🧪 Currently, automation is not widely used in the process development lab, but it has been successful in drug discovery and production plants.
🔬 Automation combined with digitalization can provide better data management and insights into bioprocesses.
👥 Collaboration with technology suppliers and a solid data infrastructure are essential for successful automation projects.
🤔 Challenges include the lack of regulatory standards for the development and deployment of AI in medical use cases.
👨🔬 AI has potential applications in process development, such as cell therapy manufacturing and production scheduling.
📚 AI is already used to process lab images and make process decisions.
💡 Automation and AI can greatly benefit process development and lead to more efficient drug manufacturing.
Introduction:
The use of automation in drug discovery and production has been well-established, but its application in process development is still limited. However, according to Robert Schmitt, a professor at RWTH Aachen University, automation in process development has the potential to improve efficiency and accelerate the optimization of processes. It can enable parallelization and higher throughput of experiments, leading to faster and more reliable results. Additionally, the combination of automation with digitalization can improve data management and provide better insights into process data. Despite the potential benefits, there are challenges in implementing automation in process development, including the need for close collaboration with technology suppliers and the establishment of a solid data infrastructure. Furthermore, the integration of artificial intelligence (AI) alongside automation can further enhance process development, particularly in areas like cell therapy manufacturing. However, the lack of regulatory standards and guidelines for AI deployment in medical applications remains a major hurdle.
- Automation in process development can enhance efficiency and accelerate the optimization of processes.
- The combination of automation with digitalization improves data management and provides better insights into process data.
- Close collaboration with technology suppliers and a solid data infrastructure are vital for successful implementation of automation in process development.
- AI has the potential to further enhance process development, particularly in cell therapy manufacturing and resource scheduling.
- The lack of regulatory standards and guidelines for AI deployment in medical applications is a major challenge.
Conclusion:
The use of automation in process development has the potential to revolutionize the field by improving efficiency and accelerating the optimization of processes. By enabling parallelization and higher throughput of experiments, automation can lead to faster and more reliable results. Additionally, the integration of AI can further enhance process development, particularly in complex areas like cell therapy manufacturing. However, challenges such as close collaboration with suppliers and establishing a robust data infrastructure need to be addressed. Furthermore, the development of regulatory standards and guidelines for AI deployment in medical applications is crucial for widespread acceptance and trustworthiness. Overall, the adoption of automation and AI in process development can bring significant advancements to the pharmaceutical industry.
