MétaCan
Menu
Back to cohort
Record W4298362686 · doi:10.17615/8tsa-8w11

Discovery of Small Molecule Mer Kinase Inhibitors for the Treatment of Pediatric Acute Lymphoblastic Leukemia

2020· article· en· W4298362686 on OpenAlexfundno aff
Chao Yang, William P. Janzen, Deborah DeRyckere, Jacqueline Norris‐Drouin, Douglas K. Graham, Amy Van Deusen, Hari S. Patel, Michael J. Miley, Mischa Machius, Debra M. Hunter, Jing Liu, Dmitri Kireev, H. Shelton Earp, Xiaodong Wang, Catherine Simpson, Victoria Korboukh, Stephen V. Frye, Gary L. Johnson, Susan Sather

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsnot available
FundersBasic Energy SciencesU.S. Department of Health and Human ServicesNational Cancer InstituteOffice of ScienceUniversity of TorontoU.S. Department of Energy
KeywordsLymphoblastic LeukemiaSmall moleculeCancer researchLeukemiaMedicineComputational biologyBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Ectopic Mer expression promotes pro-survival signaling and contributes to leukemogenesis and chemoresistance in childhood acute lymphoblastic leukemia (ALL). Consequently, Mer kinase inhibitors may promote leukemic cell death and further act as chemosensitizers increasing efficacy and reducing toxicities of current ALL regimens. We have applied a structure-based design approach to discover novel small molecule Mer kinase inhibitors. Several pyrazolopyrimidine derivatives effectively inhibit Mer kinase activity at sub-nanomolar concentrations. Furthermore, the lead compound shows a promising selectivity profile against a panel of 72 kinases and has excellent pharmacokinetic properties. We also describe the crystal structure of the complex between the lead compound and Mer, opening new opportunities for further optimization and new template design.

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 science0.0000.000
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 head0.032
GPT teacher head0.240
Teacher spread0.208 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicPhagocytosis and Immune RegulationFrench-language works237,207