How the Right Use of Data Can Transform Cannabis Testing Operations

May 26, 2022

Cannabis legalization in various US states has motivated the cannabis industry to ensure credibility and produce high-quality products for consumers. Cannabis testing labs also aim at clearing third-party audits and meeting regulatory requirements to assure product safety and quality. With increasing quality standards in the cannabis industry, testing labs focus on meeting regulatory compliance such as ISO 17025:2017, 21 CFR part 11, GLP, and state and local regulations. Moreover, increased attention from the US Food and Drug Administration (FDA) to safeguard public health is propelling the cannabis manufacturers and testing labs to follow the current Good Manufacturing Practices (cGMP).

Labs rely on data to monitor their performance and make decisions that help them meet the requirements of both consumers and regulators. However, labs face multiple daily challenges, data management challenges being one of the biggest ones. It is difficult to capture, analyze, and manage large volumes of data from multiple samples. For industries such as cannabis, making the maximum use of data becomes a differentiating factor for sustainable, long-term success. Cannabis companies can generate better outcomes and avoid nonconformance to regulatory requirements with the proactive use of cannabis data.

How to Make the Most Out of Your Lab Data

Leveraging the data correctly can help reduce human errors in cannabis labs and ensure improved data traceability and integrity. The following ways can help cannabis labs effectively leverage their data to overcome operational challenges:

1. Ensure Data Integrity for Decision-Making

Data-driven decisions in the cannabis industry help ensure the quality of the final products. Therefore, it is essential to maintain data integrity. According to the FDA, the data that is attributable, legible, contemporaneously recorded, original, and accurate (ALCOA), promises data integrity. Cannabis labs should maintain data integrity as they receive, store, process, and report data collected during cannabis testing. Labs can maintain data integrity by deploying systems that support data integrity.

2. Adopt Effective Measures to Minimize Data Variability

With large volumes of data generated daily, cannabis testing labs find it challenging to prevent data variability when testing the same samples. Sometimes, human errors lead to altered data which drives data variability. Lab instruments come from different manufacturers and have different calibration standards, adding to data variation. Furthermore, different labs use different methods for analytical testing, leading to inter-laboratory variation in test results. Standardization of testing protocols, effective data management strategies, implementation of a quality management system, and compliance with good laboratory practice (GLP), ISO/IEC 17025 can reduce data variability in cannabis testing labs to a great extent.

3. Leverage Data to Identify and Analyze Operational Issues

Nowadays, labs mostly rely on automation to standardize their lab workflows. Automation helps labs eliminate manual errors and data inconsistencies, increase testing throughput, and reduce the turnaround time. Automation provides complete data traceability and helps track samples at any point in the sample life cycle without manual intervention. Furthermore, automation flags out-of-specification results and enables lab managers to take further actions. Additionally, audit trails track all data changes, who made the changes, and when. This helps maintain data integrity, identify process irregularities, initiate necessary corrective actions, and is crucial for third-party audits.

4. Visualize Data to Improve Efficiency

Nowadays, labs can generate an abundance of data from a single sample. Data visualization tools can help them pick the most important data from the pile of collected data. Visualizing data using graphs and pie charts provides a good interpretation of data. It not only helps optimize the sample testing process at the lab but also helps the producers of cannabis products produce more diverse and customized products for consumers. Easy interpretation of analytical data helps in quick and effective decision-making.

How Can a Cannabis LIMS Drive the Right Use of Data in Cannabis Labs?

Nowadays, labs rely on data management tools to manage large volumes of data and maintain efficiency and quality in the regulatory process. Labs need to practice sensible use of data to monitor lab performance, reduce data variability, and automate lab testing. A Laboratory Information Management System (LIMS) is a perfect tool for labs to help them make accurate use of data and digitize operational workflows.

A Cannabis LIMS can help testing labs support data integrity concepts, track deviations from standard procedures, and centrally store data to make business and reporting decisions.

A LIMS helps cannabis testing labs in the following ways:

Supports data integrity
Reduces data errors & ensures high-quality data
Minimizes data variability through process standardization
Provides complete traceability through lab data
Provides complete audit trail from sample accessioning to disposal, thus providing insights into the lab processes
Enhances result accuracy & lab efficiency
Generates graphs to visualize data
Streamlines QA/QC processes
Supports compliance with ISO/IEC 17025:2017, 21 CFR part 11, GLP, GMP, and other state and local regulations

Conclusion

Cannabis testing labs can proactively address their daily challenges by making the most out of their data. As the cannabis industry grows, it becomes essential for cannabis testing labs to have validated test methods, follow best practices, and comply with quality and safety standards. Sensible and digital data management can help labs meet demands both from consumers and regulators.

With a quality-centric approach, cannabis testing labs have started embracing the benefits of LIMS. A cannabis LIMS can help testing labs manage data better and guide them towards an easier path to compliance.