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1185 Optimization of purine-based TLR7 agonists as payloads for immune-stimulating antibody conjugates (ISACs)

2022· article· en· W4308383460 on OpenAlexaff
Graham A. E. Garnett, Katina Mak, Renee Duan, Truman Hirkala-Schaefer, Manuel Lasalle, Nichole Escalante, Joy Guedia, Kara White Moyes, Sam Lawn, Raffaele Colombo, Jamie Rich, Stuart D. Barnscher

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsNuvation (Canada)Zymeworks (Canada)
Fundersnot available
KeywordsImmune systemTLR7PurinePharmacologyAntibodyPeripheral blood mononuclear cellSplenocyteIn vitroChemistryCytokineCancer researchImmunologyBiologyBiochemistryToll-like receptorInnate immune system

Abstract

fetched live from OpenAlex

<h3>Background</h3> Immune-stimulating antibody conjugates (ISACs) consist of antibodies conjugated to immune stimulants and are designed to induce antitumor immune response. Despite promising preclinical results, ISAC clinical development has been hampered by systemic toxicities or lack of efficacy. Substituted purines have previously been identified as a privileged scaffold to elicit TLR7 activation. Here, we demonstrate newly designed purine-based TLR7 agonists conjugated to trastuzumab which show significant tumor volume reduction in a HER2-high gastric cancer xenograft model without associated body weight loss (BWL) in healthy mice. <h3>Methods</h3> A library of TLR7 agonists was generated by varying substituents at C2- and N9-positions of a common 6-amino-8-hydroxy-purine scaffold, and the structure-activity relationship was studied <i>in vitro</i> using human and mouse TLR7 reporter gene assays (RGAs) as well as measuring cytokine secretion from human peripheral blood mononuclear cells (PBMCs) and mouse splenocytes. Lead TLR7 agonists were conjugated to trastuzumab, and the resulting ISACs were evaluated <i>in vitro</i> for their abilities to induce the production of interleukin-6 (IL-6) from human PBMCs or mouse splenocytes co-cultured with NCI-N87 tumor cells. Selected ISACs were tested for efficacy (single iv injection at 2.5 mg/kg) in mice bearing NCI-N87 tumors (figure 1) and for tolerability (single iv injection at 3, 15, and 45 mg/kg) in healthy mice (figure 2). <h3>Results</h3> We prepared ~220 new TLR7 agonists with different substituents at C2- and N9-positions of the purine scaffold. Compounds with IC50 &lt;100 nM in both human and mouse TLR7 RGAs were further screened for their abilities to induce production of cytokines in PBMCs and mouse splenocytes. Certain substituents were found to be highly immunostimulatory in both human and murine settings. Lead TLR7 agonists were conjugated to trastuzumab with a drug-to-antibody ratio of ~4 using cleavable or non-cleavable linkers. ISACs capable of inducing IL-6 production from PBMCs or splenocytes co-cultured with tumor cells were further tested in an NCI-N87 xenograft model in comparison to unconjugated trastuzumab and trastuzumab conjugated with the same linker-payload as NJH395, a clinical benchmark ISAC. Selected ISACs were tested for tolerability in healthy mice with a lead ISAC (Trastuzumab-MTvkPABC-P5) identified, capable of inducing tumor regression without causing BWL. <h3>Conclusions</h3> We demonstrated the potential of using novel purine-based TLR7 agonists as payloads for ISACs. In contrast to other TLR7-agonist conjugates, our lead ISAC appears to have a sufficiently wide therapeutic window displaying efficacy in an NCI-N87 xenograft model at 2.5 mg/kg without causing BWL in healthy mice at 45 mg/kg. <h3>Ethics Approval</h3> All animal studies were performed in accordance with Institutional Animal Care and Use Committee (IACUC)-approved protocols.

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 score0.027
Threshold uncertainty score0.782

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 head0.021
GPT teacher head0.313
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

Citations2
Published2022
Admission routes1
Has abstractyes

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