Rational Design of Cannabinoid-Containing Complex Mixtures (CCCMTM) for Disease-Targeted Therapies
Bibliographic record
Abstract
To discover novel, disease-specific therapies, GBS utilizes rational design principles in creating Cannabinoid-Containing Complex Mixtures (CCCMTM) targeting the endocannabinoid system. GBS incorporates data from high throughput experiments using disease-specific cell and animal models that are combined with computer models of cannabinoid-sensitive receptor interactions in a predictive network pharmacology-based algorithm. The bioavailability of GBS’ Cannabinoid-Containing Complex Mixtures (CCCMTM) is enhanced using patent-protected, oral delivery systems including: a. oral dissolving tablets, b. time-released nanoparticles for oral administration, c. oral thin films, and d. gel capsules. Using an animal model of the disease, Proof of Concept has been established for GBS’ Parkinson’s disease therapy and the Mechanism of Action is being further explored. At the NRC Canada, GBS’ Parkinson’s Disease CCCM™ achieved the statistically-significant reduction of Parkinson’s- like symptoms in an animal model of the disease. Additionally, GBS’ neuropathic pain formulations look promising in animal studies. These important preclinical results will be included in GBS’ Investigational New Drug (IND) applications with US FDA in order to enter human clinical trial as soon as possible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".