Predicting liver cytosol stability of small molecules

Area of Science:

• Drug Metabolism and Pharmacokinetics
• Medicinal Chemistry
• Computational Chemistry

Background:

• Cytochrome P450 (CYP450) metabolism is well-studied, but cytosolic enzyme metabolism is often overlooked in early drug discovery.
• Limited data and tools exist for designing compounds to avoid cytosolic enzyme liabilities.
• Overlooking cytosolic metabolism can delay drug discovery and lead to compound failure.

Purpose of the Study:

• To screen compounds for liver cytosol-mediated metabolic stability.
• To extract transformation rules for optimizing compounds against cytosolic liabilities.
• To develop in silico models for predicting cytosolic enzyme-mediated liabilities.

Main Methods:

• Screened 1450 compounds for metabolic stability in mouse and human liver cytosol.
• Utilized matched molecular pairs analysis and QSAR modeling to identify stability-affecting transformations.
• Validated transformation rules prospectively and on diverse chemical libraries.
• Generated in silico classification models using molecular descriptors and machine learning.

Main Results:

• Identified chemical structure transformations impacting cytosolic metabolic stability.
• Developed and validated transformation rules for medicinal chemists.
• Created predictive in silico models for cytosolic liabilities.
• Made validation datasets publicly available.

Conclusions:

• This study presents the first systematic in silico approach to address cytosolic enzyme-mediated liabilities.
• The developed tools and rules aid chemists in designing more metabolically stable drug candidates.
• Publicly available data and models facilitate further research in drug metabolism.