Insights from Pharma 2025: Harnessing AI: Revolutionizing drug discovery and early disease detection
Here, we share the key highlights from the conversation between Jörg Schüttrumpf, Chief Scientific Innovation Officer at Grifols, and Poka Cui, Director at Vintura, part of Cencora, on the transformative role of artificial intelligence (AI) in pharmaceuticals.
Understanding the power of AI in Research and Development (R&D)
Key applications of AI
- Real-world data utilization: Grifols has harnessed an extensive database of over 100 million samples collected from 5 million plasma donors over 10 to 15 years. By integrating this data with real-world evidence, Grifols can identify disease patterns and predict health outcomes. Schüttrumpf shared a notable example ofcollaboration with the Michael J. Fox Foundation, where they are using AI to explore biomarkers that can indicate Parkinson’s disease years before diagnosis.1
- Precision medicine: AI empowers researchers to detect disease markers at earlier stages, shifting the focus from treatment to prevention. Schüttrumpf noted that understanding these biomarkers facilitates the development of targeted therapies capable of altering disease trajectories, ultimately improving patient outcomes. “If you can detect so early, you can really change the course of the disease over the lifetime,” he explained, highlighting a critical advancement in the pursuit of precision medicine.
- Optimizing clinical trials: The integration of AI in clinical trials aids in identifying the right patient populations and optimizing trial designs. Schüttrumpf explained how real-world data can inform inclusion and exclusion criteria, ensuring that clinical trials are more effective and aligned with patient needs.
Challenges and considerations
- Data privacy and integration: The need for robust data governance is paramount. Schüttrumpf pointed out the importance of anonymizing patient data while ensuring that the integration of various data sources does not compromise privacy. He noted that navigating data privacy remains a significant challenge for the industry as a whole.
- Understanding AI algorithms: The complexity of AI technology can make it a "black box" for many organizations. Schüttrumpf emphasized the need for transparency in AI algorithms to avoid biases and ensure accurate outcomes.
- Collaboration across the ecosystem: Schüttrumpf underscored the necessity for collaboration among pharmaceutical companies, regulatory bodies, and other stakeholders to fully leverage AI's capabilities. “It’s crucial that we reach out, partner with private and public organizations”, he added, highlighting the importance of collective effort.
Future directions in AI and pharma
The pharmaceutical landscape is rapidly being reshaped by AI, offering vast opportunities for improving drug discovery and disease detection. By embracing AI technologies and fostering collaboration across the industry, stakeholders can drive innovation and significantly enhance patient outcomes. This discussion underscored a collective commitment to leveraging AI in the pursuit of smarter, more effective healthcare solutions.

Watch the full conversation
The contents of this piece contain marketing statements and do not include legal advice.
Source:
1: https://www.grifols.com/en/view-news/-/news/grifols-pioneers-high-tech-analysis-of-plasma-bank-to-detect-early-signs-of-parkinson-s-disease
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