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Record W3158404933 · doi:10.1021/acs.jmedchem.0c02200

Pharmacological Targeting of Executioner Proteins: Controlling Life and Death

2021· review· en· W3158404933 on OpenAlexafffund
Justin Pogmore, David Uehling, David W. Andrews

Bibliographic record

VenueJournal of Medicinal Chemistry · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsProgrammed cell deathSmall moleculeApoptosisChemistryBcl-2 familyCell biologyCancer researchComputational biologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Small-molecule mediated modulation of protein interactions of Bcl-2 (B-cell lymphoma-2) family proteins was clinically validated in 2015 when Venetoclax, a selective inhibitor of the antiapoptotic protein BCL-2, achieved breakthrough status designation by the FDA for treatment of lymphoid malignancies. Since then, substantial progress has been made in identifying inhibitors of other interactions of antiapoptosis proteins. However, targeting their pro-apoptotic counterparts, the "executioners" BAX, BAK, and BOK that both initiate and commit the cell to dying, has lagged behind. However, recent publications demonstrate that these proteins can be positively or negatively regulated using small molecule tool compounds. The results obtained with these molecules suggest that pharmaceutical regulation of apoptosis will have broad implications that extend beyond activating cell death in cancer. We review recent advances in identifying compounds and their utility in the exogenous control of life and death by regulating executioner proteins, with emphasis on the prototype BAX.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.029
GPT teacher head0.322
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2021
Admission routes2
Has abstractyes

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