Drug Interactions between Androgen Receptor Axis-Targeted Therapies and Antithrombotic Therapies in Prostate Cancer: Delphi Consensus
Bibliographic record
Abstract
Background/Objectives: Abiraterone acetate, apalutamide, darolutamide, and enzalutamide, which make up the androgen receptor axis-targeted therapies (ARATs) drug class, are commonly used in the management of prostate cancer. Many patients on ARATs also receive oral antithrombotic therapy (i.e., anticoagulants or antiplatelets). The concomitant use of ARATs and antithrombotic therapies creates the potential for clinically relevant drug–drug interactions, but the literature regarding the actual consequences of these interactions, and guidance for co-prescribing, is limited. We assembled a multidisciplinary panel of experts and provided them with clinical information derived from a comprehensive literature review regarding the drug–drug interactions between ARATs and antithrombotic therapies. Methods: A three-stage modified electronic Delphi process was used to gather and consolidate opinions from the panel. Each stage consisted of up to three rounds of voting to achieve consensus on which ARAT/antithrombotic therapy drug pairs warrant attention, the possible clinical consequences of drug–drug interactions, and suggested actions for management. Results: The panel achieved consensus to avoid 11 ARAT/antithrombotic therapy drug pairs and modify therapy for eight pairs. Assessments relied heavily on pharmacokinetic data and extrapolation from drug–drug interaction studies of similarly metabolized drugs. Conclusions: This e-Delphi process highlights the need for further research into the clinical impact of ARAT/antithrombotic drug interactions. Nonetheless, the suggested actions aim to provide clinicians with a practical framework for therapeutic decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.289 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".