Automated Assay Optimization with Integrated Statistics and Smart Robotics

Abstract

The transition from manual to robotic high throughput screening (HTS) in the last few years has made it feasible to screen hundreds of thousands of chemical entities against a biological target in less than a month. This rate of HTS has increased the visibility of bottlenecks, one of which is assay optimization. In many organizations, experimental methods are generated by therapeutic teams associated with specific targets and passed on to the HTS group. The resulting assays frequently need to be further optimized to withstand the rigors and time frames inherent in robotic handling. Issues such as protein aggregation, ligand instability, and cellular viability are common variables in the optimization process. The availability of robotics capable of performing rapid random access tasks has made it possible to design optimization experiments that would be either very difficult or impossible for a person to carry out. Our approach to reducing the assay optimization bottleneck has been to unify the highly specific fields of statistics, biochemistry, and robotics. The product of these endeavors is a process we have named automated assay optimization (AAO). This has enabled us to determine final optimized assay conditions, which are often a composite of variables that we would not have arrived at by examining each variable independently. We have applied this approach to both radioligand binding and enzymatic assays and have realized benefits in both time and performance that we would not have predicted a priori. The fully developed AAO process encompasses the ability to download information to a robot and have liquid handling methods automatically created. This evolution in smart robotics has proven to be an invaluable tool for maintaining high-quality data in the context of increasing HTS demands.

High‐Throughput Screening for Kinase Inhibitors

“…To improve efficiency, we handle this first step of assay development in a standardized manner, starting with a screen for factors and interactions that have significant effects. For this purpose, design of experiments (DOE) software (Design Expert (StatEase)) is used together with automated pipetting (Biomek, AAO software, Beckman Coulter) 24. This allows efficient randomized screening of many variables and their interactions.…”

Automated liquid handling robot for rapid lateral flow assay development

“…The automated liquid handling robot, specifically, is a robotic system that has been demonstrated to improve laboratory processes by automating the pipetting component of a range of laboratory methods. The impact of these robots has generally been increased reproducibility, decreased cost, and increased overall efficiency [ 17 – 21 ]. Initially demonstrated to automate the highly repetitive enzyme-linked immunosorbent assay (ELISA), automated liquid handlers have since been used to automate numerous types of processes, such as nucleic acid extraction, purification, or assembly; cell culture; drug discovery; and materials discovery [ 18 , 22 – 25 ].…”

Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development

“…Brute-force approaches, which test as many variable permutations as possible, and DOE approaches, which apply statistics to systematically determine the relationship between a set of variables affecting an output, have been used in high-throughput screening (HTS) assay optimization. 7–10 Both suffer from a lack of feedback and adaptivity. This means that they each specify the entire set of experiments to be conducted a priori, which means they waste resources on low-performing experiments rather than concentrating attention on high-performing experiments.…”