RESUMO
The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types - mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector.