Solidago canadensis L. Herb Extract, Its Amino Acids Preparations and 3D-Printed Dosage Forms: Phytochemical, Technological, Molecular Docking and Pharmacological Research
Bibliographic record
Abstract
Background/Objectives: The Canadian goldenrod (Solidago canadensis L.) is one of the most widespread species of the genus Solidago from the Asteraceae family. It has a rich composition of biologically active compounds and is traditionally used to address kidney, urinary tract, and liver diseases. Previously, it was proven that the S. canadensis extract obtained with a 40% ethanol solution had the most promising anti-inflammatory and hepatoprotective activity. Therefore, this extract was selected for the further formulation of amino acid preparations and 3D-printed dosage forms. The aims of the present study were to investigate the chemical composition, toxicity, and antimicrobial, anti-inflammatory, and hepatoprotective activity of S. canadensis dry extract, its amino acid preparations, and 3D-printed dosage forms. Results: A total of 18 phenolic compounds and 14 amino acids were determined in the extracts. The S. canadensis herb extracts were verified to be practically non-toxic preparations (toxicity class V, LD₅₀ > 5000 mg/kg). They also showed moderate antimicrobial activity against Staphylococcus aureus, Enterococcus faecalis, and β-hemolytic Streptococcus pyogenes. The most pronounced hepatoprotective activity was observed with S. canadensis herb extract and its amino acid preparations with phenylalanine, alanine, and lysine at a dose of 25 mg/kg body weight. The most pronounced anti-inflammatory activity was found with S. canadensis herb extract and its preparation with arginine. According to the calculated docking score array and the analysis of binding modes in the active sites of COX-1 and COX-2, the flavonoid fraction and caffeic acid in the S. canadensis extracts presented moderate inhibitory activity. Conclusions: The development of innovative 3D-printed oral dosage forms represents a promising strategy to formulate dietary supplements or pharmaceutical preparations for these herbal extracts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".