Statistics and decision making in high-throughput screening.

Area of Science:

• Biochemistry
• Pharmacology
• Computational Biology

Background:

• Screening experiments involve decision-making regarding compound activity on biological systems.
• Experimental results exhibit inherent variability, necessitating statistical approaches for reliable interpretation.
• Statistics provides the framework for decision-making under uncertainty in scientific research.

Purpose of the Study:

• To highlight the critical role of statistical tools in analyzing screening experiments.
• To demonstrate how statistical methods aid in decision-making throughout the high-throughput screening (HTS) workflow.
• To guide the interpretation of screening data for valuable information and sound decision-making.

Main Methods:

• Focus on statistical tools for assay development, HTS process monitoring, and primary data analysis.
• Review of statistical process control for HTS campaign quality.
• Methodologies for detecting and managing HTS patterns to avoid bias.
• Approaches for statistically guided hit selection in HTS.

Main Results:

• Statistical tools are crucial for assessing assay quality and ensuring screening production readiness.
• Statistical process control monitors HTS campaign performance and detects potential biases.
• Pattern detection and mitigation methods improve the reliability of HTS data.
• Statistically guided hit selection enhances the accuracy of identifying active compounds.

Conclusions:

• The application of statistical tools is fundamental to robust HTS workflows.
• Statistics enables informed decision-making at key HTS stages: assay development, campaign monitoring, and hit identification.
• Utilizing statistical methods transforms raw screening data into actionable insights for drug discovery.