White Paper

Effective Design of Experiment Studies

White Paper

DoE For Innovative Chemical Development in the Lab

Video: Improve DoE

Design of Experiment studies facilitate fast and efficient discovery and development of new chemical entities, which was an ongoing challenge. Organic chemists and engineers apply various techniques and methods to improve synthetic pathways, so that they become more effective and minimize the consumption of precious starting material. Advancements in synthesis methods and tools have made improvements possible.

In synthetic organic chemistry labs, obstacles or constraints still surface and need to be resolved. A chemical reaction is affected by multiple parameters, making it difficult to find optimal parameters quickly and efficiently. Chemistry can be influenced by parameters including concentration, addition rate, temperature, the solvent, catalyst and pH. Frequently, parameters influence each other. Reactions may also be affected by the stirrer type and speed, which impact the mass transfer and further influence the result of the experiment.

Rather than adopting a "trial-and-error" approach, where each parameter is examined individually and interactions between them cannot easily be detected, scientists can apply the Design of Experiments (DoE) methodology in the synthetic organic chemistry lab. A white paper describes the DoE approach and how it is used to identify the relationship between parameters, defining optimal settings at which the response variable (including yield, selectivity, impurity level) reaches the ideal value.

Two Design of Experiment (DoE) case studies are presented:

  • Researchers at the Zurich University of Applied Sciences (ZHAW) investigated styrene polymerization to:
    • Understand the performance and accuracy of temperature control, a pre-requisite for reliable Design of Experiment (DoE) studies
    • Track progress and repeatability
  • Scientists at Lonza SA explored a Peptide Synthesis reaction to:
    • Identify the impact of reaction parameters
    • Provide a better understanding of the cause and effect of process variability
    • Shorten chemical development cycles
    • Support the Quality-by-Design (QbD) approach