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Chemotyping Study Highlights Benefits of Cannabinoid and Terpene Fingerprinting

Jul 25, 2018 | By Alexander Beadle

Chemotyping Study Highlights Benefits of Cannabinoid and Terpene Fingerprinting

Credit: Jurassic Blueberries on Flickr

Over 500 unique compounds have been identified in cannabis, but despite this, much of the research focus in the field of cannabis science has been on the study of just two cannabinoids, tetrahydrocannabinol (THC) and cannabidiol (CBD). THC is the main psychoactive component of cannabis and is responsible for the ‘high’ that recreational users typically seek. CBD is non-psychoactive, but its properties as an anti-psychotic and anti-epileptic, among other things, have made it of significant interest to the medical community.

These are not the only two compounds that exhibit potential therapeutic properties. Other cannabinoids, such as cannabigerol (CBG), have also shown anti-inflammatory and analgesic effects. Additionally, some terpenes, the compounds that give cannabis its distinctive aroma, have demonstrated antiseptic behavior. 


Due to generations of crossbreeding between cannabis plants, a huge number of different cannabis cultivars now exist, each with differing ratios of medicinally active compounds. These ratios determine the overall medicinal effect of each specific cannabis cultivar. With this in mind, it is important that a formal classification system for cannabis cultivars is put in place to ensure the quality and reliability of cannabis for medicinal use.


Potential cannabis classification systems


Three main classification systems for cannabis are already in use. The first is based predominantly on botany and classifies cannabis into different categories based on the plant’s appearance, THC content, and geographical origin. The second method describes five chemotypes dependent on the ratio of THC and CBD present in the cannabis plant. Neither of these methods provides enough detail about all the medicinally significant components to be of real use when creating a classification system that can serve clinical researchers. 


The most promising method for clinical research involves the creation of a “chemical fingerprint” which includes more of the common cannabinoids and terpenes. While fingerprinting could be useful for drug standardization purposes, it does require many cultivars to be analyzed and fingerprinted before it becomes a practical system.


Analysis of the Canadian cannabis market


With this need for a wider sample size in mind, a team of Canadian researchers from the University of Alberta and Labs-Mart Inc. set out to fingerprint and classify a range of the cannabis cultivars that are available to purchase in the Canadian cannabis retail market. 


The team used high-performance liquid chromatography with diode-array detection (HPLC-DAD) to quantify ten cannabinoids in 32 cannabis samples. The ten cannabinoids included both Δ9-THC and CBD, their acidic counterparts THCA and CBDA, Δ8-THC, cannabigerol (CBG), cannabigerolic acid (CBGA), tetrahydrocannabivarin (THCV), cannabinol (CBN), and cannabichromene (CBC).


The presence of fourteen terpenes, all of which were known to display some medicinal or pharmacological activity, was also quantified. This was achieved using gas chromatography with mass spectroscopy (GC-MS). Both the HPLC-DAD and GC-MS methods were validated as part of the study and were found to have good specificity, repeatability, and precision.


Once the mass fractions of the cannabinoids and terpenes were measured for each sample, the data was examined through cluster analysis and principal component analysis (PCA). These methods allowed the cultivars to be grouped using hierarchical clustering.


Clustering cannabis cultivars


Across the 32 cannabis samples analyzed, the total THC content measured in weight percent varied between 0.24% and 7.08%. Total CBD percent was spread over a similar range, from undetectable up to 5.52%. Cluster analysis based solely on these values indicated four clusters. The first two clusters have high THC content, usually over 3%, with a CBD content of less than 1%. The third cluster contained cultivars with roughly equal THC/CBD content, and the final cluster consisted of a single CBD dominant sample. 


After including the other cannabinoids and terpenes the distribution of the cultivars within the clusters changed and the first three clusters all appeared THC-dominant, with the fourth cluster remaining CBD-dominant. Cultivars that were clustered together under this system were observed to have similar chemical compositions to their nearest neighbors. This similarity could lead to the effective prediction of neighboring cultivars medicinal properties.


PCA techniques were used to establish a rough order of importance in terms of how the content of each chemical compound influenced the cultivar clustering. Three variables were determined to be responsible for nearly two-thirds of the clustering formations. 


The first variable, PC1, represented a mixture of THCA with seven of the fourteen terpenes present. Clusters 1, 2, and 3 were all separated along PC1, indicating the three clusters have distinct combinations of THCA and the relevant terpenes. The second variable, PC2, described six of the ten cannabinoids tested, including CBD and CBDA. Cluster 4 is clearly separated from the others across PC2, which is to be expected as Cluster 4 was shown to be a CBD-dominant cluster. PC3, the third variable, relates to the content of α-Pinene and Δ9-THC. Clusters 2 and 3 are clearly separated across PC3.


The impact of cultivar classification


The classification of cannabis cultivars into broad clusters with similar composition may allow clinical researchers to predict the possible therapeutic effects of a given cultivar. This is important as it will give medical professionals access to better information that can help them provide the most appropriate care for their patients. By focusing on a broad array of cannabinoids and terpenes, this study found a clear clustering of three THC-dominant clusters and one CBD-dominant cluster, a stark deviation from the clustering pattern observed when only THC and CBD were taken into account. These new clusters also had more similar compositions within each cluster than the previous clusters, which demonstrates the potential effectiveness of this classification system.


The 32 cultivars studied here do not encompass the whole of the Canadian cannabis market, so it is recognized that there is still work to be done before a nationwide classification system is complete and can be effectively used for medicinal purposes. However, this research does make it clear that in order to classify cultivars in the most accurate way possible, a much wider range of compounds than simply THC and CBD need to be taken into account, and this study is just the first step towards a more accurate classification system.


 

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