A theoretical entropy score as a single value to express inhibitor selectivity

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Maximally selective ligands are crucial in drug discovery to minimize toxicity and side effects.
• Compound selectivity is increasingly monitored early in the drug discovery pipeline.
• A quantitative measure is needed to interpret large selectivity profiling datasets.

Purpose of the Study:

• To propose a novel, quantitative measure for compound selectivity.
• To address the limitations of existing selectivity metrics.
• To provide a robust tool for assessing selectivity in drug discovery.

Main Methods:

• Development of a theoretical entropy score based on IC(50) data.
• Application of the entropy score to kinase and nuclear receptor profiling data.
• Analysis of high-throughput screening and clinical compound profiling data.

Main Results:

• The proposed entropy score is non-arbitrary, fully utilizes IC(50) data, and is independent of a reference enzyme.
• The entropy score demonstrates superior robustness across different data sources, being less sensitive to errors.
• Analysis revealed that increased kinase inhibitor selectivity does not always correlate with improved drug-like properties.

Conclusions:

• The theoretical entropy score is the optimal method for quantifying selectivity from panel profiling data.
• This score facilitates the study of molecular mechanisms underlying selectivity.
• The entropy score serves as a valuable tool for guiding compound progression in drug discovery programs.