5) with 1 patient achieving complete remission (CR) and 4 patients with marrow CR. All 3 nonresponders harbored TP53 mutation of which 2 had therapy related MDS. Among the responders, the responses were durable with a median duration of response not reached. Among the 10 patients, the 5 responders remain on therapy, one non-responder was switched to a different therapy, one patient died on day 58 due to pneumonia, and 1 patient each died due to sepsis and refractory disease respectively. At a median follow up of 5.5 months, the median overall survival was 7.1 months (range 1-9.5 months) (Figure 1). The 4- and 8-week mortality was 0% and 10% (n=1) respectively Conclusion: This study in higher risk patients with R/R MDS suggests potential benefit with the addition of Venetoclax to HMA with a 55% overall response and an OS of 7.1 months. TP53 and complex karyotypes still confer poor prognosis despite the addition of Venetoclax. Figure 1 Figure 1. Disclosures Daver: Astellas: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Trovagene: Consultancy, Research Funding; ImmunoGen: Consultancy, Research Funding; Glycomimetics: Research Funding; Genentech: Consultancy, Research Funding; Novartis: Consultancy; Bristol Myers Squibb: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Hanmi: Research Funding; Sevier: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Novimmune: Research Funding; Trillium: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Yilmaz: Daiichi-Sankyo: Research Funding; Pfizer: Research Funding. Konopleva: AbbVie: Consultancy, Honoraria, Other: Grant Support, Research Funding; KisoJi: Research Funding; Sanofi: Other: grant support, Research Funding; Calithera: Other: grant support, Research Funding; Agios: Other: grant support, Research Funding; Rafael Pharmaceuticals: Other: grant support, Research Funding; Stemline Therapeutics: Research Funding; Ascentage: Other: grant support, Research Funding; Cellectis: Other: grant support; AstraZeneca: Other: grant support, Research Funding; Forty Seven: Other: grant support, Research Funding; Reata Pharmaceuticals: Current holder of stock options in a privately-held company, Patents & Royalties: intellectual property rights; Ablynx: Other: grant support, Research Funding; Eli Lilly: Patents & Royalties: intellectual property rights, Research Funding; Novartis: Other: research funding pending, Patents & Royalties: intellectual property rights; Genentech: Consultancy, Honoraria, Other: grant support, Research Funding; F. Hoffmann-La Roche: Consultancy, Honoraria, Other: grant support. Kadia: Genentech: Consultancy, Other: Grant/research support; Ascentage: Other; AstraZeneca: Other; Genfleet: Other; Astellas: Other; Cellonkos: Other; Sanofi-Aventis: Consultancy; Pulmotech: Other; Novartis: Consultancy; Cure: Speakers Bureau; Liberum: Consultancy; Pfizer: Consultancy, Other; Dalichi Sankyo: Consultancy; BMS: Other: Grant/research support; Jazz: Consultancy; Amgen: Other: Grant/research support; Aglos: Consultancy; AbbVie: Consultancy, Other: Grant/research support. Kantarjian: Astellas Health: Honoraria; Pfizer: Honoraria, Research Funding; Ipsen Pharmaceuticals: Honoraria; Jazz: Research Funding; Astra Zeneca: Honoraria; Aptitude Health: Honoraria; Novartis: Honoraria, Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; BMS: Research Funding; Ascentage: Research Funding; Amgen: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; KAHR Medical Ltd: Honoraria; NOVA Research: Honoraria; Precision Biosciences: Honoraria; Taiho Pharmaceutical Canada: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.285
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes1
Has abstractyes

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