Enhanced HTS hit selection via a local hit rate analysis.

Area of Science:

• Drug Discovery
• Computational Chemistry
• Biotechnology

Background:

• High-throughput screening (HTS) is crucial for identifying drug leads but suffers from false positives and false negatives.
• Current methods using simple activity cutoffs are insensitive to these screening artifacts, impacting lead discovery efficiency.
• A novel analysis approach is needed to improve the accuracy of hit selection in HTS.

Purpose of the Study:

• To develop and validate a new method for postprocessing HTS data that minimizes false positives and false negatives.
• To enhance the efficiency and reliability of hit selection in drug discovery pipelines.
• To improve the identification of active compounds and novel chemical series from HTS campaigns.

Main Methods:

• Developed a local hit rate (LHR) analysis method that assesses compound activity based on its chemical neighborhood.
• Evaluated the LHR method across ten diverse high-throughput screens, including cell-based and biochemical assays.
• Compared LHR performance against traditional hit selection based on primary screen activity values.

Main Results:

• The LHR analysis method demonstrated superior performance in identifying active compounds and active chemical series compared to primary activity ranking.
• LHR was approximately 2.3-fold more effective in identifying active compounds and 2.3-fold better at finding false negatives.
• The method successfully identified novel hit series that would have been missed by conventional approaches, increasing lead discovery potential.

Conclusions:

• Local hit rate (LHR) analysis is a robust and effective method for improving hit selection in high-throughput screening.
• This approach significantly enhances the identification of true active compounds and novel chemical matter, overcoming limitations of traditional methods.
• LHR analysis offers a valuable tool for increasing the efficiency and success rate of drug discovery programs.