Assay Optimization: A Statistical Design of Experiments Approach

Abstract

With the transition from manual to robotic HTS in the last several years, assay optimization has become a significant bottleneck. Recent advances in robotic liquid handling have made it feasible to reduce assay optimization timelines with the application of statistically designed experiments. When implemented, they can efficiently optimize assays by rapidly identifying significant factors, complex interactions, and nonlinear responses. With the use of an integrated approach called automated assay optimization developed in collaboration with Beckman Coulter (Fullerton, CA), the process of conducting these experiments has been greatly facilitated. This approach imports an experimental design from a commercial statistical package and converts it into robotic methods. The data from these experiments are fed back into the statistical package and analyzed, resulting in empirical models for determining optimum assay conditions. The optimized assays are then progressed into HTS. This tutorial will focus on the use of statistically designed experiments in assay optimization.