DiscOmic Labeling System for Cannabis


In this article, Marco Troiani and Savino Sguera of Digamma Consulting explain a new labeling system for cannabis consumers and its utility.

The following is an article produced by a contributing author. Growers Network does not endorse nor evaluate the claims of our contributors, nor do they influence our editorial process. We thank our contributors for their time and effort so we can continue our exclusive Growers Spotlight service.


Editor’s Note: This is a long contributor article, so I have included links to skip to different sections.

  1. Why is a labeling system needed for cannabis?
  2. How does the DiscOmic labeling system work?
  3. References


Why is a labeling system needed for cannabis?


The purpose of a labeling system is to communicate the contents of a product to an interested observer. The observer could be a prospective consumer about to make a purchase or a producer or broker who is negotiating industrial scale purchases and sales of product.

In many industries, such as food, labeling conventions have been well-established. The FDA has developed very specific guidelines for foods, showing the now famous “Nutrition Facts” label that displays the three basic biomolecular categories: protein, carbohydrates, and fats. Looking at this label, a person can get a sense of what they are about to ingest. For example butter, olive oil, coconut oil, and avocado are all very high in fats. Wheat, potato, corn, rice, and sugarcane are all almost entirely carbohydrates. Meats and beans have very high protein contents. Most nuts are considered as “superfoods” because they contain an even balance of protein, carbs, and fats.

Another example exists in the pharmaceutical industry, which is also regulated by the FDA. The labels for drugs are very different from the food industry. Whereas food represents a living continuum of countless chemical diversity, drugs are designed to ideally have only one or two active ingredients per pill, with the rest made up of an inactive filler. Pharmaceuticals are rather easy to label because all one needs to do is label the dose of the active compound and the consumer knows all they need to know about a particular formulation of a medicine.

Cannabis, when compared to food and pharmaceuticals, is crossing a new frontier in this era of human history — a fusion of the biological continuum of plants and the analytical and deductive approach of western pharmaceutical medicine. I say “in this era of human history” because, before the modern age, plant medicines and folk wisdom were widespread among different cultures, and in many parts of the world this style of medicine is still widely practiced. But in modern western medicine, removing “unnecessary” ingredients from the final product has long been the goal, precisely zeroing in on the one “true” molecule responsible for its effects. This philosophy led to the pharmaceutical terms of “active ingredients” and “inactive ingredients”. However, as recent scientific interest in the entourage effect has picked up speed, western scientists are findings that combinations of compounds, especially ones that are co-present in naturally occurring organisms, may be greater than the sum of their parts.1, 2, 3, 4, 5

One of the major roadblocks to the development of a cannabis-based labeling system is that it does not fit either FDA model. When we try to follow the pharmaceutical model we get dispensaries labeling their products by percentage of THC and occasionally percentage of CBD. Not including the cannabinoid acids, many states are now mandating that other cannabinoids such as CBN, CBC, THCV, CBDV, CBDN should be enumerated in labels as well. Thorough cannabis labeling is becoming more important as other cannabinoids are being bred into strains. CBD interest has increased recently, and interest in THCV has begun to increase as well.

Cannabinoid labeling does not factor in terpenes, of which over 100 have been identified in cannabis. In 2018, any cannabis lab worth its salt can deliver reports on at least 20 terpenes to their clients. The issue is: what do you do with this information? Show all 20 of them to the consumer? Show them all to the producer? Unless you have a biochemist or biostatistician analyzing these numbers, they will most likely overwhelm and confuse the viewer.

What this means is that very often a lot of valuable data on cannabis is essentially discarded, as sales and compliance take precedence in the eyes of retailers.

Enter the DiscOmic system. Consumers want to know the details about their cannabis, and retailers and producers often have that information, but color coding 30 numbers just isn’t going to work, as it is too confusing to be used intuitively. The DiscOmic system allows an individual to enter cannabinoid and terpene data into a browser or application and generate a simple, easy-to-use label that can represent any sample of cannabis in the world, real or imagined.

The DiscOmic system uses the powerful pattern recognition of the human brain to tie together strains with similar profiles. Instead of trying to “teach” the brain what every nuanced terpene in the cannabis sample “means”, we allow the brain to draw broad patterns based on similarities. Because this can be difficult to visualize, let’s examine a few illustrated labels:

Image showing six different samples that were lab tested for terpenes, and their resulting DiscOmic labels. Note the intuitive nature of recognizing self-similarity makes this system very easy to use. Of the six profiles, two showed noteworthy similarity and have been outlined in blue.

Viewing the illustrations we can see, without knowing or attempting to decipher what the colors and layers in the DiscOmic label signify, an exact match, a close match, and can confidently tell when there is not significant match. This is because DiscOmic was not designed to be used by scientists but everyday cannabis consumers from all walks of life.

Those who can most benefit from this intuitive labeling system are cannabis consumers and cannabis producers:

  1. The consumer can easily memorize a consistent terpene profile in the cannabis they purchase from a dispensary, even as dispensaries routinely run out of certain strains and replace them with new ones, creating a revolving door menu.
  2. Producers also benefit. A scientifically-backed method of demonstrating strain consistency allows producers to sell their products with more confidence and assure intermediaries of the quality and consistency of their products.
  3. Producers can also protect themselves from fraudulent competitors who simply re-name inferior cannabis to match the most fashionable strain at the time. Growers and distributors can defend their product from “generic brand” cannabis and protect their strain’s value over time.

Diagram showing how the DiscOmic label can be display individual terpene content by using the color-coded terpene-group system outlined above.

In addition to being useful for similarity comparisons between strains of cannabis, the DiscOmic label can be read to describe the concentrations of individual terpenes or terpene groups in the cannabis sample. The nature of the groupings is determined via statistical analysis of cannabis with enzymology and biosynthesis pathways, which we will cover in greater detail later.

The groupings follow this basic scheme:

  1. The inner layer is myrcene (red) vs. ocimene (blue)
  2. The middle layer is terpinenes and carene (purple) vs. limonene and linalool (yellow)
  3. The outer layer is caryophyllene, humulene, nerolidol (orange) vs. pinenes (green).

The greater the radius of the ring, the higher the concentration of the terpenes in that group. Using this system, a viewer of the DiscOmic label can tell what major terpenes are present in the cannabis. This is useful for two main predictions about the cannabis product: flavor and medicinal effect.

To be able to use the colors of the DiscOmic label to predict the fragrance of a cannabis sample, the “smell and feel” of each color component needs to be understood by the viewer. To help the viewer better understand the scents of the component terpenes, we have included a chart illustrating these compounds’ scents.

Chart outlining the human qualitative sensations of the major terpenes present in cannabis, color-coded with the DiscOmic label scheme. The chart outlines each terpene as it’s found in nature, and a description of the smell of the isolated terpene.

In addition to terpene profile informing on the flavor and fragrance of a cannabis sample, the medical value of cannabis is closely tied to its terpene content. At Digamma, we have built a database with 499 peer-reviewed medical science research papers looking at one or more of the components of cannabis in relation to human disease. Using this database, we have generated six tables showing the weighted contribution of each terpene to treating a set of disorders. This allows software to connect patients with specific disorders and make recommendations based on data derived from the DiscOmic label. The six tables we have summarized cover the following disorders: cancer, anxiety, depression, pain, insomnia, and infections.

Six tables correlate the major terpenes in cannabis with the number of research studies confirming a connection between the listed terpene and the listed disorder. Terpenes have been color-coded to match their DiscOmic groupings. Lists were derived from dataset of 499 peer-reviewed publications and 958 unique cannabis flower samples analyzed for terpene content.

In the references we have provided a chart to help connect each terpene by DiscOmic group for those wishing to take a closer look at the scientific literature on the medical effects of terpenes.

DiscOmic Group Terpene References
Orange Caryophyllene 6-34
Orange Humulene 35-42
Orange Nerolidol 43-50
Purple D-Terpinene (Terpinolene) 51-59
Red Myrcene 60-90
Blue Ocimene 91-100
Yellow Limonene 101-145
Yellow Linalool 146-183
Green Pinenes 184-200


How does the DiscOmic labeling system work?


There are two critical components to the DiscOmic system: statistical analysis using artificial intelligence (A.I.) algorithms and biochemical enzymology. The AI component explores and discovers relationships between terpene groups, and the enzymology component verifies and validates statistical patterns observed in the data gathered from the studies.

The AI component of the DiscOmic system began with simpler observations rooted in competing terpene axes. A competing terpene axis is when two terpenes, though both common at high levels in cannabis, are rarely, if ever, present in large quantities together. This indicates a genetic link, which we will examine in greater detail in the following section addressing enzymology and genetics.

Competing axes, when graphed with one axis representing the concentration of each terpene, visually displays the competition between the terpenes (represented by a strong negative correlation). But we’re looking at 23 different terpenes for our dataset. To look at the correlation of every terpene would require generating 253 graphs and analyzing each one individually.

In addition to the impracticality of generating 253 graphs for every dataset analyzed, some of the correlations may not be visible when only examining the data in a series of one-to-one relationships. Some patterns are only visible when the sum of one group is compared to another, as is the case with some of the DiscOmic terpene groups. In these scenarios, the amount of terpenes in a group varies widely and seems to have no correlation, but when that group’s data is measured as a sum, very clear patterns start to emerge. We have illustrated two sets of terpene group axes below. This shows how a grouping selection can illuminate patterns in the data that were not previously accessible and would have been less likely to be discovered with a one-to-one relationship approach.

An illustration of the oppositional nature of competing terpene groups in two scatter diagrams. The left diagram compares the sum of the sesquiterpenes caryophyllene, humulene, and nerolidol to the sum of alpha and beta pinene. The right diagram compares the sum of limonene and linalool with the sum of the terpinenes and carene. All data has been normalized based on the dataset.

An AI approach that allows for the inclusion of both rapid multi-component comparison and the discovery and identification of stable groupings is a technique called self-organizing maps. In this data analytics technique, the data is plotted with every value treated as an independent axis. Because there can only be three spatial axes and this model uses 23, the data is grouped by similarity in a higher dimensional space. Once the data is scattered out in hyperspace, a process called unsupervised learning begins to collect patterns in the data. The algorithm is unaware of any group labels, and blindly looks for patterns in the hyperspace grouping. The result is that a dataset with 23 variables can be reduced into a dataset with six numbers, without losing significant information. In this way the process can be likened to a data compression program that creates a .zip archive file, because it reduces information in a way that does not lose anything that cannot be reconstructed from the compressed file itself.

To better understand how human and robot minds created the six groups in the DiscOmic label system, we will examine some of the self-organizing maps that were created by unsupervised learning algorithms.

A diagram illustrating the concept of a self-organizing map. On the bottom the variables, x1, x2, etc, each have a connection to a sample represented on planar grid. The result is a heat map with a hot-spot over the cluster of samples representing the clustering of one of the variables.

The concept of a self-organizing map is essentially a plane in a higher dimensional space upon which each variable cluster can be projected. Because a plane is two-dimensional, we can visualize this space, and using colors in a heat map system, we can visualize the diffusion or spread of the clustering as its projection intersects with the plane.

For self-organizing maps describing cannabis samples, each unit on the map represents a sample or group of samples. They are organized in the cluster sharing the feature or grouping of features, in this case high concentrations of certain terpenes. The self-organizing map is set to one terpene and the heat-map shows the cluster of that terpene in the grid. As the self-organizing maps of different terpenes are compared, the clustering within the group of samples can be seen readily, and the terpene groups can easily be identified.

Illustration of two planes of cannabis data illustrated as self-organizing maps. The first plane shows only two components, CBDA and THCA. The second plane shows several terpene components which are grouped into opposing or non-overlapping clusters.

Examining the terpenoid plane across the major terpenes shows four major cluster spots, roughly corresponding to the four corners of the plane. The diametric opposition of the orange and green groups and the purple and yellow groups is graphically visible in the plane of the self organizing maps. With these 11 two-dimensional images, we have been able to correlate the kind of data that would have taken hundreds of two dimensional images generated with a more-traditional scatter-graph approach.

Editor’s Note: The following section is technically complex, be ye forewarned.

Once the AI component of the analysis generates tightly-correlated groupings and matches them with competitive groupings, we have the three concentric layers of the DiscOmic system. At this point, we have groupings that make statistical sense, but no underlying biochemical theories that support the observed patterns. In order to add strength to the organizational scheme, we have developed underlying enzymological hypotheses for each of the three rings used in the DiscOmic system. Before we review how the data relates to enzymology in cannabis, let’s do a quick review of enzymology, biosynthesis, and genetics, and how all three relate.

An overview of how enzymes mediate reactions that occur during biosynthesis. Clockwise from top-left: A scheme for an anabolic enzyme that builds chemical bonds; a scheme for a catabolic enzyme that destroys chemical bonds; and an example of a biosynthetic enzyme-mediated pathway of limonene.

Here we see a quick overview of the mechanism of enzymes. Enzymes are either anabolic, meaning that they build bigger molecules by forming chemical bonds, or catabolic, meaning they break molecules down by cleaving chemical bonds. In fact many enzymes have activity of both anabolism and catabolism, but every enzyme must be a member of at least one of the two categories. By using combinations of these enzymes that target different bonds and different molecules, nearly any molecule imaginable can be constructed.

These linear biosynthetic pathways are the link between genetic information and physical traits. If a genome is a library, genes are like books, and each book is a set of instructions on how to build a specific enzyme or protein. Any physical difference in the enzyme will cause physical differences in molecules made by that enzyme. It is these differences in molecular structure that are the cause of the differences in physical traits in the whole organism.

The anabolic process can be thought of as a long assembly line, where each enzyme modifies a chemical bond and then passes the molecule to the next enzyme in the line. This is graphically represented as biosynthetic pathways, like the one illustrated above for limonene. Although humans, for simplicity of interpretation, like to represent the pathway to one molecule as a straight line, the truth is that many molecules are made from common precursors, causing our linear biosynthetic pathways to branch out. Inside the cells of an organism, these pathways happen in parallel at the same time, and the precursor reaction must produce enough precursor to distribute to all the enzymes.

If there is not enough precursor to keep every enzyme at full capacity, they compete for available precursors, often with the winners being faster reaction enzymes and the losers being slower reaction enzymes. To better illustrate the complexity of terpene biosynthesis and enzymology, we have constructed a biosynthetic mapping of terpenes and cannabinoids based on the research paper by Rodney Croteau published in 1987 on terpene synthesis.6 Although Croteau’s paper did not perform experiments in cannabis plants, the terpene synthesis pathways examined were similar enough to create a biosynthetic landscape within which the enzymological analysis of cannabis could operate.

Map overview of the biosynthesis of terpenes in higher plants. The universal precursor, mevalonic acid pyrophosphate, can be seen in the top-left of the image. Arrows indicate enzymatic transformations and branch out in numerous directions to a variety of terpenes and intermediates. Colors from the DiscOmic system have been used to circle the major terpenes in each terpene group. Editor’s Note: Click the thumbnail for a larger image.

The complexity and coordination involved in biosynthesis is evident in the above enzymological map. The highlighting of the major terpenes in each of DiscOmic’s terpene group illustrates the complexity of finding these patterns, and why the AI measures described above were necessary for establishing definite patterns. Because the enzymology of terpene synthesis is so complex, we will examine a biosynthetic map that focuses exclusively on the compounds of interest and their common precursors in order to better understand each DiscOmic terpene group.

Enzymology mapping of the relationship between the outer-ring groups 1 and 2. Intermediates illustrated as black circles, and enzymes are indicated in blue. Terpenes are indicated by major group color.

The first map, the sesquiterpenes (orange) vs. the pinenes (green) is a straightforward inverse relationship. The best way to chemically explain inverse statistical relationships is to describe a system with a common precursor and two competing enzymes. In this scheme the common precursor is labeled as precursor 1 (or P1). P1 is hypothesized to be geranyl pyrophosphate based on the available data.6 Two enzymes, E1 and E2, compete for this precursor to generate intermediates that can either become pinenes or sesquiterpenes. Additional enzymes may mediate the distribution of terpenes within that group, such as E3 and E4 mediating P2, believed to be farnesyl pyrophosphate based on available evidence.6

Enzymological models such as this can easily explain the phenomenon where individual terpenes fail to have any correlation but show a strong correlation as a group. This would explain how the total of the group would be a more consistent number than the components, because the components of that group are all derived from a common precursor.

Enzymology mapping of the relationship between the middle-ring groups 4 and 3. Intermediates illustrated as black circles, and enzymes are indicated in blue. Terpenes are indicated by major group color.

The second map, the terpinenes (purple) vs. the limonene-linalool (yellow) is also a straightforward inverse relationship. Again, we hope to describe a system with a common precursor and two competing enzymes to explain the observed correlation. In this scheme the common precursor is labeled as precursor 1 (or P1). P1 is hypothesized to be linalyl pyrophosphate based on the available data.6 Two enzymes, E1 and E2, compete for this precursor to generate intermediates that can either become terpinenes or limonene-linalool. Additional enzymes may mediate the distribution of terpenes within that group, such as E3 and E4 mediating P2, believed to be alpha-terpinyl pyrophosphate.6

Once again, we see a scheme where individual terpenes fail to have much correlation but show tight correlation as a group. The reasoning is based on the stable number of the total, contrasted with the more chaotic competing enzymes that do the “final touches” on terpene synthesis.

Enzymology mapping of the relationship between the inner-ring groups 6 and 5. Intermediates illustrated as black circles, and enzymes are indicated in blue. Terpenes are indicated by major group color.

The third map, ocimene (blue) vs. the myrcene (red) is also more complex relationship than was observed in the first or second map. Ocimene and myrcene are not inversely correlated, where the presence of one competes with the other, nor are they directly correlated, where the amount of one is tied to the amount of the other in a fixed ratio. This relationship shows no observable correlation, yet both terpenes are present at such high concentrations that they constitute a substantial portion of a cannabis sample’s terpene content. The lack of correlation between the two cannot be explained very easily, because both are known to be made through the precursor geranyl pyrophosphate.6

The best theory that fits the evidence at this time is that both ocimene and myrcene can be made from a common precursor, but each terpene can also be made from derivatives of that precursor.6 Now if ocimene and myrcene are made from the exact same precursors, then we would see them in a fixed ratio. But, if ocimene and myrcene are able to interact with different derivatives of geranyl pyrophosphate, then it would allow two independent numbers to be derived from the same baseline value of precursor. What would influence the final number of these two terpenes would be a mixture of enzymes, some affecting the myrcene pathway, some the ocimene pathway, and many having distinct effects on both.

This degree of complexity is very common in enzymological schemes, especially in higher plants such as cannabis. With complications such as enzyme promiscuity, a phenomenon where one enzyme can actually cross-catalyze different reactions at different rates, these patterns can become even more challenging to understand. It is for this reason that tools for the consuming public, the cannabis industry, and the cannabis research community, are needed to help humans begin to benefit from the complexities of cannabis, even as the final aspects of our scientific understanding of the cannabis and human organisms are still being discovered.


References


  1. Russo, Ethan B. “Taming THC: potential cannabis synergy and phytocannabinoid‐terpenoid entourage effects.” British journal of pharmacology 163.7 (2011): 1344-1364.
  2. Russo, Ethan B., and John M. McPartland. “Cannabis is more than simply Δ 9-tetrahydrocannabinol.” Psychopharmacology 165.4 (2003): 431-432.
  3. What is Cannabidiol? | Project CBDhttps://www.projectcbd.org/science/terpenes/terpenes-and-entourage-effect
  4. CNN | Dr. Sanjay Gupta – https://edition.cnn.com/2014/03/11/health/gupta-marijuana-entourage/
  5. McPartland, John M., and Ethan B. Russo. “Cannabis and cannabis extracts: greater than the sum of their parts?.” Journal of Cannabis Therapeutics 1.3-4 (2001): 103-132.
  6. Croteau, Rodney. “Biosynthesis and catabolism of monoterpenoids.” Chemical Reviews 87.5 (1987): 929-954.



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About the Author

Digamma Consulting is a full-service laboratory and consulting firm for cannabis. Savino Sguera and Marco Troiani are leading partners for Digamma and have years of experience under their belts. Their market analyses and scientific insights have been well-received here at Growers Network.