Analysis of Design of Experiments with Dynamic Responses

Multivariate understanding offers
information that is critical
to the successful evaluation of risk within a pharmaceutical process.
A common means to acquire such data in the absence of detailed prior
knowledge is a design of experiments (DoE). A significant challenge
in the implementation of conventional DoE methodology is the analysis
of processes with transient responses. A large number of processes
in the pharmaceutical industry are described by kinetic processes
which results in severe difficulties in the interpretation of the
DoE analysis. In this paper we describe a workflow utilizing classic
DoE practices, data visualization, and techniques extracted from the
larger field of machine learning to facilitate the interpretation
of DoE results in process development. The resulting analysis is appropriate
for use in surveying the knowledge space for a chemical reaction and
identifying areas of risk that merit verification. For clarity the
workflow is described using a numerical solution to a well-defined
reaction network with specified kinetic responses. Case studies are
subsequently presented from the development of two late-stage API
processes.