Abstract 3849: Identification and optimization of chemical compounds as potent agonists of human STING with anticancer activity in mice
Bibliographic record
Abstract
Abstract The host STING pathway plays a critical role in innate immune sensing of cancer, that drives type-I interferons (IFNs) production and promotes aggressive antitumor responses. Compound DMXAA is an agonist of mouse STING (mSTING) and demonstrated potent antitumor activities in several tumor models, including melanoma. However, DMXAA cannot activate human STING (hSTING), which provides a possible rationale for its failure in recent clinical trial. In this project, we aim to identify novel chemical compounds as potent STING agonists. By combining structure-based drug design and in vitro assays, we have discovered two initial hits as STING agonists that belong to two different chemical scaffolds. Direct binding of our compounds with hSTING was confirmed by Surface plasmon resonance (SPR) analysis. Chemical optimization of our initial hits has led to compound #150 and #171. We demonstrated that #150 and #171 at 10 uM have substantially activated STING pathway in HEK293 cells that were transiently transfected with plasmid expressing hSTING. Compound #171 at 20 uM potently activated STING signaling in human THP-1 cells that harbor hSTINGHAQ as well as PBMC cells from a panel of human donors. Importantly, with DMXAA as the control, we demonstrated that #171 at 20 mg/kg via i.p. has significant antitumor effect against TRAMP-c1 tumor in C57/BL6 mice. Citation Format: Jian Hui Wu. Identification and optimization of chemical compounds as potent agonists of human STING with anticancer activity in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3849.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".