Introduction:
This article discusses the transition in bioprocessing from quality by testing (QbT) to quality by design (QbD) and the challenges and advancements involved in this shift. The authors highlight the benefits of QbD, such as increased operational efficiencies, reduced market time, and product consistency. They also emphasize the role of artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DTs) in optimizing various aspects of quality assurance.
- QbD is a systematic approach to quality in process development, integrating quality into process design and control.
- QbD enables increased operational efficiencies, reduced market time, and product consistency.
- AI, IoT, and DTs are crucial for real-time data analysis, predictive modeling, and process optimization in QbD implementation.
- Bioprocessors face challenges in implementing digital twins (DTs), such as system integration, data security, and hardware-software compatibility.
- Advancements in noninvasive analytical techniques, sensor adaptability, regulatory compliance, and user-friendly interfaces are needed for better data collection and process monitoring in QbD.
Conclusion:
The transition from QbT to QbD in bioprocessing is underway and offers significant benefits. However, challenges related to implementing DTs and improving data collection and process monitoring need to be addressed. Advancements in technology and analytical techniques are essential for the successful implementation of QbD in bioprocessing.
