Automation of the assay development phase of drug discovery

12/98

Presentation:  Automation of the assay development phase of drug discovery
Damien Dunnington (1), Anthony Lozada (1), Hsiu-Yu Tseng (1), Paul Taylor (2) and Frances Stewart (2)
1. Hoechst Marion Roussel, Route 202-206, Bridgewater NJ 08807
2. SmithKline Beecham Pharmaceuticals, 709 Swedeland Road, King of Prussia PA 19406

The early phase of drug discovery, beginning with information and ending with lead compounds, has been re-engineered in recent years to accommodate advances in combinatorial chemistry and genomics. However, the reengineering has not been uniform and a disproportionate effort has been devoted to the screening phase, with relatively little attention to the assay development and hit follow-up stages where substantial bottlenecks persist. As new technologies such as miniaturization and fluorescence are introduced, the gap between an assay conceived by a disease group and the requirements for automated high throughput screening is becoming ever wider. The assay development and reformatting bottleneck has inspired an automated approach toward streamlining and ultimately eliminating this problem. This approach combines established design-of-experiments techniques with robotics and interfacing software, with the ultimate goal of assay configuration in a virtual lab environment and direct interfacing with robotics for execution. Examples will be presented to illustrate the impact of these approaches on assay configuration, robustness and hit detection, and progress toward a fully automated process will be discussed.

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Table of Contents

Automation of the assay development phase of drug discovery

Bottlenecks in the early process for drug discovery

The problem:

PPT Slide

PPT Slide

DOE approaches can handle multiple variables

Implementation

Implementation contd.

Implementation contd.

Implementation contd.

Some real life results

Kinase assay: response surface modeling

Kinase assay: surface modeling contd.

Hit detection is not impaired by optimization

Assay robustness is improved through optimization

Assay validation

Conclusions

Author: Dunnington, Damien J.

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