Sathiyapriyan Pavithra, Mukherjee Shatanik, Vogel Thomas, Essen Lars-Oliver, Boerema David, Vey Martin, Kalina Uwe
Research & Development CSL Innovation GmbH Marburg Germany.
Department of Chemistry Philipps-Universität Marburg Marburg Germany.
Anal Sci Adv. 2025 May 29;6(1):e70013. doi: 10.1002/ansa.70013. eCollection 2025 Jun.
Protein-based therapeutics have revolutionized modern medicine, addressing complex diseases with unprecedented specificity and efficacy. The rising demand for biologics has driven the evolution of biomanufacturing practices to ensure consistent quality and operational efficiency. Traditional batch testing, with its inherent limitations, is being replaced by quality by design (QbD) frameworks and process analytical technology (PAT). PAT facilitates real-time monitoring and control by integrating advanced analytical tools and data-driven methodologies to optimize downstream processing (DSP). This review highlights the recent advancements in PAT tools, including spectroscopy, chromatography and biosensors. Spectroscopic techniques provide rapid, non-invasive measurements, while biosensors offer high specificity for monitoring critical quality attributes. Additionally, the integration of chemometric modelling and digital twins enables predictive analytics and enhances process control, paving the way for real-time release (RTR) of the product. Despite challenges in regulatory compliance and technology integration, innovations in automation and machine learning are bridging these gaps, accelerating the transition to intelligent manufacturing systems. This article provides a comprehensive evaluation of emerging analytical technologies and strategic insights into their integration, aiming to support the biopharmaceutical industry's shift towards robust, continuous and adaptive manufacturing paradigms.
基于蛋白质的疗法彻底改变了现代医学,以前所未有的特异性和疗效应对复杂疾病。对生物制品不断增长的需求推动了生物制造实践的发展,以确保一致的质量和运营效率。传统的批次检测因其固有的局限性,正被质量源于设计(QbD)框架和过程分析技术(PAT)所取代。PAT通过整合先进的分析工具和数据驱动的方法来优化下游加工(DSP),从而实现实时监测和控制。本综述重点介绍了PAT工具的最新进展,包括光谱学、色谱法和生物传感器。光谱技术提供快速、非侵入性测量,而生物传感器对监测关键质量属性具有高特异性。此外,化学计量学建模和数字孪生的整合实现了预测分析并增强了过程控制,为产品的实时放行(RTR)铺平了道路。尽管在法规合规和技术整合方面存在挑战,但自动化和机器学习方面的创新正在弥合这些差距,加速向智能制造系统的转变。本文对新兴分析技术进行了全面评估,并对其整合提供了战略见解,旨在支持生物制药行业向稳健、连续和适应性制造模式的转变。