How To Optimize Peptide Synthesis

This is the second blog post in a 2 part series where I discuss the real-time monitoring of bio-based chemical synthesis.
Peptides are organic compounds involving chains of amino acids, and – as the building blocks of proteins – peptides are typically much more complex than the compounds involved in more traditional organic synthesis. Solution phase synthesis of peptides, as a result, is a relatively complex process – and the characterization and optimization of peptide synthesis is often a time-consuming and costly procedure. Due to the number of synthesis steps typically involved, maximizing the yield and selectivity of each step in a peptide synthesis process is critical to maintaining an economical yield of the final product.

Peptide synthesis is naturally subject to a wide range of influences – such as the reaction temperature, solvent, catalyst, as well as concentrations of the substrate and reagent. As a result, there is significant potential for variability in target properties such as the product composition, purity, yield and selectivity. With the overall complexity inherent in the design and optimization of peptide synthesis, sophisticated concepts and state-of-the-art tools are necessary as an essential element of peptide synthesis.

Rather than adopting a trial and error approach, statistical Design of Experiments (DoE) is generally applied to help deal with the complexity and potential interactions of operating variables. In order for development based on the Design of Experiments concept to be successful, it is essential that experiments are reproducibly performed within a framework of precise and accurate control of experimental operating conditions.

The requirements for effective experiment setup and execution demand a high degree of flexibility, precision and reproducibility. A partially automated synthesis tool, such as EasyMax™, provides this capability in an elegant and easy to use design. EasyMax™ supports the execution of solution phase peptide syntheses within a precisely controlled framework. The reactor system is operated with an easy-to-use touchpad, and features two fully independent reactors that can be equipped with dosing units, various stirrer types, as well as analytical measuring probes such as pH measurement, Mid IR or particle characterization. Critical process parameters and changes in operating conditions are recorded in detail for immediate viewing and for further in-depth analysis.

Statstical Design of Experiments (DoE)Conclusions

  • Statistical Design of Experiments (DoE) has proven to be an effective method of identifying relevant parameters for optimization of peptide synthesis, highlighting the relative influence of temperature and reagent concentrations on reaction yield and selectivity.
  • This systematic approach can minimize the number of experiments required.
  • A kinetic model was created from the experimental data, allowing prediction of impurities and design of a reaction process to minimize their occurrence.
  • EasyMax™ enabled the complete execution and analysis of the Design of Experiments (DoE) in a highly efficient manner, with precise control of reaction conditions to ensure experimental reproducibility, data integrity and high quality results.

See this and more recently reported applications in fermentation and bioprocessing in the new white paper: Process Analytical Technology (PAT) for Biotech.