Bibliographic record
Abstract
As societal attitudes towards cannabis evolve along with our understanding of the endocannabinoid system, medical cannabis is poised to re-enter the mainstream pharmacopeia, especially in North America. Presently, significant gaps in knowledge exist regarding the true chemical composition of medical cannabis as well as the pharmacodynamics of cannabinoids and cannabis extracts. As such, deconvoluting the chemical complexity of cannabis is essential for maximizing its therapeutic benefits and reducing adverse effects in the clinic. Accordingly, this thesis presents a comprehensive investigation of cannabis and cannabinoids at the pre-clinical level, drawing on core aspects of medicinal chemistry and applying developed techniques under large study settings. Through pilot experiments, extraction protocols for the optimal recovery of cannabinoids from different types of cannabis plant material were investigated and the diverse phytochemistry of cannabis was revealed through extensive chemical profiling, reiterating the need for standardization and consistency in therapeutic-grade cannabis extracts. Meanwhile, receptor profiling at CB1 and CB2 receptors confirmed the importance of decarboxylation for the optimal biological activity of cannabinoids and cannabis extracts, demonstrating the suitability of microwave-assisted extraction for producing pharmacologically active cannabis extracts. Following this, high-level chemoinformatic analyses of receptor responses versus cannabinoid concentrations of cannabis extracts generated meaningful prediction models for CB1R and CB2R agonism, predicting the cannabinoid receptor activities of individual phytocannabinoids with reasonable accuracy and revealing differences in cannabinoid receptor responses between pure cannabinoids and cannabis extracts. Finally, the total synthesis and characterization of two pharmaceutically novel cis stereoisomers of CBD was undertaken, resulting in the first unambiguous identification of cis-CBD species and the isolation of a hitherto undescribed cis-Δ9-THC species. Through this thesis, we report significant real-world evidence for the case that cannabis and cannabis extracts behave as complex mixtures of physiologically active molecules, whereby their effects on receptors vary significantly with chemotype and in turn reflects their chemical compositions. Several first-in-kind studies detailed in this thesis pave the way for further investigations of cannabinoids into their chemical structures, mechanism(s) of receptor binding, as well as synergistic and antagonistic effects during co-administration that together bring about the unique and medically important effects of cannabis.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".