title: Overcoming obstacles in the implementation of factorial design for assay optimization.
abstract: Factorial experimental design (FED) is a powerful approach for efficient optimization of robust in vitro assays-it enables cost and time savings while also improving the quality of assays. Although it is a well-known technique, there can be considerable barriers to overcome to fully exploit it within an industrial or academic organization. The article describes a tactical roll out of FED to a scientist group through: training which demystifies the technical components and concentrates on principles and examples; a user-friendly Excel-based tool for deconvoluting plate data; output which focuses on graphical display of data over complex statistics. The use of FED historically has generally been in conjunction with automated technology; however we have demonstrated a much broader impact of FED on the assay development process. The standardized approaches we have rolled out have helped to integrate FED as a fundamental part of assay development best practice because it can be used independently of the automation and vendor-supplied software. The techniques are applicable to different types of assay, both enzyme and cell, and can be used flexibly in manual and automated processes. This article describes the application of FED for a cellular assay. The challenges of selling FED concepts and rolling out to a wide bioscience community together with recommendations for good working practices and effective implementation are discussed. The accessible nature of these approaches means FED can be used by industrial as well as academic users.
authors.affiliation: 1 Discovery Sciences Statistics, AstraZeneca Pharmaceuticals , Alderley Park, Cheshire, United Kingdom .
authors.forename: Robert
authors.initials: R
authors.lastname: Shaw
authors.forename: Martina
authors.initials: M
authors.lastname: Fitzek
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Mouchet
authors.forename: Graeme
authors.initials: G
authors.lastname: Walker
authors.forename: Philip
authors.initials: P
authors.lastname: Jarvis
mesh_terms: D001681:Biological Assay / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D055808:Drug Discovery; D005163:Factor Analysis, Statistical; D015233:Models, Statistical; D011381:Programming Languages; D012984:Software