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T10: PSE4BioMedical and (Bio)Pharma -Including keynote by JNJ
Time:
Tuesday, 08/July/2025:
11:00am - 12:00pm
Chair: Dimitrios I. Gerogiorgis Co-chair: Satyajeet Bhonsale
Location:Zone 3 - Room E030
KU Leuven Ghent Technology Campus
Gebroeders De Smetstraat 1, 9000 Gent
Keynote by Geert Crassaerts (JNJ)
Session Abstract
Transforming Pharmaceutical Development: The Power of AI and Predictive Modeling
Presenters: Niels Vandervoort (Director digital transformation J&J) and Geert Craessaerts (Director Data&Process engineering)
In today's rapidly evolving pharmaceutical landscape, J&J is presented with exciting opportunities alongside exciting challenges. As our new molecules grow in complexity and the development cycles for new medicines are much shorter, it is clear that we must adapt our ways of developing new medicines to thrive in this dynamic environment. We will need a paradigm shift from iterative insights where we learn from doing to predictive insights in which we can anticipate product & process behavior at scale or in the patient. Therefore, digital transformation is not only a technological revolution; it is a business transformation in which we rethink how we develop our products and how digital can be an enabler and a catalyst.
By harnessing the power of new & advanced digital technologies like AI and by using the predictive power of advanced modelling; we will be able to leverage historical insights much better, and we can kick start our product & process design and start from a much better place of knowledge. Additionally, when we can generate the right insights faster through predictive models or by executing the right experiments through model-driven DOE and experimental iterations; we will be able to make better product & process design decisions much faster in the development lifecycle. Pharmaceutical companies typically make use of a wide range of predictive modelling technologies, including traditional methods based on mechanics, data-driven techniques, and hybrid models. We adopt our modelling technology based on what we know about the science, the physics and the availability of data. The presentation will highlight several examples from drug substance and drug product manufacturing, showing how these flexible modelling technologies help us with better and faster designing the processes. There is a great momentum for the broader usage of AI in pharmaceutical development given the support from authorities like FDA. However when entering the AI process modelling world, we should emphasize that transparency and explainability in AI systems are crucial for gaining trust from stakeholders and meeting regulatory requirements.
