Identification and evaluation of a lipid-lowering small compound as a PCSK9 inhibitor
Bibliographic record
Abstract
INTRODUCTION: Hyperlipidemia is a key contributor to cardiovascular diseases, underscoring the necessity for alternative lipid-lowering treatments beyond statins. OBJECTIVES: This study aimed to synthesize and identify small, low-toxicity lipid-lowering compounds and investigate their mechanisms of action. METHODS: A series of tetrahydroisoquinoline compounds were synthesized, with HepG2 cells used to screen and identify B11 as a potent, low-toxicity lipid-lowering candidate. B11's efficacy was tested in various hyperlipidemic animal models, including C57BL/6 mice, hamsters, and humanized PCSK9 transgenic (B6-hPCSK9) mice. Target interactions were investigated using various in vitro techniques, including molecular docking, cellular thermal shift assays (CETSA), drug affinity responsive target stability (DARTS), and surface plasmon resonance (SPR). Furthermore, we evaluated the synergistic effects of B11 combined with statins in C57BL/6 mice. RESULTS: A series of tetrahydroisoquinoline compounds was synthesized, identifying B11 as a potent lipid-lowering candidate with minimal toxicity. B11 significantly reduced total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) in the plasma and liver of high fat diet (HFD) induced mice, hamsters, and B6-hPCSK9 mice, without causing any adverse effects. Mechanistically, B11 targets the 455-692 amino acid region of pro-protein convertase subtilisin/kexin type 9 (PCSK9), blocking its interaction with the low-density lipoprotein receptor (LDL-R) and inducing PCSK9 degradation via the ubiquitin-proteasome pathway. This process leads to increased LDL-R levels, enhancing LDL-C clearance. Notably, The unique mechanism of B11 enables combination therapy with atorvastatin, leading to stronger lipid-lowering effects and lower liver toxicity. CONCLUSIONS: These findings demonstrate a small, non-statin compounds, and provide the potential alternative treatment approach for hyperlipidemic patients.
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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 | 0.009 | 0.001 |
| 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".