Abstract

Numerical taxonomy and pattern recognition analysis offer powerful tools that can greatly reduce the information burden of multiple-assay screening programs. These methods can be used to rationally design prescreens, identify assays that have similar chemical response patterns, select reporter assays for chemical response groups, evaluate drug selectivity, and predict a drug's likely mechanism of action. When combined with assays designed to identify lead compounds that have characteristics likely to cause failure at a later and more expensive stage of development, a simple three-stage primary discovery process consisting of a rational prescreen, reporters, and clinical failure assay can reduce the number of required culture wells by more than 20-fold and can eliminate all but 1–2 drugs per 1000 tested as leads for further evaluation and development.

Highlights

  • The extraordinary volume of data generated by high throughput screening has shifted the bottlenecks in drug discovery from compound acquisition and screening to the management and analysis of data

  • How can biological data be used to make the screening process smaller, simpler, faster, and cheaper? And how can biological data be used to better prioritize lead compounds for further development? Numerical taxonomy and pattern recognition o er powerful tools for addressing these questions, and can greatly reduce the information burden of multi-assay screening programs

  • There are more than 300 di erent human neoplastic diseases, each a potential screening target

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Summary

Dealing with the data deluge in high throughput screening

Numerical taxonomy and pattern recognition analysis oå er powerful tools that can greatly reduce the information burden of multiple-assay screening programs. T hese methods can be used to rationally design prescreens, identify assays that have similar chemical response patterns, select reporter assays for chemical response groups, evaluate drug selectivity, and predict a drug’s likely mechanism of action. When combined with assays designed to identify lead compounds that have characteristics likely to cause failure at a later and more expensive stage of development, a simple three-stage primary discovery process consisting of a rational prescreen, reporters, and clinical failure assay can reduce the number of required culture wells by more than 20-fold and can eliminate all but 1–2 drugs per 1000 tested as leads for further evaluation and development

Introduction
Identifying chemical response groups
Reporter assays
Rationally designed prescreen
Clinical failure assays
Comparing ngerprints
Findings
Association coe cients

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