Tara Scherder, Partner, SynoloStats
Limited platform knowledge, complex relationships, and accelerated speed to market are just a few of the reasons why process development of oligonucleotides, like other cell and gene therapy products, is so challenging. The advantages of designed experiments (DOE) instead of One Factor at A time (OFAT) experimentation in such situations is well recognized, gaining significant popularity over the past decade. Control strategies developed using designed experiments result in robust processes, reducing risk not only in early process development, but across the product lifecycle. But do you know there are recent developments in the science of designed experiments? Computational advancements have led to new classes of designs with desirable properties such as reduction in the number of experiments, flexibility to assign varying focus to factors and adequate precision, even in the presence of second order terms. In this talk, examples of the advantages of these designs over classical designs will be shown, along with common mistakes made for all classes of designed experiments.