Nano‐Enabled Reposition of Proton Pump Inhibitors for TLR Inhibition: Toward A New Targeted Nanotherapy for Acute Lung Injury
Bibliographic record
Abstract
Toll-like receptor (TLR) activation in macrophages plays a critical role in the pathogenesis of acute lung injury (ALI). While TLR inhibition is a promising strategy to control the overwhelming inflammation in ALI, there still lacks effective TLR inhibitors for clinical uses to date. A unique class of peptide-coated gold nanoparticles (GNPs) is previously discovered, which effectively inhibited TLR signaling and protected mice from lipopolysaccharide (LPS)-induced ALI. To fast translate such a discovery into potential clinical applicable nanotherapeutics, herein an elegant strategy of "nano-enabled drug repurposing" with "nano-targeting" is introduced to empower the existing drugs for new uses. Combining transcriptome sequencing with Connectivity Map analysis, it is identified that the proton pump inhibitors (PPIs) share similar mechanisms of action to the discovered GNP-based TLR inhibitor. It is confirmed that PPIs (including omeprazole) do inhibit endosomal TLR signaling and inflammatory responses in macrophages and human peripheral blood mononuclear cells, and exhibits anti-inflammatory activity in an LPS-induced ALI mouse model. The omeprazole is then formulated into a nanoform with liposomes to enhance its macrophage targeting ability and the therapeutic efficacy in vivo. This research provides a new translational strategy of nano-enabled drug repurposing to translate bioactive nanoparticles into clinically used drugs and targeted nano-therapeutics for ALI.
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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 | 0.001 |
| 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".