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Record W3129279218 · doi:10.1038/s41467-021-21410-1

Checkpoint inhibition through small molecule-induced internalization of programmed death-ligand 1

2021· article· en· W3129279218 on OpenAlexaff
Jang‐June Park, Emily P. Thi, Victor H. Carpio, Yingzhi Bi, Andrew G. Cole, Bruce D. Dorsey, Kristi Fan, Troy O. Harasym, Christina L. Iott, Salam Kadhim, Jin Hyang Kim, Amy C. H. Lee, Duyan Nguyen, Bhavna Paratala, Ruiqing Qiu, André White, L. Damodharan, Christopher Leo, R.K. Suto, René Rijnbrand, Sunny X. Tang, Michael J. Sofia, Chris B. Moore

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsArbutus Biopharma (Canada)
FundersUniversity of Pennsylvania
KeywordsInternalizationProgrammed cell deathLigand (biochemistry)Small moleculeCell biologyCancer researchAntigenChemistryBiologyApoptosisImmunologyCellReceptorBiochemistry

Abstract

fetched live from OpenAlex

Programmed death-ligand 1 is a glycoprotein expressed on antigen presenting cells, hepatocytes, and tumors which upon interaction with programmed death-1, results in inhibition of antigen-specific T cell responses. Here, we report a mechanism of inhibiting programmed death-ligand 1 through small molecule-induced dimerization and internalization. This represents a mechanism of checkpoint inhibition, which differentiates from anti-programmed death-ligand 1 antibodies which function through molecular disruption of the programmed death 1 interaction. Testing of programmed death ligand 1 small molecule inhibition in a humanized mouse model of colorectal cancer results in a significant reduction in tumor size and promotes T cell proliferation. In addition, antigen-specific T and B cell responses from patients with chronic hepatitis B infection are significantly elevated upon programmed death ligand 1 small molecule inhibitor treatment. Taken together, these data identify a mechanism of small molecule-induced programmed death ligand 1 internalization with potential therapeutic implications in oncology and chronic viral infections.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.053
GPT teacher head0.345
Teacher spread0.292 · 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 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

Citations133
Published2021
Admission routes1
Has abstractyes

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