In June 2024, Thakur, G., et al. presented a "cyber-physical production system (CPPS) for the continuous manufacturing of mAbs" that also provides end-to-end automation infrastructure for data collection. Their real-time analysis also includes a series of interconnected surge tanks for successful steady-state operation of over 55 hr which includes downstream unit operations that are tracked using multivariate approaches. The authors state that their accomplishment is "the first end-to-end CPPS for the continuous manufacturing of mAbs" . Paper 11: 76 words, Time: 6 minutes 44.03 seconds In July 2024, Kunzelmann, M., et al. detailed an in-silico approach "to establish multivariate acceptable ranges" between process parameters. The authors' layered approach is based on risk assessment and statistical models which are established from prior knowledge and data. Their study is advantageous in that no additional wet-lab experiments are required for building statistical models and subsequent process control. The authors also include a case study for a mAb polishing application . Paper 12: 71 words, Time: 6 minutes 32.19 seconds In January 2024, Khuat, T., et al. published a 59 page overview of machine learning applications related to antibody development, manufacturing, and formulation. These current trends, challenges, and opportunities are now more significant due to an increase in real-time monitoring of large-scale production data. Process variables and quality attributes are of high importance for bioproduct design multidimensionality, which are addressed in this comprehensive review of machine learning solutions . Paper 13: 69 words, Time: 7 minutes 15.88 seconds In January 2024, Eslami, T., et al. published a comprehensive review regarding Model Predictive Control (MPC) that "addresses the complexity of modern biopharmaceutical production processes" by optimizing operations and ensuring consistent product yields. The review covers various MPC applications ranging "from robust control to stochastic model predictive control", which assists the field of biotechnology and process engineers . Paper 14: 61 words, Time: 4 minutes 58.21 seconds In August 2024, Agarwal, P., et al. presented a novel experimentally validated model to "explain the perfusion dynamics under different operating conditions and scales for process optimization." The model works by accurately describing CHO cell culture growth dynamics, while a neural networks explains mAb production to optimize media exchange rates. High accuracy was reported across six perfusion runs, including a 50% increase in volumetric productivity. In addition, two 5L experiments validated large bioreactor capability . Paper 15: 77 words, Time: 7 minutes 49.12 seconds In March 2024, Wang, Y., et al. presented a general framework that combined model based robust optimization with optimal experiment design to improve optimization. Their mechanistic model structure reduced uncertainty related to optimization to maximize the potential for improving the worst-case performance "by balancing between exploration and exploitation." Their efficient and robust optimization framework was then illustrated on "a realistic simulation case study of medium optimization with Chinese hamster ovary cell cultivation" consisting of 23 extracellular metabolites and 126 reactions . Paper 16: 85 words, Time: 7 minutes 36.06 seconds