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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".