Drug manufacturers continually look for ways to improve the quality, efficiency and cost-effectiveness of their processes while maintaining robustness and compliance. Numerous factors can influence the performance of a given process, and continuous improvement is a critical aspect of modern API manufacture. Traditionally, researchers have examined the effects of each variable in an experiment by changing one factor at a time, e.g. changing the temperature or concentration of a single chemical, while holding the other variables constant. More recently, there has been growing interest in the use of statistical modelling and computer simulation of experiments that enable rapid, low-cost predictive models for process improvements as well as providing valuable data about interactions among process variables.
Ninety years ago, Sir Ronald Fisher proposed that ‘large and complex experiments have a much higher efficiency than simple ones’1 and he demonstrated the design of an agricultural experiment that would simultaneously examine the effects of three different parameters on crop yield. He argued that this approach would not only be more efficient in identifying the effects of different soil treatments on crops – involving substantially less time, labour and land than ‘single question’ methods – but that it would also allow for analysis of all possible interactions among the experimental parameters, as well as delivering more accurate results. This early paper was an important advance that described the first DoE model.
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