Quality Management Systems and The Recall
Updated: Mar 9
Recently, many labs testing cannabis received a letter from a supplier that a concentration of the Δ9-THC qualified reference standard was 94% of the concentration listed on the certificate of analysis. This particular standard was in circulation from early in 2019 until it’s lack of purity was identified in late October 2020.
What is a reference standard, and why is this important? A reference standard is a well characterized, highly purified compound used in lab analyses to scientifically determine the concentration of an unknown sample. They are also used in the development and validation of analytical methods. This sounds complicated, but in essence it creates a mirror by which you can compare your existing sample to prove the purity and concentration of your sample. In testing cannabinoids, terpenes, heavy metals and pesticides standards are critical to accurately measuring the levels of contamination as well as measuring how active a sample is. A standard curve is created (in my analogy, this is the mirror) to compare the reference standard against the sample. Are you with me?
If the standard is not pure, the curve that we create from it works like a fun house mirror – exaggerating some aspects of the sample or minimizing others. When we know the purity of standard, we can adjust how we look into the mirror to compensate for the fun house effect. If we don’t know that a compensation is required, then the data that we collect from the fun house mirror will not represent the true characteristics of the sample.
Finding out that almost two years of data have been used for reporting cannabinoid concentration have been based on a less pure standard has caused concern to ripple through the entire industry. The thought of having to reach back through months of data to understand the impact of a mislabelled standard is soul-crushing.
So did this recall affect the results of cannabinoid testing in labs and therefore producer product? The short answer is – yes.
The longer answer is, in organizations with a robust quality management system in place, this kind of information is understood by performing an impact assessment. Before we get to how a lab would conduct the impact assessment, it is important to understand how a quality management system ensures consistent and reliable data. Within a quality program, a lab will validate their vendors. This can include reviewing COAs provided by the suppliers, but also looks at the consistency and reliability of the material supplied by each vendor. The quality management system also ensures that every step of testing is being followed including equipment calibration, software operations, which materials are used (including the supplier and lot number of all reference standards used), training of equipment operator, the document reviewer, and on and on.
Armed with all of this information, the quality system is able to retrospectively reframe the data collected and fully understand the impact of information like this. Our lab is currently reviewing the data of the last 21 months, but we have already conducted an initial impact assessment that shows this change in the concentration of the standard has not significantly changed the data we have reported. We know this from the third-party standard we run in every assay to show that the chromatography is repeatable.
Why is this important for a cannabis producer to know and understand? Producers selling cannabis to the retail market are responsible for the information on the label. So, if the lab for any reason, is not providing accurate information for the cannabis product label, the producer is responsible not the lab. A lab’s robust Quality Management System will catch system failures that could impact client data. It’s like insulation, it will protect the results, and this is critical to avoiding a producer recall.
Cannabis producers choosing a lab should ask the question and have eyes on a labs quality management system. This will come up during a lab audit and should be on any vendor validation form.