The control of ingredients quantities in pharmaceutical formulations is critical to a drug product quality. Often, the Active Pharmaceutical Ingredient (API), and sometimes specific excipients, concentrations testing in the final product is required to release a batch.
By applying Design of Experiments (DoE) and Multi-Variate Data Analysis (MVDA), a UV-Vis spectroscopy chemometric model has been developed which is capable of measuring simultaneously both ingredients contents in the formulation. The test can easily be applied to finished or bulk product and the results are immediately available.
Design of experiments is typically presented as a “one shot” approach. However, it may be more efficient to divide the experiment into smaller pieces, thus expending resources in a smarter, more adaptive manner. This sequential approach becomes especially suitable when experimenters begin with very little information about the process, for example, when scaling up a new product. It allows for better definition of the design space, adaption to unexpected results, estimation of variability, reduction in waste, and validation of the results.
A nice new addition to Design Expert is the KCV designs (Kowalski, Cornell, and Vining 2000 and 2002) for experiments that involve both mixture components and process variables. This talk presents an overview on these designs. It begins with a brief history of their origin. It then motivates the basic approach for the construction of these designs and contrasts this approach to other approaches popular at that time. It then discusses some of the subtleties involved in analyzing these designs. An example illustrates their use.
In today’s Industry 4.0, industrial processes are becoming increasingly complex, presenting significant challenges to the industrial experimenter. In particular, modern experimental design practice can often lead to non-standard situations. In this talk I will discuss some examples of the non-standard experimental design situations I’ve encountered in modern practice, with the common denominator in all these situations being a split-plot treatment structure.
In this presentation, I will speak from the heart on my lifelong involvement with design of experiments. Starting with my early years as a new engineer using DOE, I will then focus on providing DOE for 35 years at Stat-Ease. In wrapping up, I will give a sneak peek at version 13 of Design-Expert® software.
By way of example, this presentation lays out a strategy for design of experiments (DOE) that provides maximum efficiency and effectiveness for development of a robust process. It provides a sure path for converging on the ‘sweet spot’—the most desirable combination of process parameters and product attributes. Whether you are new or experienced at doing DOE, this talk is for you (and your organization's bottom line!).