Development of itaconate polymer microparticles for intracellular regulation of pro-inflammatory macrophage activation
Bibliographic record
Abstract
Itaconate (IA) is an endogenous metabolite and a potent regulator of the innate immune system. Its use in immunomodulatory therapies has faced limitations due to inherent challenges in achieving controlled delivery and requirements for high extracellular concentrations to achieve internalization of the highly polar small molecule to achieve its intracellular therapeutic activity. Microparticle (MP)-based delivery strategies are a promising approach for intracellular delivery of small molecule metabolites through macrophage phagocytosis and subsequent intracellular polymer degradation-based delivery. Toward the goal of intracellular delivery of IA, degradable polyester polymer-(poly(itaconate-co-dodecanediol)) based IA polymer microparticles (IA-MPs) were generated using an emulsion method, forming micron-scale (∼ 1.5 µm) degradable microspheres. IA-MPs were characterized with respect to their material properties and IA release kinetics to inform particle fabrication. Treatment of murine bone marrow-derived macrophages with an optimized particle concentration of 0.1 mg/million cells enabled phagocytosis-mediated internalization and low levels of cytotoxicity. Flow cytometry demonstrated IA-MP-specific regulation of IA-sensitive inflammatory targets. Metabolic analyses demonstrated that IA-MP internalization inhibited oxidative metabolism and induced glycolytic reliance, consistent with the established mechanism of IA-associated inhibition of succinate dehydrogenase. This development of IA-based polymer microparticles provides a basis for additional innovative metabolite-based microparticle drug delivery systems for the treatment of inflammatory disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".