Peptide Synthesis is naturally subject to a wide range of influences, such as reaction temperature, solvent, catalyst, as well as concentrations of the substrate and reagent. As a result, the target output variables such as the product composition, purity, yield or stereospecificity may vary in wide range.
Rather than adopting a trial and error approach, whereby each parameter is examined on an individual basis and interactions between these parameters cannot be easily detected, in today's industry the statistical Design of Experiments (DoE) is generally applied. In order for the development based on the DoE concept to be regarded as a success, it is essential that any experiments are performed within an accurately controlled framework under accurately maintained and reproducible conditions, thus enabling the target output variable (e.g. selectivity or yield) to reliably achieve its optimum value.
The requirements placed on the experiment setup and execution is therefore high and demands a high degree of flexibility, precision and reproducibility.
Guest presenter - Didier Monnaie, Ph.D.
Our guest speaker, Didier Monnaie, presents a highly complex application that illustrates the use of statistical methods together with a synthesis workstation. Didier Monnaie, Ph.D. is a project manager at Lonza Braine, a company specializing in R&D and custom manufacturing of therapeutic peptides. For the past ten years, he has been in charge of research and technology projects, including process analytical technologies (PAT). As a black belt six sigma, he is now in charge of process improvements, statistical data analysis and the implementation of the Quality by Design (QbD) initiative in the R&D department of Lonza Braine.
Chemical synthesis, Design of Experiments, DoE, peptides, peptide synthesis, Quality by Design, QbD, biology, automation, productivity increase, reproducibility, Didier Monnaie, Lonza, Process Analytical Technology, PAT, chemical development
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