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Tumor drug distribution and target engagement of MLN9708, an investigational proteasome inhibitor, in patients with advanced solid tumors.

2012· article· en· W3012427039 on OpenAlexaff
Alessandra Di Bacco, Allison Berger, Neeraj Gupta, Feng Gao, Stephen J. Blakemore, Mark G. Qian, Susan Chen, Bradley Stringer, Yang Yu, Ray Liu, Stephen Tirrell, Doug Bowman, David C. Smith, Daniel M. Sullivan, Jeffrey R. Infante, John Kauh, Lillian L. Siu, Gordana Vlahovic, John A. Thompson, Thea Kalebic

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBiopsyProteasome inhibitorProstate cancerCancerImmunohistochemistryCohortUrologyInternal medicinePathology

Abstract

fetched live from OpenAlex

3077 Background: MLN9708 is a potent investigational proteasome inhibitor, which upon intravenous (IV) administration immediately hydrolyzes to the active form MLN2238. MLN9708 is currently being evaluated in a phase 1 trial in solid tumors (NCT00830869). This trial has a dose-escalation arm and five expansion cohorts: non-small cell lung cancer (NSCLC), soft tissue sarcoma, head and neck cancer, prostate cancer, and a tumor biopsy cohort of mixed histology. The purpose of the tumor biopsy cohort was to obtain pre- and post-dose biopsies to determine drug distribution and target engagement in post-dose tumor samples. The latter was measured by the increase in levels of ATF-3, a marker of unfolded protein response/endoplasmic reticulum stress, which is upregulated in response to proteasome inhibition. Methods: The tumor biopsy cohort included 20 patients dosed at the maximum tolerated dose who consented to core needle biopsies during screening and after either the first or second dose of MLN9708 (IV 1.76 mg/m2; 4–20 hours post-dose). Tumor biopsies were individually weighed, homogenized, and analyzed for the presence of MLN2238 using a quantified LC/MS/MS methodology. ATF-3 levels in tumors were determined by an immunohistochemical assay (IHC) on six sections for each tumor biopsy. Tumor area was identified using Aperio Genie, a machine learning program for pattern recognition, and the percentage of ATF-3 positive area in the tumor was measured. Results: Biopsies from 20 patients were collected for assessment of drug distribution and target engagement. Ten patients with paired pre- and post-dose biopsies of sufficient size were considered evaluable for PK analysis; MLN2238 was present in all 10 (100%) post-dose biopsies analyzed. Tumor pairs from 7 patients passed quality control by H&E staining for tumor content and were evaluable for ATF-3 IHC. Six of 7 paired samples (86%) showed a statistically significant (p<0.05) increase in post-dose ATF-3 levels. Conclusions: Overall, emerging data from MLN9708 phase 1 solid tumor analysis show that MLN2238 is present in tumors and demonstrates target engagement upon inhibition of the proteasome in tumor tissue biopsies.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.354
Teacher spread0.319 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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