The use of Cannabis sativa is always been quite controversial: it can have beneficial health effects for several pathologies, be an environmentally friendly raw material for manufacturing and textiles, but it is also the most widely spread illicit drug in the world. However, the recent discovery of the endocannabinoid system, generated an increasing interest for this plant in the pharmaceutical and therapeutic fields. In fact, Cannabis produce a class of terpenophenolic compounds called phytocannabinoids, of which the most abundant and intoxicating psychoactive is delta-9-tetrahydrocannabinol (THC). Another important phytocannabinoid is cannabidiol (CBD), able to contrast the psychoactive effects of THC. Based on their THC and CBD content, the numerous strains of C. sativa are distinguished into drug-type, with high THC content, and fibre-type or hemp, with CBD as the main phytocannabinoid. Besides these two compounds, more than 100 other phytocannabinoids have been identified to date.
The objective of this proposal is the comprehensive identification of phytocannabinoids in industrial hemp. In general, most of the studies profiling phytocannabinoids published in the literature report only the major constituents. However, it has been demonstrated that low-abundance cannabinoids could play a crucial role in determining the pharmaceutical properties of Cannabis and its derivatives.
For this reason, inflorescence and other aerial parts of hemp of different strains and grown in different conditions will be analyzed and compared for their phytocannabinoid profile by high resolution mass spectrometry, using a suspect screening approach.
If the analytical platform will be successful, it could be possible the identification of novel bioactive compounds from industrial hemp. Furthermore, a comparative analysis of samples could allow a better understanding of the most bioactive strains of C. sativa or the finest pedoclimatic conditions for its cultivation.
The purpose of this project consists in gaining a comprehensive knowledge on phytocannabinoids of industrial hemp, paying attention also to less studied (because at low concentration) compounds. This could be useful for possible pharmacological, cosmetics and nutraceutical applications.
Delta9-THC and CBD, along with a few other cannabinoids, have been extensively investigated by targeted MS for obtaining quantitative results, which are particularly important in the case of Cannabis sativa. Based on their relative content, different strains of cannabis are in fact classified as fiber-type or drug-type, often also determining its legal status. However, the more the interest in Cannabis has increased for pharmaceutical and therapeutic applications, the more the knowledge on hemp phytocannabinoid content has grown in recent years, resulting in the need for a different approach rather than targeted analysis. In fact, it is known that low-abundance cannabinoids could play a crucial role in determining the pharmaceutical properties of Cannabis and its derivatives.
Whether comprehensive characterization of strains of Cannabis are needed, low-abundance compounds are searched or metabolites of cannabinoids after assumption are investigated, untargeted analyses represent the most viable strategy. Untargeted approaches based on HPLC coupled to HRMS permit the simultaneous collection of large sets of data of both known and unknown compounds while forgoing the opportunity to perform quantitative analysis. Moreover, targeted analyses furnish extremely rapid and straightforward results, while data analysis of the gigantic sets of data collected in untargeted fashion cannot be carried out manually, and dedicated software programs for the extractions of features from raw data are generally required. Thanks to MS-based data processing software programs, m/z ratios and their associated MS/MS spectra can be extracted and aligned, and diverse adducts deriving from the same compound are grouped, thus generating a list of features to manually validate according to retention time, masses and diagnostic product ions.
Furthermore, with the purpose of streamlining the manual validation, data processing programs grant the access online MS-based databases and libraries for automatic matches of features to compound names, structures and, sometimes, recorded MS/MS spectra. Even the most complete available databases, however, do not possess exhaustive data for structure-related classes of compounds, such as phytocannabinoids, especially when it comes to the study of unreported or unknown species. Moreover, since small molecule masses and molecular formulas are shared among many diverse species, broad range database are often unsuitable for the profiling of a specific class of compounds. Therefore, a different approach for raw data analysis has to be chosen. Indeed, compiling a database of reported and unreported cannabinoid derivatives, on the basis of the structural modifications reported in the literature, it is possible to switch from an untargeted approach to a suspect screening approach, thus making the identification more reliable and semi-automatized.
If the analytical platform will be successful, it could be possible the identification of novel bioactive compounds from industrial hemp. Furthermore, a comparative analysis of samples of different strains or of the same strain but grown in different geographical/agronomical conditions, could allow a better understanding of the most bioactive strains of Cannabis sativa or the finest pedoclimatic conditions for its cultivation.