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Olaparib plus abiraterone as first-line therapy in men with metastatic castration-resistant prostate cancer: Pharmacokinetics data from the PROpel trial.

2022· article· en· W4286295274 on OpenAlexaff
Andrew J. Armstrong, Noel W. Clarke, Antoine Thiery-Vuillemin, Mototsugu Oya, Giuseppe Procopio, J. Menezes, Gustavo Girotto, Pooja Ghatalia, Franco Nolè, Omar Din, Philipp Spiegelhalder, I. Minčík, Robbert J. van Alphen, Nicolaas Lumen, Christian Hosius, Diansong Zhou, Laura Barker, Melanie Dujka, Fred Saad

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsOlaparibMedicineProstate cancerAbirateroneAbiraterone acetatePlaceboInternal medicinePharmacokineticsUrologyOncologyPharmacologyCancerAndrogen deprivation therapyAndrogen receptorChemistryPathology

Abstract

fetched live from OpenAlex

5050 Background: PROpel (NCT03732820) is a double-blind, Phase III trial of abiraterone + olaparib vs abiraterone + placebo as first-line treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). Here we report results from the pharmacokinetics (PK) analysis of patients in PROpel. Methods: Patients were randomized 1:1 to receive abiraterone (1000 mg qd) plus prednisone/prednisolone with either olaparib (full monotherapy dose: 300 mg bid) or placebo. Eligible patients were biomarker unselected with confirmed prostate adenocarcinoma and castration-resistant metastatic disease. They had not received prior chemotherapy or next-generation hormonal agents (NHAs) for mCRPC. PK sampling was performed in a subset of patients. Concentrations of olaparib and abiraterone, and its active metabolite Δ4-abiraterone, were measured at steady state predose, at 30 min, 2 h, 3 h, 5 h, and 8 h postdose. The data underwent noncompartmental analysis to evaluate the effect of olaparib on abiraterone PK. The PK of olaparib in the presence of abiraterone was also compared with olaparib PK from other monotherapy studies to evaluate the effect of abiraterone on olaparib PK. Results: The PK analysis included 66 patients from the olaparib + abiraterone arm and 58 patients from the placebo + abiraterone arm. Olaparib absorption was rapid, with median tmax,ss of 2 h. Absorption of abiraterone was rapid in both treatment groups, with median tmax,ss observed between 2.00 and 2.04 h. The steady state exposure of olaparib in the presence of abiraterone, based on AUCss, Cmax,ss and Cmin,ss, was similar to observations for patients receiving olaparib 300 mg bid monotherapy in other Phase III studies, with values of 39.3 μg⋅h/mL, 6.3 μg/mL, and 1.0 μg/mL, respectively. Steady state exposures for abiraterone were similar between the two treatment arms (abiraterone + placebo: AUC(0–8) = 339.5 ng⋅h/mL, Cmax,ss = 105.4 ng/mL, Cmin,ss = 8.5 ng/mL; abiraterone + olaparib: AUC(0–8) = 393.7 ng⋅h/mL, Cmax,ss = 112.6 ng/mL, Cmin,ss = 7.7 ng/mL), and PK data for the abiraterone + olaparib arm were similar to those reported in the literature for abiraterone monotherapy. Conclusions: Combination treatment of olaparib ( full monotherapy dose: 300 mg bid) and abiraterone (1000 mg qd) in patients with mCRPC had no clinically significant effect on the PK profiles of either drug. The steady state exposures for abiraterone were similar between the two treatment arms, indicating that co-administration with olaparib 300 mg bid has no effect on the PK of abiraterone. In line with previous Phase II trial data, results from PROpel confirmed that there were no relevant PK based drug–drug-interactions between olaparib and abiraterone. Clinical trial information: NCT03732820.

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)0.002
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.321
GPT teacher head0.515
Teacher spread0.194 · 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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Citations2
Published2022
Admission routes1
Has abstractyes

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