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Record W4308678741 · doi:10.1080/14789450.2022.2142564

The role of E3 ubiquitin ligase in multiple myeloma: potential for cereblon E3 ligase modulators in the treatment of relapsed/refractory disease

2022· review· en· W4308678741 on OpenAlexaff
Paul G. Richardson, María‐Victoria Mateos, Annette Juul Vangsted, Karthik Ramasamy, Niels Abildgaard, P. Joy Ho, Hang Quach, Nizar J. Bahlis

Bibliographic record

VenueExpert Review of Proteomics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCereblonUbiquitin ligaseDNA ligaseUbiquitin-Protein LigasesUbiquitinCancer researchMultiple myelomaChemistryBiologyCell biologyGeneticsDNAImmunologyGene

Abstract

fetched live from OpenAlex

INTRODUCTION: Insights into the mechanisms of protein homeostasis and proteasomal degradation have led to new strategies of redirecting the ubiquitin-proteasome system (UPS) to reduce or eliminate proteins or survival factors key to malignant pathobiology, multiple myeloma (MM) in particular. These strategies have enabled researchers to target proteins that were previously considered difficult to modulate by pharmacological means. AREAS COVERED: E3 ubiquitin ligase complex, and summarizes current strategies for co-opting the UPS, including CELMoD compounds, SNIPERs, PROTACs, and degronimids. A detailed discussion is provided on lead CELMoD compounds iberdomide and mezigdomide, which are currently being evaluated in clinical trials in patients with MM. EXPERT OPINION: Since a high proportion of patients develop drug resistance, it is vital to have novel therapeutic agents for treating relapsed patients with MM more effectively. It is encouraging that the expanding pathophysiological insight into cellular signaling pathways in MM increasingly translates into the development of novel therapeutic agents such as targeted protein degraders. This holds promise for improving outcomes in MM and beyond.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.020
GPT teacher head0.307
Teacher spread0.288 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations10
Published2022
Admission routes1
Has abstractyes

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