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Agnes Shanley is senior editor of Pharmaceutical Technology.
Tools help lab scientists capture and use more data and work to ensure data integrity.
Like many crucial regulations, good laboratory practices (GLPs) were enacted in 1979 after FDA observers found serious problems in documentation, training, and data integrity at a number of US research labs (1). Decades later, regulators still find deficiencies in the way that some companies’ labs approach data integrity, training, and standard operating procedures (SOPs). Another major problem is reproducibility. According to the Global Biological Standards Institute (GBSI), 50% of published preclinical research cannot be reproduced, a problem that results in product development delays and wastes $28 billion/year in the US alone (2). Culprits were found to be biological reagents and reference materials, study data, and lab protocols.
A number of tools are being developed to help lab scientists capture and use more data, for example, LabStep, an interactive digital platform designed to help scientists get around some of the deficiencies of electronic lab notebooks (ELNs) and refer directly to protocols, SOPs, and other important data (3).
LabTwin introduced a new voice-activated lab assistant at BIO 2019 in Philadelphia. Combining artificial intelligence, voice recognition, and machine language, the hands-free device allows researchers to document steps taken and save explicit details that cannot currently be saved in ELNs (4).
Ultimately, compliance depends on following best practices. Stuart Jones, regulatory quality assurance professional in good laboratory practice (RQAP-GLP) and director of quality assurance at PPD Laboratories’ Bioanalytical Laboratory shared recommendations with Pharmaceutical Technology.
PharmTech: What are GLP’s biggest challenges?
Jones: Because we work in such a regulated environment, a seemingly minor matter can have a significant impact on quality. As such, training is an important best practice, from the time of hire, to retraining when a deviation occurs. Annual refresher training as well as specific group remedial training also should be provided when needed. Meanwhile, the use of automated or electronic systems, such as ELNs, can be especially beneficial in maintaining the most accurate documentation.
PharmTech: How do you recommend that companies tackle training?
Jones: Initial training, especially with newer employees, can be done through reading, lecture, and/or some type of knowledge or learning assessment, but the best results occur when that theoretical work is followed up and supplemented by hands-on training. This is accomplished most effectively by teaming new employees with experienced staff using training goals established within a predetermined curriculum. Some measure of refresher training should be required on at least an annual basis and it should be consistent across all experience levels. Metrics around unplanned protocol and SOP deviations, as well as human error, should be used to gauge the effectiveness of training plans.
PharmTech: What best practices do you recommend to make data less siloed and more accessible to those who may need it (e.g., on cross functional teams working at the same company or facility?)
Jones: One of the best ways to establish a more cross-functional approach and enhance data accessibility is to use one system across all sites. If one across-the-board system is not a possibility, then the multiple systems must be able to work in tandem. Data portals and SharePoint sites also can be utilized to securely share information on a real-time basis.
PharmTech: Reproducibility is a problem for preclinical research. Is that also the case for quality control labs? What do you recommend?
Jones: We have found that, after research and development of the method by our scientists, it is important to involve the sample analysis team in performing some, if not all, of the validation experiments, with technical assistance provided, as needed, by the R&D scientists who developed the method. This approach allows for a shared collaboration between the research and production teams and continues into sample analysis to ensure reproducible results from the developed and validated method. Best practices include following the proper bioanalytical method validation guidances, the bridging of critical reagents, analyst method qualification, and scientific expertise/knowledge of the assay, as well as the use of incurred sample reproducibility testing.
1. H. Danan, Pharmaceutical Technology 15(6) (2003).
2. L.Freedman et al, BioPharm International 28(10) pp 14-21 (2015).
3. Lab Twin, “Voice Activated Laboratory Assistant to Launch at BIO 2019,” Press Release, May 21, 2019.
4 J. Gould, “How Technology Can Help Solve Science’s Reproducibility Crisis,” Podcast, nature.com, April 25, 2019.
Supplement: Outsourcing Resources
When referring to this article, please cite it as A. Shanley, “GLPs: Better Data Access Needed to Improve Compliance," Pharmaceutical Technology Outsourcing Resources Supplement (August 2019).