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Record W4379984225 · doi:10.1158/1538-7445.am2023-3104

Abstract 3104: Synthesis and optimization of small molecules designed to stimulate the immune system by inducing DNA damage and blocking PD-1/PD-L1

2023· article· en· W4379984225 on OpenAlexaff
Ana Belén Fraga Timiraos, Caterina Facchin, Nadia Babaa, Anne‐Laure Larroque‐Lombard, Bertrand J. Jean‐Claude

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsImmune systemImmunotherapyCancer researchCancerMelanomaCancer immunotherapyPD-L1Cancer cellImmune checkpointDNA damageChemistryBiologyDNAImmunologyMedicineBiochemistryInternal medicine

Abstract

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Abstract Introduction. Activating the immune system against cancer is becoming an increasingly effective therapy option that can result in dramatic and durable responses in several cancer types. One approach to achieve the reactivation of endogenous antitumor T cells is by blocking PD-1/PD-L1 immune checkpoints expressed on T cells and other leukocytes. However, only limited cancer patients (15-25%) respond to anti-PD-1/PD-L1 immunotherapy. One of the most pressing current clinical challenges is to convert nonresponsive, “cold” tumors to responsive, “hot” tumors. Interestingly, after DNA-damaging chemotherapy, the immune environment may be changed from “cold” tumors to “hot” tumors by increasing the tumor mutation burden and the generation of neoantigens on the surface of cancer cells. Therefore, we surmised that a molecule capable of inducing promutagenic DNA and block PD-1/PD-L1 could not only induce neoantigens but also synergistically enhance immune response against the targeted tumor. Using an approach developed in our laboratory termed the “combi-targeting” strategy, we designed and synthesized a series of “combi-molecules” programmed to generate the promutagenic species and a small molecule capable of blocking PD-1/PD-L1. Material and methods. Melanoma cell line B16-F10 was used to determine IC50 of the new molecules with SRB assay. Homogenous time-resolved fluorescence (HTRF) binding assay was used to determine the IC50 inhibition of PD-1/PD-L1. Drug metabolism in extracted cells was measured by LC-MS. Results. We discovered a structure activity relationship of the combi-molecules based on the substitution of the side chain of the alkylating agent and the PD-1/PD-L1 scaffold. By altering the scaffolds of the combi-molecules from sulfonamides to biphenyl derivatives, we optimized binding to PD-1/PD-L1 from millimolar to micromolar levels. In vitro growth inhibitory analysis showed that the combi-molecules with biphenyl scaffold were 24-fold more potent in B16-F10. Importantly, analysis of intracellular metabolites of the combi-molecules revealed three main metabolites that can only result from the release of the shortlived promutagenic species. Conclusions. The biphenyl scaffold is optimal for maintaining strong PD-1/PD-L1 binding potency and enough potent to contribute to cell death. Hydrolytic cleavage of the alkylating agent is an indirect evidence of the formation of the alkylating species required to induce promutagenic lesions. Citation Format: Ana Belen Fraga Timiraos, Caterina Facchin, Nadia Babaa, Anne-Laure Larroque-Lombard, Bertrand Jean-Claude. Synthesis and optimization of small molecules designed to stimulate the immune system by inducing DNA damage and blocking PD-1/PD-L1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3104.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.298
Teacher spread0.257 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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