An economic framework to prioritize confirmatory tests after a high-throughput screen

Area of Science:

• Drug discovery and development
• Computational chemistry
• Biotechnology

Background:

• High-throughput screening (HTS) is crucial for identifying potential drug candidates.
• Traditional methods for selecting hits for confirmation rely on statistical measures like the false discovery rate (FDR).
• These statistical approaches often overlook the economic realities of experimental resource allocation.

Purpose of the Study:

• To reframe the hit selection problem in high-throughput screening as an economic decision rather than a purely statistical one.
• To develop a novel economic framework for optimizing the number of hits to pursue for confirmatory experiments.
• To introduce a tool that quantifies the marginal cost of discovery, balancing true and false positives.

Main Methods:

• An economic analysis framework was developed to model the decision-making process for hit confirmation.
• The proposed strategy was validated using retrospective simulations of screening data.
• Prospective experiments were conducted to assess the real-world applicability of the economic model.

Main Results:

• The economic analysis provides an optimal solution for deciding the number of hits to confirm.
• A novel tool was developed to quantify the marginal cost of discovery, reflecting the economic trade-off between true and false positives.
• The validated strategy successfully identified 157 additional active compounds that were previously misclassified as inactive.

Conclusions:

• An economically optimal experimental strategy for hit confirmation in high-throughput screening can be derived.
• This economic approach leads to more rational and efficient experimental strategies compared to purely statistical methods.
• The developed framework enhances drug discovery by improving the identification of active compounds and optimizing resource allocation.