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Drug-drug interactions in patients with castration-resistant prostate cancer undergoing abiraterone therapy: Characterizing the scale of the problem.

2015· article· en· W2418296065 on OpenAlexaff
Rehana Jamani, Esther K. Lee, Scott R. Berry, Carlo DeAngelis, Angie Giotis, Urban Emmenegger

Bibliographic record

VenueJournal of Clinical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstate cancerPharmacologyAbiraterone acetateInternal medicineCYP2D6Adverse effectAndrogen deprivation therapyCYP3A4DrugCancerCytochrome P450

Abstract

fetched live from OpenAlex

269 Background: Abiraterone acetate (AA), used to treat CRPC, inhibits androgen biosynthesis by blocking cytochrome P450 (CYP) 17. It also inhibits other cytochromes involved in the metabolism of various widely-used medications (strong inhibition of CYP1A2, CYP2D6, and CYP2C8; moderate inhibition of CYP2C9, CYP2C19 and CYP3A4/5). Hence, there is presumably a high potential for drug-drug interactions (DDI) that can either diminish the efficacy of AA or concurrent medications, or increase the risk of DDI-related adverse events (AE); however, the scale of AA-associated DDI is currently unknown. Methods: We conducted a retrospective review of pharmacy records and electronic pt charts to retrieve individual drug histories, comorbidities, and AE of CRPC pts beginning AA treatment between Jan 2010 and Apr 2014. Individual drug histories were analyzed for DDI using two commercial databases, Lexicomp and Micromedex. Results: 91 informative pts were identified. Using Lexicomp, the most common drugs flagged for potential DDI of high clinical significance (i.e., “avoid combination”, or “consider therapy modification”) with AA were dexamethasone, metoprolol, clopidogrel, oxycodone, and citalopram. They were administered to 12 (13%), 10 (11%), 6 (7%), 4 (4%), and 4 (4%) pts respectively. Micromedex assigned a major risk of DDI to oxycodone and a moderate risk to metoprolol. At least 1 potentially significant DDI was found in 38/91 pts (42%) with Lexicomp, and in 42/91 pts (46%) with Micromedex. Most common AE were fluid retention seen in 19 pts (21%), fatigue in 15 (16%), liver-function test abnormalities in 13 (14%), hypertension in 13 (14%), and pain in 11 (12%), all corresponding to AE typically associated with the use of AA. We did not find unequivocal evidence for DDI-related AE. Conclusions: The use of commercial DDI databases reveals a substantial risk of potentially consequential DDI in CRPC pts undergoing AA therapy, although some of the flagged DDI (e.g., between AA and dexamethasone) may be of theoretical rather than practical concern. Further investigation with larger patient populations is required to better establish the clinical relevance of these DDI.

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)0.005
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.120
GPT teacher head0.447
Teacher spread0.327 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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