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Dasatinib-Associated Major Molecular Responses Are Rapidly Achieved in Patients with Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Following Resistance, Suboptimal Response, or Intolerance on Imatinib.

2008· article· en· W2982074944 on OpenAlexaff
Andreas Hochhaus, Martin C. Müller, Jerald P. Radich, Susan Branford, Benjamin Hanfstein, Philippe Rousselot, Jeffrey H. Lipton, Eric Bleickardt, Ritwik Sinha, Timothy P. Hughes

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsDasatinibNilotinibImatinibMedicineInternal medicineImatinib mesylateMyeloid leukemiaOncologyPharmacologyImmunology

Abstract

fetched live from OpenAlex

Abstract Dasatinib (SPRYCEL®) is a highly potent BCR-ABL inhibitor, with 325-fold higher potency than imatinib and a 16-fold higher potency than nilotinib in vitro. Across a series of phase II and III trials with more than 2 years of follow-up, dasatinib has demonstrated durable efficacy in patients with CML following resistance, suboptimal response, or intolerance to imatinib. Qualitative and quantitative assessment of BCR-ABL transcripts by RT-PCR is the most sensitive method for assessing minimal residual disease in patients with CML. In the treatment of patients with CML in the first-line setting, achievement of a major molecular response (MMR) within 18 months of therapy is considered to be clinically important. Here, molecular responses to dasatinib in patients with CML-CP following resistance, suboptimal response, or intolerance to imatinib were analyzed using data from two phase II studies (CA180-013 [START-C], -017 [START-R]) and a phase III dose-optimization study (CA180-034). BCR-ABL mRNA levels were determined in peripheral blood samples using real-time quantitative PCR and results were standardized using the international scale. A MMR was defined as a reduction of BCR-ABL transcripts to ≤ 0.1%. Samples were assayed at one of four laboratories (Mannheim, Germany; Adelaide, Australia; or Seattle, USA for −013 and −017; and Wallingford, USA for −034). Of 1,158 patients with CML-CP who were randomized to (017) or treated with (013/034) dasatinib, 1,067 had a molecular assessment and were included in the analysis. After 3, 6, 12, and 24 months of follow-up, a MMR was achieved by 12%, 22%, 35%, and 40%, respectively, of all patients analyzed. The 24-month MMR rate among patients who had achieved or maintained a complete cytogenetic response was 72%. In patients with imatinib resistance/suboptimal response (n=829), overall MMR rates after 3, 6, 12, and 24 months were 10%, 18%, 29%, and 34%, respectively, and in patients with imatinib intolerance (n=238), MMR rates were 22%, 37%, 55%, and 63%, respectively. In patients treated during the 034 study with dasatinib 100 mg once daily, which is associated with fewer key side effects, MMR rates at different time points were similar to rates with other dose schedules (24-month MMR: 100 mg QD, 36%; other schedules, 38%). Among all patients who achieved a MMR, median time to MMR was 5.7 months (6.0 months with 100 mg QD), and after 18 months, 92% of responding patients were without loss of MMR (88% with 100 mg QD). Overall, the results of this analysis demonstrate that high rates of MMR are rapidly achieved in patients with CML-CP treated with dasatinib following resistance, suboptimal response, or intolerance to imatinib. Table Major molecular response (%) Follow-up (months) 3 6 12 24 All analyzed patients (n=1067) 12 22 35 40 Resistant/suboptimal response (n=829) 10 18 29 34 Intolerant (n=238) 22 37 55 63 Phase II studies (013/017) (n=467) 17 27 39 44 Resistant (n=373) 13 21 31 35 Intolerant (n=94) 33 50 69 78 Phase III dose-optimization study (034) (n=600) 9 19 32 38 Resistant/suboptimal response (n=456) 7 16 27 33 Intolerant (n=144) 15 29 46 54 Response by dose schedule 100 mg QD (n=154) 7 18 29 36 70 mg BID (n=146) 9 18 32 38 140 mg QD (n=144) 13 22 32 38 50 mg BID (n=156) 7 17 34 38

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.264
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations16
Published2008
Admission routes1
Has abstractyes

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