Review of drug-drug interactions in patients with prostate cancer
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to provide an overview of common drug-drug interactions (DDIs) associated with prostate cancer treatments and outline recommendations for managing polypharmacy. DATA SOURCES: A literature search of PubMed, Embase, and CINAHL was carried out to identify pharmacokinetic and pharmacodynamic changes caused by DDIs that are relevant for prostate cancer patients, DDIs between prostate cancer therapies and co-administered medications (both prescription and over-the-counter), and measures to prevent DDIs. Medication package inserts were used to identify the impact of DDI on the prostate cancer therapy and suggested interventions. DATA SUMMARY: No DDIs are expected for the LHRH agonists leuprolide acetate, histrelin, goserelin, or leuprolide mesylate. However, DDIs have been reported for GnRH antagonists, anti-androgens, PARP inhibitors, and taxanes. Although there are no confirmed DDIs for sipuleucel-T to date, it is not generally recommended to use sipuleucel-T concurrently with immunosuppressive medications. Interventions to prevent DDIs include the use of software that can detect clinically significant DDIs, up-to-date medication reconciliation, the inclusion of dedicated clinical pharmacists in cancer treatment teams, and patient/caregiver education. CONCLUSIONS: Prostate cancer patients have a high risk of potential DDIs due to numerous new anti-cancer therapies, the increased use of treatment combinations, and the likelihood of comorbid conditions also requiring drug therapy. Drug-drug interaction screening software, up-to-date medication reconciliation, inclusion of oncology pharmacists on healthcare teams, and patient/caregiver education will aid the development of treatment plans that focus on achieving an optimal risk-benefit profile whilst reducing the risk of DDIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".