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Relationship between abiraterone exposure, prostate-specific antigen (PSA) kinetics, and overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC) .

2014· article· en· W2589352431 on OpenAlexaff
Steven Xu, Charles J. Ryan, Kim Stuyckens, Matthew R. Smith, Fred Saad, Thomas W. Griffin, Youn Choi Park, Margaret K. Yu, An Vermeulen, Italo Poggesi, Partha Nandy

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAbiraterone acetateProstate cancerMedicineProportional hazards modelProstate-specific antigenHazard ratioUrologyOncologyInternal medicineChemotherapyDocetaxelProstateCancerSurvival analysisAndrogen deprivation therapyConfidence interval

Abstract

fetched live from OpenAlex

39 Background: Abiraterone, the active metabolite of abiraterone acetate (AA), is an effective androgen biosynthesis inhibitor for patients with metastatic castration-resistant prostate cancer (mCRPC). We conducted a sequential exposure-biomarker-survival modeling analysis to explore the relationship between prostate-specific antigen (PSA) kinetics and overall survival (OS) and to establish the exposure response for PSA kinetics and OS in chemotherapy-naïve and -pretreated patients with mCRPC following AA administration. Methods: The exposure-PSA-survival modeling framework was based on two phase III studies, COU-AA-301 (chemotherapy-pretreated, N = 1184) and COU-AA-302 (chemotherapy-naïve, N = 1081), and included a mixed-effects tumor growth inhibition (TGI) model to describe PSA dynamics in response to AA and a Cox proportional hazards survival model to evaluate the relationship between relative risk of death and PSA dynamic end points. Results: The TGI model best described the longitudinal PSA dynamics following AA treatment. Abiraterone exposure significantly increased PSA decay rate (maximum effect of 2.72, p < 0.0001). The estimated concentration for 50% of the maximum effect (EC50) was 4.75 ng/mL. The abiraterone effect on PSA kinetics was similar in chemotherapy-naïve and -pretreated subjects, and approximately 90% of subjects had a steady-state concentration greater than the EC50. All model-predicted PSA metrics were strongly associated with OS in both populations; model-based post-treatment PSA doubling time showed the strongest association (hazard ratios approximately 0.9 in both populations). Simulations showed that the modeling framework could accurately predict the survival outcome for both studies. Conclusions: The analysis revealed a similar effect of abiraterone on PSA kinetics and association between PSA kinetics and OS in chemotherapy-naïve and -pretreated subjects, providing additional evidence for surrogacy of PSA kinetics and the use of PSA end points to indicate clinical benefit of abiraterone in subjects with mCRPC regardless of prior chemotherapy. Furthermore, the study confirmed that the recommended 1,000 mg/d dose of AA leads to adequate clinical exposure above the effective level. Clinical trial information: NCT00638690, NCT00887198.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.433
Teacher spread0.275 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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