Problem: Cultivators and retail employees use unvalidated anecdotes to classify Cannabis flowers.
Cannabis contains many active ingredients. When consumed, these ingredients are associated with many different effects. This many-to-many relationship can be described as molecular clusters intertwined in a web of psychological and physiological events. Traditional pharmaceutical studies have created oversimplified models of a single Cannabis ingredient causing a single effect. The approach is not well suited for botanical Cannabis uses. Studying botanical Cannabis necessitates a broader model where ingredients are evaluated in synergy.2
The sativa/indica distinction as commonly applied in the lay literature is total nonsense and an exercise in futility.
Dr. Ethan Russo (20161)
Classification: 30 years of research proves cannabis can be classified scientifically.
DYC Classification. The most accurate research on real world Cannabis evaluates relationships between the plant’s active ingredients. Traditionally, research has focused on THC to describe its effects. On the other hand, consumers have used “sativa”, “hybrid”, or “indica” to identify Cannabis types without knowing the chemical composition of Cannabis. However, “sativa”, “hybrid”, or “indica” are phenotypic descriptors of Cannabis and not predictive of its effects.3 As an alternative, consumers have used aroma as an ad hoc determinant of Cannabis types. Preliminary evidence suggests Cannabis aroma may be more predictive of subjective pleasurable effects than THC content.4 From the community’s perspective, the aroma classification somewhat worked. However, an aroma based system is not logistically available to consumers. Packaging requirements restrict access to Cannabis aroma. Health and safety standards don’t permit open containers of Cannabis where consumers might be able to smell the flower. In response, DYC presents a simple classification system that communicates the plant’s characteristics by symbol and color. The system involves objective measures, contributes to inventory management, and creates quality assurance (QA).Although Cannabis sativa contains many active ingredients, two cannabinoids stand out in highest content. THCA and CBDA are Cannabis dominant cannabinoids. They are derived from a single precursor, CBGA. Their relationship is represented by molecular and genetic links. As an expression of Cannabis, THCA and CBDA are associated with a ratio of enzymes that produce them, namely THCA synthase and CBDA synthase.5 Consequently, THCA and CBDA are seen in Mendelian distribution where Type I (high THCA/low CBDA), Type II (equivalent THCA/CBDA), and Type III (low THCA/high CBDA) identify three cannabinoid chemotypes.6 Clinical evidence supports a predictive effect to the relationship between the Cannabis dominant cannabinoids.7
Cannabis potency labeling is misleading despite the regulatory requirements for unbiased third party testing.8 Labels list the content of single active ingredients without describing the relationship between ingredients. Poor labeling impacts medical patients who want to control dosage as well as customers expecting an effect. Operators, who are required to identify single measures of THC and CBD, fail to communicate the composite of active constituents that actually contribute to potency.
Government definition for hemp draws a line at 0.3% THC. The distinction creates a situation where a single plant could be identified as hemp and marijuana.. Confusion ensues as the hemp industry sells products under far less regulations than the marijuana industry. The same product might be sold in both industries as consumers are uncertain which to trust. From the operator’s perspective, conflicting laboratory protocols make lab comparisons difficult, the standards used vary from lab to lab, and sampling bias increases variance. The DYC solution establishes a standard that normalizes interlab variability yet ensures consistency.
Dual Classifications. Incorporating both cannabinoid and terpene ratios in a dual classification system, presents an objective alternative to “sativa”, “hybrid”, or “indica”. As a primary classification, Type I, Type II, and Type III represent the most effective chemical relationships in Cannabis. Dominant cannabinoid ratios accurately portray the relationship. Cannabis Type I is the most psychoactive type with the highest ratio of THCA to CBDA. Cannabis Type II has more balanced levels of THCA and CBDA and may be ideal for pain or sleep. Cannabis Type III contains the least amount of THCA, yet results in an extensive range of effects including reducing inflammation, reducing anxiety, and attenuating pediatric seizures.
DYC incorporates a second classification using Cannabis terpenes. Although lower in content, terpenes have been recognized as important constituents in Cannabis.9 They are volatile organic hydrocarbons that are a component of the essential oil in most plants. Whether ingested or inhaled, terpenes also contribute to Cannabis flavor and aroma. When combined with cannabinoids, they commonly act in synergy.10 The Cannabis research organization, CESC, has evaluated the relationship between Cannabis terpenes and reported on its influence on consumer effects.11
Validation: Community-driven feedback in line with research results.
Cannabis constituents and consumer attributes present a many to many relationship that must be incorporated into any predictive classification system.
DYC Validation. The DYC system has been validated using a database of laboratory tests associated with over 30,000 consumer responses.12 The system associates natural biochemical clusters in Cannabis with different effects. Dominant cannabinoid clusters are represented by Type I, II, or III and natural terpene clusters are represented by a DYC designated color. YELLOW represents a cluster of terpenes that are associated with an energetic and creative effect, whereas GREEN represents terpenes that result in more focused and productive effects. BLUE represents a cluster of terpenes that can be relaxing yet euphoric, whereas PURPLE terpenes are attenuated resulting in drowsiness and sleep. It’s important to note that people are different. Cannabis requires an initial period of consumer trial and error (i.e. discover your color) prior to predicting its effects.
Quality Cannabis Relies on Validated Evaluations. The To validate its symbols and colors, DYC partnered with the CESC’s Dosing Project Initiative, an epidemiological study on community-acquired Cannabis products and their effects. Based on the work of Dr. Jean Talleyrand, M.D. and Dr. John Abrams, Ph.D. the Dosing Project applies multivariate analysis to consumer reported data, determining effective doses of Cannabis while optimizing side effect profiles The Dosing Project Initiative validates DYC classifications, as they are supported by consumer trial and error to inform best practices.
Principle Objectives:
- DYC Cannabis classification system applies symbols and colors to cannabinoids and terpenoids
- Evaluations of consumer effects validate the classifications
- Ongoing analysis establishes a dynamic algorithm
Dosing Project Study. In the Dosing Project study on the left, consumers respond categorically to questions on Cannabis dose and the effect on chronic pain. The contingency analysis depicts the probability of a therapeutic effect after smoking or vaporizing Cannabis flowers. In each graph, the width of a column indicates the number of observed responses. Green represents the probability of a complete therapeutic response, whereas yellow, red, and purple indicate almost complete, somewhat complete, and no therapeutic response, respectively. The most popular dose is reported as 3-4 puffs. With Type I Cannabis use the dose-effect relationship increases (right). Consumers are most likely to have a complete therapeutic response when consuming 1 or more grams. However, comparisons of all Cannabis types creates a confused picture (left). In conclusion, the likelihood of a therapeutic effect is dependent on the type and dose of Cannabis used. The analysis validates the benefit of cannabinoid classifications to accurately evaluate the dose-effect relationship.
The DYC algorithm models the relationship between terpene clusters, aroma, and reported “sativa-hybrid-indica” effects. Ideally, multivariate analysis can be used to describe relationships between Cannabis terpenes. In the above graph, Type I Cannabis is represented by three terpene-derived chemotypes. A scatter plot overlays the chemotypes to the aroma and proposed effects of smoked or vaporized Cannabis. Fuel, earth, and floral aromas correlate with energetic (sativa), neutral (hybrid), and sedative (indica) effects. The algorithm suggests two “sativa’ chemotypes. The “indica” cultivars comprise three-quarters of the data set, primarily due to localized sources. The canonical center represents an overlap of attenuated terpenes suggesting a fourth chemotype. This analysis proposes a terpene classification system that can be applied to predict consumer effects.
Consumer responses are associated with terpene clusters. Many of the terpenes produced in Cannabis interact with cannabinoids.13 In this proof of concept analysis, Type I DYC colors are compared to a large database of both positive and negative consumer responses. After smoking Type I Cannabis flowers, respondents identify the effects they experienced. All four colors were associated with consumer responses indicating that the effects were universal. Some effects were more prevalent than others. For example, significantly more consumers felt energetic after using a DYC yellow or relaxed after using a DYC blue, green,
or purple.
Product comparisons validate the model. DYC classifications provide objectivity, which introduces consumers to quality assurance. Operators can use the classifications to improve their inventory management and maintain a broad range of products. DYC also provides a structure for product comparisons. The 3D graph above depicts a Type I flower inventory at a Cannabis dispensary. Four cultivars (strains) were identified for study.. Three are located at the extremes indicating that they have the highest terpene content. The fourth cultivar is at the canonical center where each of the three terpene clusters have attenuated. An analysis of the four cultivars demonstrates effects from different terpene profiles. Repeat analysis tracks trends or drifts in terpene content as cultivators experiment with crossing and hybridization,
The DYC model depicts outliers.
- Cultivars that are chemically exceptional stand out.
- Products that are supplemented or altered also stand out
Focus groups and case studies contribute to a narrative. An important contribution to the validation of Cannabis products involves qualitative responses. Qualitative analysis focuses on effects that are difficult to measure. For example, the psychoactive effect of Cannabis is difficult to quantify and can be assessed in a narrative. Discourse and themes associated with the Cannabis “high’’ demonstrate different perspectives. Qualitative analysis provides a broader perspective of the DYC classification system.
Certification: Consumer-friendly certification sets the standard for the industry.
DYC Certification. Scientific methodology uses a comprehensive approach to the many to many relationships between Cannabis constituents and their multiple effects. Certification attests to product consistency and facilitates the appropriate use of Cannabis.
Predicting an association between color, dose, and effect. In the analysis on the top left, three DYC colors are evaluated for the safe and effective dosage when treating pain. Multiple correspondence analysis is applied to discover associations between categorical variables. In this example, there are five dosage categories, 1-2 puffs, 3-4 puffs, 5-6 puffs, ½ gram, and 1 gram. The safest and most effective dose among the cohort of respondents is indicated by the proximity of the DYC colored arrow to each dosage category (blue dot). This analysis suggests
an appropriate color and dose for treating pain.
Value vs Price. Terpenes contribute to consumer experiences.14 In the example on the next page, DYC terpene classifications are overlaid with consumer prices to validate market trends. The analysis models three terpene profiles from a Type I Cannabis flower inventory in San Francisco where terpene content is considered a maker of quality. As terpene content increases, higher prices are assigned to the product. Wholesale and retail prices can be compared to objectively assess the value of products. The analysis provides an opportunity to compare value with price. Applying a value-price index improves consumer and operator purchasing decisions.
Achieving certification necessitates an assessment of product safety. Although Cannabis is not toxic, it can cause adverse events particularly for the inexperienced user. Cannabis induced adverse events are commonly attributed to delta-9 THC content or marijuana. Marijuana is defined as the Cannabis plant with a THC content greater than 0.3%. However, Cannabis contains many other active ingredients that interact with THC. Thus, it is more accurate to create an index, incorporating the multiple constituents of the Cannabis flower rather than solely THC content. CESC’s safety index was developed in the above example using the dosage of consumed cannabinoids per kilogram body weight. Logistic regression determines which dose is at a fifty percent probability of a complete or almost complete therapeutic response to pain. Likewise, we calculate which dose has a fifty percent probability of reported adverse events. The results are expressed as a ratio or index and are used to compare products by class. In the above example, Type I DYC PURPLE Cannabis flowers have a greater safety index than Type I DYC YELLOW Cannabis flowers.
Application: Go Discover Your Color
DYC classifications validate Cannabis trade and provide quality assurance for a better consumer experience.
A consortium of Cannabis scientists have contributed to the DYC Cannabis flower classification system. CESC founders, Jean Talleyrand M.D. and John Abrams, Ph.D., created a forum for real world Cannabis research. Dr. Abrams, a molecular biologist, provided an extensive background in biochemistry. His work on terpenes has pioneered advancements in Cannabis nomenclature. Dr. Talleyrand, a Cannabis specialist with over 25 years clinical experience, is the principal investigator of CESC’s flagship initiative, the Dosing Project. Focusing on multiple chemical classes associated with multiple consumer effects, Doctors Abrams and Talleyrand evaluate relationships in biological systems.
DYC provides symbols and colors for both operators and consumers to communicate Cannabis trade and value. The algorithm incorporates cannabinoids and terpenoids and is accessible to anyone with a Cannabis certificate of analysis (CoA). Users sign into the DYC platform, upload the CoA, and receive a symbol and color indicating on the plant’s chemical constituents. In its 9th year of operation, the Dosing Project solicits consumer responses and validates DYC product classifications. The initiative engages in a partnership with DYC that raises the tide for all operators and consumers to benefit from Cannabis quality assurance.
DYC puts a “stake in the ground” with an algorithm that communicates in color and symbols.
Endnotes
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576603/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334252/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576603/
- https://www.mdpi.com/2813-1851/1/2/8
- https://pubmed.ncbi.nlm.nih.gov/21653452/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283674/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221366/
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282396#:~:text=Average%20observed%20THC%20potency%20was,the%20highest%20label%20reported%20values
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9608144/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084246/
- https://www.taylorfrancis.com/chapters/edit/10.1201/9780429274893-2/aroma-cannabis-john-abrams-william-ellyson-victor-gomez-jean-talleyrand
- https://www.nature.com/articles/s41477-021-01003-y
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763918/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763918/
