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www.biopharminternational.com Quality and Regulatory Sourcebook March eBook 2023 BioPharm International ® 11 Qualit y Control These changes are accelerating the need for new innovations. One solution is the use of intelligent in- formatics in the pharmaceutical quality control (QC) laboratory to improve the quality and safety of drugs and reduce the risk of costly errors and recalls. How- ever, while technology can simplify many workflows, it also comes with a learning curve and new burdens may be placed on its users. Pharma companies must determ ine t he ef fectiveness of technologica l ad- vancements and the potential challenges they may introduce in an environment like pharmaceutical quality assurance/quality control (QA/QC). Current use of automation in QC In QC laboratories, automation is already used to per- form a wide range of routine tasks, including sam- ple preparation, sample analysis, data collection and analysis, and reporting. Automating these tasks can minimize variability, improve traceability, simplify workflows, and streamline recordkeeping. That's par- ticularly the case for QC analysis, which lends itself more naturally to automation because of the routine and repeatable nature of the work. An analytical elec- tronic laboratory notebook (ELN), for example, can sup- port existing R&D and process-driven quality control workflows, as well as optimizing the use of information collected in a data repository. Automated electronic repository and data management systems can provide intelligently aggregated, reliably stored, and easily ac- cessed scientific information, as well as providing in- tegration with a multitude of applications. Automated systems can be programmed to follow specific protocols and procedures, ensuring consis- tent and reproducible results. For example, automated liquid handling systems can quickly and accurately transfer and dispense precise volumes of samples and reagents, reducing the time and labor required to perform these tasks manually. In addition, auto- mation can be used to improve the traceability, con- sistency, and documentation of laboratory processes, as automated systems can generate detailed records of all actions taken and results obtained and reduce the risk of variability between different users. This reduction in variability can be particularly important in QC laboratories, where the accuracy and integrity of data are critical. However, automation should lessen the burden of training, not just reduce mundane tasks. Usability of a system can significantly impact its efficiency and ef- fectiveness. If a system is difficult to navigate, it may take longer for users to learn how to operate it, and they may be more likely to make mistakes or errors. This increases the likelihood of mistakes, which can lead to delays or errors in laboratory processes and may result in inaccurate or unreliable results. While automation can reduce the risk of human error, it is still important for operators to understand the limita- tions of the system and to follow proper procedures to ensure accurate and reliable results (3). Adoption of smart software Current automation tools are being enriched with the emergence of intelligent informatics, a branch of computer science that focuses on the use of advanced algorithms and data analysis techniques to extract useful information from complex datasets. This field uses advanced technologies, such as artificial intel- ligence (AI) and machine learning, for data analysis and interpretation to improve efficiency in the lab- oratory. Intelligent informatics can be used to sup- port QC laboratories by analyzing data from various sources, such as manufacturing processes, product testing, and customer feedback. Intelligent informat- ics also provides detailed health checks for a system, including insights into any potential issues or areas that need improvement. Intelligent informatics can significantly improve efficiency in a pharmaceutical laboratory by enabling faster and more accurate data analysis, automating routine tasks, and predicting and proactively solv- ing problems. For example, algorithms can be used to identify patterns and trends in data that may in- dicate potential quality issues with a drug. Real-time error information allows for the identification and resolution of issues as they arise, rather than waiting until the end of a process or after a problem has oc- curred. This information can then be used to improve manufacturing processes and detect problems before they occur. Additionally, intelligent informatics can monitor the quality of drugs throughout the supply chain, ensuring that they meet the required stan- dards at every stage of production. Intelligent informatics can also support the compli- ance audit trail with a complete record of all activities and decisions. This support may include documen- tation of training and qualifications of personnel, as well as records of procedures, protocols, and stan- dard operating procedures (SOPs). In addition, the Intelligent informatics can also support the compliance audit trail with a complete record of all activities and decisions.