Data-Rich Experimentation and Kinetic Modeling
Accelerating ADC Process Development
Program Overview
- The Power of DRE: Learn how just a few experiments can yield hundreds of data points, providing a "reaction fingerprint" far superior to traditional sampling.
- Mechanistic Insight: Discover how plotting conversion vs. impurity concentration identified that elimination impurities were derived from the product, not the starting material.
- Overcoming Vendor Variability: See how kinetic modeling identified "hidden" catalytic impurities in different starting material batches.
- Predictive Scale-Up: Observe the seamless transition of a model built in a 100 mL EasyMax to a 5 L reactor, and eventually a 60 L manufacturing scale.
- In Silico Problem Solving: Learn how to use models to adjust parameters (like base equivalency) to fix crystallization fouling without running new physical experiments.
A typical reaction has 4 or more important variables, and those variables can have discrete or continuous values (at least 103 to 104 possible reaction conditions). Those experimental spaces cannot be covered empirically due to time and material constraints. This presentation describes how researchers employed kinetic modeling to quickly develop reactions conditions as well as adopt quickly to changing external factors.
In this technical briefing, Jan Riedel of Pfizer shares how his lab overcame a critical scale-up failure—where a 2x scale-up unexpectedly ballooned from 60 to 140 hours—by using a model-driven approach to rescue the project timeline in under one week.
About the Presenter
Jan Riedel, Ph.D.
Pfizer
Jan Riedel was born and raised in Germany. He received his undergraduate and master’s degree in chemistry from the University of Dortmund. Jan received his Ph.D. in Organic Chemistry doing organometallic method development from the University of California, Irvine. From 2019-2023, he worked at Seagen as Process Chemist in the development of drug linkers for ADC’s. Since 2023, Jan works at Pfizer as Process Chemist focusing on more efficient and flexible ways to do process development using Data-Rich-Experimentation (DRE) and DRE enabled technologies like Kinetic Modeling.
