FULYA Akpinar Singh
Senior Director Genmab
Fulya is Senior Director at Genmab, where she leads the application of quantitative clinical pharmacology and model-informed approaches to support drug development across innovative therapeutics. With prior leadership roles at Bristol Myers Squibb, she brings extensive expertise in quantitative systems pharmacology, translational modelling, and clinical pharmacology, leveraging mechanistic and data-driven insights to inform decision-making from preclinical research through clinical development.
Seminars
As modelling capabilities continue to expand, many organisations are now operating multiple modelling approaches in parallel, including pharmacometrics, QSP, machine learning, and AI enabled analytics. Yet the greatest value is often realised not from any individual model, but from understanding how these approaches can work together to answer development questions more effectively. This interactive workshop explores how organisations are building integrated modelling strategies that connect mechanistic understanding, quantitative prediction, and data-driven insights to support decisions across discovery and development.
This workshop will gather experts to discuss:
- Understand where pharmacometrics, QSP, machine learning, and AI each provide unique value across the development lifecycle, and where overlap can create duplication or inefficiency
- Explore practical frameworks for connecting modelling approaches, including how outputs from one model can inform, refine, or validate another
- Examine how AI and machine learning can be used to incorporate complex data sources such as imaging, genomics, transcriptomics, proteomics, and real-world datasets into modelling workflows that were previously constrained by data complexity
- Review case studies demonstrating how integrated modelling strategies improved dose selection, patient stratification, biomarker identification, and clinical trial design
- Discuss the challenges of transparency, validation, and regulatory acceptance when combining mechanistic and AI-enabled approaches
