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Pharmacokinetic and pharmacodynamic analysis of adavosertib in advanced ovarian cancer.

2022· article· en· W4281856738 on OpenAlexaff
Amit M. Oza, Stéphanie Lheureux, Ainhoa Madariaga, Mihaela Cristea, Gina Mantia-Smaldone, Alexander Olawaiye, Susan Ellard, Johanne I. Weberpals, Andrea E. Wahner Hendrickson, Gini F. Fleming, Stephen Welch, Neesha C. Dhani, Vanessa Speers, Valerie Bowering, Lisa Wang, Wenjiang Zhang, Eric Xueyu Chen

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsCancer Care OntarioPrincess Margaret Cancer CentreOttawa HospitalKelowna General HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineGemcitabineCmaxPharmacokineticsPharmacodynamicsInternal medicineOvarian cancerPlaceboGastroenterologyCancerUrologyOncologyPharmacologyPathology

Abstract

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5579 Background: Adavosertib (AZD-1775) is a potent small molecule inhibitor of Wee-1, currently in clinical development. In a double-blind, placebo-controlled, phase 2 trial (NCT02151292), adavosertib and gemcitabine significantly prolonged progression-free survival (PFS) and overall survival (OS) in patients with recurrent platinum-resistant or platinum-refractory high grade serous ovarian cancer (HGSOV) compared to gemcitabine alone. We investigated whether plasma and intra-tumoral adavosertib concentrations correlated with survival in these patients. Methods: Adavosertib was administered orally on Days 1, 2, 8, 9, 15 and 16 at 175 mg per day, and gemcitabine was administered on Days 1, 8 and 15 at 1000 mg/m2 every 28 days. Serial blood samples were collected on Day 1 of cycle 1 after adavosertib administration. Tumor biopsies were taken 1-2 weeks after initiation of study treatments. Plasma and tumor adavosertib concentrations were determined using validated HPLC-MS/MS. Patients were divided into groups with plasma or tumor adavosertib concentrations above or below the biologically active concentration (BAC) of 125 ng/ml (Leijen et al, J Clin Onco 2016). Survival was described using the Kaplan-Meier method. Results: Among 61 HGSOV patients who received adavosertib, plasma samples were available in 47, and tumor samples were available in 31 patients. Among 25 non-HGSOV patients (exploratory cohort), plasma and tumor samples were available in 21 and 17 patients respectively. The mean maximum adavosertib concentration (Cmax) was 355.3 ± 120.9 ng/ml and 358.6 ± 117.9 ng/ml respectively. Cmax was above BAC in all patients. The mean tumor adavosertib concentration was 609.2 ± 1129.2 ng/ml (range: 0.47 – 5501 ng/ml) for HGSOV patients, and 964.2 ± 1611.2 ng/ml (range: 0.22 – 6116 ng/ml) for non-HGSOV patients. There was no correlation between Cmax and tumor adavosertib concentrations. In HGSOV, the median PFS was 5.8 months for patients with tumor concentrations above BAC, and 3.5 months for those with tumor concentrations below BAC (Hazard ratio (HR): 0.46, 95% confidence interval: 0.19 – 1.14, p = 0.06). No difference in PFS was seen in non-HGSOV patients according to tumor adavosertib concentration. Tumor adavosertib concentration did not correlate with OS. Conclusions: Although Cmax was above BAC in all patients, there was a high variability in tumor adavosertib concentrations. In HGSOV, higher tumor adavosertib concentration was associated with a trend towards improved PFS, but not OS. Our results indicate that the current adavosertib dosing regimen may not produce the desired concentrations in tumors for some patients, and further optimization may be needed.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.087
GPT teacher head0.507
Teacher spread0.419 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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