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Fully resolving cannabinoids with similar structural properties can prove challenging, the result in Figure 1 demonstrated several components with a resolution factor of <1.5. It is possible to use an intelligent algorithm previously reported for such challenging separations, in order to successfully quantify these partially co-eluting compounds more readily. This is called the intelligent Peak Deconvolution Algorithm (i-PDeA) [6]. Using this technique, it has been reported to deconvolute even positional isomers of o-methyl acetophenone, m-methyl acetophenone and p-methyl acetophenone [7]. As depicted in Figure 2 the same technique can be used for the deconvolution of the Δ9-THC and Δ8-THC co-eluted peak.
It was the objective of this study to further improve the resolution, whilst maintaining faster analysis of these cannabinoids. This was paramount where baseline separation was sought in order to quantify each component successfully, such as within medicinal cannabis analysis.
Retention modelling
Although retention modelling has been successfully employed in optimising analytical separations of small molecules for over 30 years, it is still not universal. Published chromatographic methods using trial and error approaches continue to be prevalent. Retention modelling software packages provide a fast and efficient means to optimise analytical separations whilst selecting conditions that provide the most robust methods. This type of Quality by Design (QbD) approach has become popular within the pharmaceutical industry and the FDA has cited a risk- based approach to drug development as a desirable state for the near future [7]. These reasons lead to the method described in this article being optimised using ACD/LC Simulator, Advanced Chemistry Development, Inc, Toronto, Canada. In Figure 3, regions of colours depicting resolution >1.5 correspond to LC conditions fully [baseline] resolving all 11 cannabinoids. The higher the resolution (Rs) number the greater the resolution of the critical pair. With this information it is possible to choose the analytical conditions which provide the optimal separation within a desired run time. Furthermore, it is also possible to select a region which offers robustness by simulating potential variance in temperature or tG. In addition, other variables such as pH and ternary mobile phase compositions can also be investigated using this strategy.
Figure 3: Simulated analytical conditions of 200 experiments. Method Details
The Shimadzu Cannabis analyser equipped with a photodiode array detector was used for the analysis. Accurate and reproducible resolution for all 11 common cannabinoids were achieved using the NexLeaf CBX column over a 12-minute analytical gradient. The analytical conditions are shown in Table 1.
Table 1. LC Method Parameters. LC System
Column Shimadzu Cannabis Analyser
NexLeaf CBX uHPLC column, 2.7 µm, 150 x 4.6mm
Mobile phase A Water + 0.1% formic acid Mobile phase B Methanol + 0.1% formic acid Rinse solution Methanol + 0.1% formic acid Flow rate
1.5 mL/min
Gradient program 0 - 12 min, 70-95%B Column
30 °C temperature
Injection volume 10 µL Detector
Co-injection
3D-PDA Water
The accompanying FDA 21 CFR Part 11 ready CDS used was the Shimadzu LabSolutions DB software. This analysis data system provides ER/ES compliance in regulated environments and included multi- data report functionality.
Materials
All solvents and diluents used were HPLC grade and pre-filtered via 0.45 µm filters from Romil Ltd. All diluents were isopropyl alcohol and methanol. Standards listed in Table 2 were obtained from Sigma-Aldrich® at a concentration of 1 mg/mL (in methanol).
Formic acid (puriss p.a.) was purchased from Sigma-Aldrich®
. Table 2: Analysed compounds.
Abbreviation Item Description CBC
CBD
CBDA CBDV CBG
CBGA CBN
Δ8-THC Δ9-THC THCA
THCV
Cannabichromene Cannabidol
Cannabidiolic acid Cannabidivarin Cannabigerol
Cannabigerolic acid Cannabinol
Δ8-Tetrahydrocannabinol Δ9-Tetrahydrocannabinol
Δ9-Tetrahydrocannabinolic acid
Tetrahydrocannabivarin Sample preparation
Varying sample matrix within the nutraceutical industry, has led to a plethora of dilution methods being reported. The most common on the market are oils containing CBD, these can be manufactured with varying types of oil such as hemp, olive or medium chain triglycerides (MCT) which are derived from coconuts. The next common sample type is vape/e- liquids, which have seen growth in general over the past few years, these have now been produced to include CBD within the e-liquids. Those companies that are producing these products from the raw materials, need to also test the flower and/ or bud they are using.
Due to oil-based products being used in both nutraceuticals and pharmaceutical production we tested a single MCT based product multiple times. Previous
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