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Efficacy and safety of apalutamide in metastatic castration sensitive prostate cancer patients with a prior history of cardiovascular or metabolic risk factors: A post-hoc analysis of the TITAN study.

2025· article· en· W4407702418 on OpenAlexaff
Arun Azad, Dingwei Ye, Hiroji Uemura, Amitabha Bhaumik, Michael Eisbacher, Anildeep Singh, Sharon McCarthy, Suneel Mundle, Kim N., Neeraj Agarwal

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
FundersJohnson and Johnson
KeywordsMedicineProstate cancerPost-hoc analysisOncologyPost hocInternal medicineProstateCancerGynecology

Abstract

fetched live from OpenAlex

165 Background: Patients with CV issues, such as MI, symptomatic congestive heart failure, or thromboembolic events occurring ≤6 months of randomization were excluded from the TITAN trial. However, patients with events occurring > 6 months prior or non-excluded CV conditions like IHD without MI were enrolled. Considering the demographics of prostate cancer and given prolonged ADT use may worsen existing co-morbid conditions, we conducted a post-hoc analysis to assess the efficacy and safety of apalutamide +ADT (APA) vs placebo+ADT(PBO) in patients with ≥ 1 risk factor or a history of CV or metabolic risk factors. Methods: CV and metabolic risk factors were categorized using MeDRA terminology and included CV ischemia, CV failure, CV arrhythmia, diabetes, hyperlipidemia, HT and obesity. Use of associated concomitant medications (con meds) were identified at study entry. Data from the final analysis after 44 months of median follow up were analysed for the co-primary endpoints of rPFS and OS, along with PSA90 or PSA<0.2ng/ml and TEAEs in patients with or without CV risk factors, and with con meds for these conditions. Results: In TITAN, 72% (378/524) and 69% (364/527) patients in the APA and PBO arms had a history of CV or metabolic risk factors at baseline; 68% (358/524) and 66% (347/527) were receiving con meds for these conditions. Individual risk factors were evenly matched between arms. All efficacy endpoints rPFS, OS, PSA90 and PSA<0.2ng/ml were superior in the APA group vs PBO group irrespective of CV/metabolic Risk and with con meds . Incidence of TEAEs were similar between subjects with and without CV & metabolic risk and with concomitant medications at baseline (Table). Conclusions: A large majority of patients enrolled in TITAN reflected an elderly population with a considerable CV risk profile. APA resulted in a significant improvement in both rPFS and OS and a favourable safety profile regardless of prior CV and metabolic baseline risk or with con meds at baseline for these conditions. Clinical trial information: NCT02489318 . With CV/metabolic risk factors at baseline Without CV/metabolic risk factors at baseline With CV/metabolic risk and concomitant medications at baseline APA+ADT ADT+Placebo APA+ADT ADT+Placebo APA+ADT ADT+Placebo Subgroup prevalence n(%) 378 (72.0%) 364 (69.1%) 147 (28.0%) 163 (30.9%) 358 (68.2%) 347 (65.8%) rPFS [HR (95% CI) p-value] 0.49 (0.38, 0.63) <0.0001 0.48 (0.33, 0.7) 0.0001 0.47 (0.36, 0.62) <0.0001 OS [HR (95% CI) p-value] 0.63 (0.5, 0.8) 0.0001 0.71 (0.49, 1.02) 0.0604 0.61 (0.48, 0.78) <0.0001 PSA90 or PSA<0.2ng/ml 326 (86.2%) 157 (43.1%) 121 (82.3%) 71 (43.6%) 307 (85.8%) 146 (42.1%) TEAE (all grades) 368 (97.4%) 353 (97.0%) 142 (97.3%) 157 (96.3%) 350 (97.8%) 337 (97.1%) Gr 3/4 TEAE 183 (48.4%) 165 (45.3%) 76 (52.1%) 55 (33.7%) 177 (49.4%) 161 (46.4%)

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.059
GPT teacher head0.421
Teacher spread0.361 · 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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