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20 Pharmaceutical Technology ® Quality and Regulatory Sourcebook March eBook 2023 PharmTech.com Qualit y Control States, combined with a 14% shor tage in pharma digital talent (2), increases pressure on lab managers to find the right people who can keep pace with the demands of a modern laboratory. The rise in remote working also plays a role, as labs are also tasked with the adoption of f lexible, hybrid approaches to work. All of these challenges contribute to increased pres- sure on technicians, as these members of the lab are increasingly being asked to take on more work and deliver results faster than ever before, complicating training procedures and requiring more supervision. 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 workf lows, it also comes with a learning curve and new burdens may be placed on its users. Pharma companies must deter m ine t he ef fect iveness of tech nologica 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 sample preparation, sample analysis, data collection and anal- ysis, and reporting. Automating these tasks can min- imize variability, improve traceability, simplify work- flows, and streamline recordkeeping. That's particularly the case for QC analysis, which lends itself more natu- rally to automation because of the routine and repeat- able nature of the work. An analytical electronic labora- tory notebook (ELN), for example, can support 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 accessed scien- tific information, as well as providing integration with a multitude of applications. Automated systems can be programmed to follow specific protocols and procedures, ensuring consistent 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, automation can be used to improve the traceability, consistency, and documenta- tion of laboratory processes, as automated systems can generate detailed records of all actions taken and results obtained and reduce the risk of variability between dif- ferent users. This reduction in variability can be partic- ularly 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 effectiveness. 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 com- puter science that focuses on the use of advanced algo- rithms and data analysis techniques to extract useful information from complex datasets. This field uses ad- vanced technologies, such as artificial intelligence (AI) and machine learning, for data analysis and interpreta- tion to improve efficiency in the laboratory. Intelligent informatics can be used to support QC laboratories by analyzing data from various sources, such as manufac- turing processes, product testing, and customer feed- back. Intelligent informatics also provides detailed health checks for a system, including insights into any potential issues or areas that need improvement. Intelligent informatics can significantly improve effi- ciency in a pharmaceutical laboratory by enabling faster and more accurate data analysis, automating routine tasks, and predicting and proactively solving problems. For example, algorithms can be used to identify patterns and trends in data that may indicate potential quality is- sues 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 occurred. This information can then be used to improve manufacturing processes and detect problems before they occur. Additionally, intelligent in- formatics can monitor the quality of drugs throughout Intelligent informatics can also support the compliance audit trail with a complete record of all activities and decisions.