Identification of minimum essential therapeutic mixtures from cannabis plant extracts by screening in cell and animal models of Parkinson’s disease
Bibliographic record
Abstract
Medicinal cannabis has shown promise for the symptomatic treatment of Parkinson’s disease (PD), but patient exposure to whole plant mixtures may be undesirable due to concerns around safety, consistency, regulatory issues, and psychoactivity. Identification of a subset of components responsible for the potential therapeutic effects within cannabis represents a direct path forward for the generation of anti-PD drugs. Using an in silico database, literature reviews, and cell based assays, GB Sciences previously identified and patented a subset of five cannabinoids and five terpenes that could potentially recapitulate the anti-PD attributes of cannabis. While this work represents a critical step towards harnessing the anti-PD capabilities of cannabis, polypharmaceutical drugs of this complexity may not be feasible as therapeutics. In this paper, we utilize a reductionist approach to identify minimal essential mixtures (MEMs) of these components that are amenable to pharmacological formulation. In the first phase, cell-based models revealed that the cannabinoids had the most significant positive effects on neuroprotection and dopamine secretion. We then evaluated the ability of combinations of these cannabinoids to ameliorate a 6-hydroxydopmamine (OHDA)-induced change in locomotion in larval zebrafish, which has become a well-established PD disease model. Equimolar mixtures that each contained three cannabinoids were able to significantly reverse the OHDA mediated changes in locomotion and other advanced metrics of behavior. Additional screening of sixty-three variations of the original cannabinoid mixtures identified five highly efficacious mixtures that outperformed the original equimolar cannabinoid MEMs and represent the most attractive candidates for therapeutic development. This work highlights the strength of the reductionist approach for the development of ratio-controlled, cannabis mixture-based therapeutics for the treatment of Parkinson’s disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".