Process design

Design of experiments

This material is reproduced from J. D. Moseley, Design of Experiments (DoE) for Greener Medicinal Chemistry, in Green and Sustainable Medicinal Chemistry: Methods, Tools and Strategies for the 21st Century Pharmaceutical Industry, L. Summerton, H. F. Sneddon, L. C. Jones and J. H. Clark, Royal Society of Chemistry, Cambridge, UK, 2016, ch. 11, pp. 116-128.. It is copyright to the Royal Society of Chemistry (RSC) and is reproduced here with their express permission. If you wish to reproduce it elsewhere you must obtain similar permission from the RSC.

 

Design of Experiments (DoE) is a structured and efficient approach to experimentation which employs statistical techniques to investigate potentially significant factors and determine their cause-and-effect relationship on the outcome of an experiment.[1][2][3] Where a relationship between the experimental parameters (factors) and the results exists, the correlation can be detected and quantified. Hence DoE can be applied to any process for which the inputs (factors or parameters) can be controlled, and the outputs (results) can be measured in a statistically valid manner.  DoE can be employed to design optimal and robust processes.  

Due to this, DoE has been widely used for many years and across many industries, sectors and disciplines including the pharmaceutical industry. In the pharma industry, it is most frequently used in the development and scale-up of new processes for the manufacture of intermediates for potential new drugs (once a decision has been made on the synthetic route).[4][5] Often it is also used in the formulation processes required for the final active pharmaceutical ingredient (API).[6] After a new drug is successfully launched, it can be employed in the initial manufacturing processes to further optimise yield and quality and reduce manufacturing costs (although these studies are rarely published).

This module on Design of Experiments has been prepared by Sandrine Olazabal from GlaxoSmithKline.

Learning Objectives:

By the end of this module you should:[+Fig]

  • Be familiar with the steps to follow when designing an experimental design;
  • Understand how to identify main effects and interaction effects within a process;
  • Understand how to DoE can be employed to discern settings to deliver acceptable and optimum performance;
  • Be aware of how to control variation in a DoE execution;
  • Be aware of the different types of experimental design approaches and their associated advantages and limitations.
  1. G. E. P. Box, W. G. Hunter and J. S. Hunter, Statistics for experimenters: an introduction to design, data analysis, and model building,Wiley, 1978.
  2. R. L. Tranter, Design and Analysis in Chemical Research,CRC Press, 2000.
  3. R. Carlson, Design and Optimization in Organic Synthesis,Elsevier, 1991.
  4. M. R. Owen, C. Luscombe, Lai, Godbert, D. L. Crookes and D. Emiabata-Smith, Efficiency by Design: Optimisation in Process Research, Org. Process Res. Dev., 2001, 5, 308–323.
  5. J. T. Kuethe, D. M. Tellers and N. Weissman Steven A and Yasuda, Development of a Sequential Tetrahydropyran and Tertiary Butyl Deprotection: High-Throughput Experimentation, Mechanistic Analysis, and DOE Optimization, Org. Process Res. Dev., 2009, 13, 471–477.
  6. S. Sjövall, L. Hansen and B. Granquist, Using DOE to Achieve Reliable Drug Administration: A Case Study, Org. Process Res. Dev., 2004, 8, 802–807.