For centuries, “humans have cultivated cannabis for the pharmacological properties that result from consuming its specialized metabolites, primarily cannabinoids and terpenoids,” wrote Lacey Samuels, PhD, professor of botany at the University of British Columbia, and her colleagues, in September. Today, scientists seek ways to improve cannabis production.
Samuels and her colleagues used quantitative electron microscopy to study cannabinoid-related enzymes. By tracking the intercellular pathways, these scientists developed a model of how cannabis cells produce metabolites, such as tetrahydrocannabinolic acid (THCA). Samuels and here team concluded: “This new model can inform synthetic biology approaches for cannabinoid production in yeast or cell cultures.”
Machine learning and synthetic biology
Other scientists also explore the use of cell culture to enhance the bioprocessing of cannabis. For example, Max Jones, PhD, associate professor of plant agriculture at the University of Guelph, and his colleagues reported: “In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems.”
Nonetheless, Jones and his colleagues pointed out that the complexity of the biochemical pathways makes it difficult to scale up the bioprocessing. By using machine learning and synthetic biology, though, these scientists believe that it’s possible to “optimize bioprocesses related to cannabinoid production.”
Many companies also work on ways to bioprocess cannabis-related compounds more efficiently. As one example, DYADIC developed a synthetic method of making cannabinoids with a C1-cell line. According to the company, C1-cells can be used to reduce the cost—compared to traditional plant-based extraction methods—of producing synthetic cannabinoids.
Based on academic and industrial research, the bioprocessing of cannabis promises more controllable products, as well as more economical and environmentally friendly processes.
