Forward-looking biomanufacturers continue to explore innovative strategies and new technologies to modernize their facilities. Leading-edge technologies in the Industry 4.0 evolution include smart sensors, predictive analytics, internet of things (IoT), cloud computing, digital twin simulations, and data lakes. In the featured report, Buytaert-Hoefen focuses on the key elements of data integrity. With assistance from the BioPhorum Operations Group, Beri et al. present one of the first discussions on the applications of robotics in bioprocessing. And Bower provides a new analysis of control chart limits and autocorrelation of data for continued process verification
A Harmonized Approach to Data Integrity
Data integrity is achievable when the collection of data is complete, consistent and accurate. Failure to maintain data integrity compromises a company’s ability to demonstrate the safety and efficacy of its products. The recent escalation of serious regulatory actions related to data integrity violations has prompted the need to assess data integrity compliance and implement systems designed to guarantee it. Comprehensive measures must be taken to ensure data is attributable, legible, contemporaneous, original, and accurate.
Opportunities for Modern Robotics in Biologics Manufacturing
Rajesh Beri, Dave Wolton, and Carl-Helmut Coulon
Biomanufacturing facilities and support operations requires an excessive amount of manual labor and manual interventions resulting in high labor costs and total cost to supply. The authors introduce potential applications for robotics in biomanufacturing, the availability of robotic systems, and the incentives for the robotics supplier industry to consider adapting existing or developing entirely new solutions for the biomanufacturing industry. They also cover key challenges in implementing mobile robotics in Biomanufacturing. To the knowledge of the authors, this is one of the first publications covering robotic applications for the biomanufacturing industry. A team of industry experts coordinated by the BioPhorum Operations Group (BioPhorum, London, UK) assisted with critical discussions and data gathering for this introductory article.
Control Chart Limit Determination for Continued Process Verification with Autocorrelated Data
Continued process verification (CPV) is performed during stage 3 of the process validation lifecycle, to monitor critical process parameters, critical quality attributes, and other attributes to demonstrate an ongoing state of control over the commercial manufacturing process. In advanced biomanufacturing processes, positive autocorrelation is prevalent in the manufacturing data used for monitoring biopharmaceutical product. The author describes the prevalence of positively autocorrelated data for CPV activities. He provides a simulation strategy to determine the unbiasing constant for a given sample size and time series model and recommends the minimum number of batches and design of control chart for individual results.