Characterization of Redox-Responsive LXR-Activating Nanoparticle Formulations in Primary Mouse Macrophages
Bibliographic record
Abstract
Activation of the transcription factor liver X receptor (LXR) has beneficial effects on macrophage lipid metabolism and inflammation, making it a potential candidate for therapeutic targeting in cardiometabolic disease. While small molecule delivery via nanomedicine has promising applications for a number of chronic diseases, questions remain as to how nanoparticle formulation might be tailored to suit different tissue microenvironments and aid in drug delivery. In the current study, we aimed to compare the in vitro drug delivering capability of three nanoparticle (NP) formulations encapsulating the LXR activator, GW-3965. We observed little difference in the base characteristics of standard PLGA-PEG NP when compared to two redox-active polymeric NP formulations, which we called redox-responsive (RR)1 and RR2. Moreover, we also observed similar uptake of these NP into primary mouse macrophages. We used the transcript and protein expression of the cholesterol efflux protein and LXR target ATP-binding cassette A1 (ABCA1) as a readout of GW-3956-induced LXR activation. Following an initial acute uptake period that was meant to mimic circulating exposure in vivo, we determined that although the induction of transcript expression was similar between NPs, treatment with the redox-sensitive RR1 NPs resulted in a higher level of ABCA1 protein. Our results suggest that NP formulations responsive to cellular cues may be an effective tool for targeted and disease-specific drug release.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".