Optimized Ginkgo biloba extract EGb 761®: boosted therapeutic benefits with minimized CYP enzyme interference
Bibliographic record
Abstract
Object The development of cognitive-enhancing drugs from Ginkgo biloba extract is actively pursued worldwide. This study compares the chemical compositions of different G. biloba extracts and their formulated drugs, highlighting the distinguishing characteristics and potential benefits of optimized G. biloba extract, EGb 761 ® . Methods We analyzed three G. biloba extracts and fifteen formulated drugs using HPLC, principal component analysis, and LC-MS/MS to identify key compositional differences. Molecular docking analysis was conducted to evaluate the binding affinity of the key component with a target protein involved in cognitive enhancement. CYP inhibition assays were performed on selected extracts and their derived products to examine drug-drug interactions. Results EGb 761 ® and its formulated drugs displayed a unique composition, characterized by a significantly higher level of protocatechuic acid (PCA). PCA demonstrated strong interactions with the M 1 receptor, acetylcholinesterase, glycogen synthase kinase-3, which are the key targets for cognitive enhancement. CYP inhibition assays indicated that EGb 761 ® and the drugs derived from EGb 761 ® had lower inhibitory activity compared to other samples. Conclusion The high PCA content in EGb 761 ® may contribute to cognitive benefits. With low CYP inhibition, it suggests minimal interference with drug metabolism, highlighting its potential as a safer cognitive enhancer. Ultimately, this study indicates that the composition of EGb 761 ® can be effectively leveraged for its pharmacological benefits.
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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 | 0.001 | 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) | 0.002 | 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".