Andresa Kunov-Kruse
Specialist Novo Nordisk
Andreas Kunov-Kruse is a Specialist in Process Analytical Technology for Injectable Finished Products at Novo Nordisk, a global healthcare leader focused on innovative treatments for chronic diseases such as diabetes and obesity. In his role, he supports the integration of advanced analytical technologies into manufacturing processes to enhance product quality, process understanding and operational efficiency. Andreas works at the intersection of science and production, contributing to robust, data-driven manufacturing strategies for complex injectable therapies. Passionate about innovation and continuous improvement, he plays a key role in helping Novo Nordisk deliver high-quality medicines to patients worldwide.
Seminars
This is a chance to reflect on the learnings from the day and discuss with the room what actionable takeaways you want to bring to your organization. Topics include:
- Demonstrating ROI & Commercial Impact Across Process Monitoring to Enhance Implementation
- Enhancing Adoption & Implementation of Tools in the Real World
- Empowering Data Management Systems to Make the Right Manufacturing Decisions Faster
- Leveraging GMP-Compliant AI & Data in Process Monitoring
This interactive workshop moves beyond theory to explore how real‑time process monitoring and predictive control are being practically applied across pharmaceutical and biotech development and CMO/CDMOs. Through real‑world case studies and facilitated group discussions, industry experts will demonstrate how sensors, PAT, and advanced analytics are being used to surface actionable insight earlier; when it matters most.
Learn how to detect deviations earlier, improve decision‑making, and reduce batch failure risk, while overcoming the organizational, technical, and operational barriers to adoption. Designed to be highly participatory, this workshop will equip attendees with concrete learnings they can take back to their development labs and manufacturing sites, thus helping turn real‑time data into predictive, process‑ready control strategies.
Industry experts will cover:
- Applying machine learning models and digitized process development data, to identify edge case scenarios and predict potential impacts on critical quality attributes in real time
- Using models as decision support tools to enhance confidence in PARs, improve anomaly detection, and support batch disposition while maintaining established quality and investigation processes
- Addressing key challenges in validating ML based approaches, establishing governance frameworks, and demonstrating commercial value
- Determining when process monitoring and PAT integration meaningfully enhances process understanding, supports robust control strategies, and justifies investment
- Implementing data collection and monitoring tools to provide a holistic view of aseptic processes, enabling stronger decision making, improved control, and long-term manufacturing performance
