MétaCan
Menu
← Back to cohort
Record W2911915950 · doi:10.1101/545525

Design and evaluation of novel 4-anilinoquinolines and quinazolines EGFR inhibitors in lung cancer and chordoma

2019· preprint· en· W2911915950 on OpenAlexfundno aff
Christopher R. M. Asquith, Kaitlyn Maffuid, Tuomo Laitinen, Chad Torrice, Graham J. Tizzard, Carla Alamillo‐Ferrer, Karl M. Koshlap, Daniel J. Crona, William J. Zuercher

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
FundersNovartis PharmaUniversity of North Carolina at Chapel HillMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario Ministry of Economic Development and InnovationChina Scholarship CouncilGenome CanadaPfizer
KeywordsChordomaEpidermal growth factor receptorEGFR inhibitorsLung cancerCancer researchChemistryMedicineComputational biologyOncologyReceptorInternal medicineBiologyPathology

Abstract

fetched live from OpenAlex

ABSTRACT Epidermal growth factor receptor (EGFR) inhibitors have been used to target non-small cell lung cancer (NSCLC) and chordomas with varying amounts of success. We have probed several key structural features including an interaction with Asp855 within the EGFR DGF motif and interactions with the active site water network. The EGFR target engagement was then evaluated in an in-cell assay. Additionally, inhibitors were profiled in representative cellular models of NSCLC and chordomas. In addition to a structure activity relationship insights for EGFR inhibtior design, we also identified a compound ( 18 ) that is the most potent inhibitor (IC 50 = 310 nM) on the UCH-2 chordoma cell line to date.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 science0.0010.000
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 head0.030
GPT teacher head0.320
Teacher spread0.289 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicLung Cancer Treatments and Mutations→French-language works237,207→