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Record W4310109045 · doi:10.1182/blood-2022-166429

Exposure-Response Analysis Supports a Lower Dose of Venetoclax in t(11;14)-Positive Relapsed/Refractory Multiple Myeloma Patients When Combined with Daratumumab and Dexamethasone

2022· article· en· W4310109045 on OpenAlexaff
Mohamed Badawi, Benjamin Engelhardt, Jesus D Badillo, Leanne Lash Fleming, Yan Luo, Jonathan L. Kaufman, Nizar J. Bahlis, Rajeev Menon, Ahmed Hamed Salem

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsVenetoclaxDaratumumabMedicineInternal medicineOncologyMultiple myelomaDexamethasonePopulationProportional hazards modelBortezomibGastroenterologyLeukemiaChronic lymphocytic leukemia

Abstract

fetched live from OpenAlex

Introduction: Venetoclax is a first in class BCL2 inhibitor approved for treatment of CLL and AML and is currently under investigation in other hematological malignancies including multiple myeloma (MM). This analysis aimed to select the optimal venetoclax dose in combination with daratumumab and dexamethasone (VenDd) in t(11;14)-positive relapsed/refractory MM patients using data from Study M15-654 (NCI: NCT03314181) which evaluated VenDd versus bortezomib in combination with daratumumab and dexamethasone (DVd). Methods: 66 subjects (47 receiving VenDd and 19 in the control arm receiving DVd) enrolled in Study NCI: NCT03314181 were included in the analysis. A population pharmacokinetic model was developed and individual subject venetoclax exposures (average area under the concentration time curve [AUCavg] and steady state AUC [AUCss] were determined using the individual post hoc empirical Bayes parameter estimates. The effect of the following covariates on the relationships for the efficacy and safety endpoints were evaluated: number of prior therapies, cytogenetic risk, international staging system (ISS) stage, t(11;14) status, bortezomib co-administration, and daratumumab route of administration. Kaplan-Meier curves and Cox proportional hazards models were used to describe the relationship between venetoclax exposures (AUCavg or AUCss) and progression free survival (PFS). Quartile plots and logistic regression models were used to describe the relationship between venetoclax exposure (AUCavg or AUCss) and the other efficacy and safety endpoints. Results: Exposure-response analysis demonstrated that compared to the control arm, venetoclax resulted in higher response rates (very good partial response or better [≥VGPR] and complete response or better [≥CR]) and longer PFS (PFS data not mature at time of analysis). Within the VenDd treatment arms, evaluating the exposure-response relationships over the venetoclax exposure range in patients receiving 400 mg or 800 mg venetoclax demonstrated that higher venetoclax exposures were not associated with an improved efficacy profile (PFS or ≥VGPR and ≥CR rates) compared to subjects with lower venetoclax exposures. While both 400 mg and 800 mg venetoclax were generally tolerated, higher venetoclax exposures (AUCavg and AUCss) trended with higher rates of treatment-emergent serious adverse events (any grade), and Grade ≥3 treatment-emergent adverse events (AUCss only). Higher venetoclax exposures were not associated with higher rates of Grade ≥2 infections, Grade ≥3 infections or Grade ≥3 neutropenia. Additionally, no other significant covariates were identified for any of the efficacy or safety endpoints evaluated. Conclusion: Exposure-response analyses support the selection and evaluation of the 400 mg QD dosing of venetoclax in combination with daratumumab and dexamethasone in t(11;14)-positive relapsed/refractory MM subjects in future studies. This is a lower dose than that selected for venetoclax when combined only with dexamethasone in t(11;14)-positive relapsed/refractory MM patients.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.245
Teacher spread0.236 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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