Predicting Autoxidation of Sulfides in Drug-like Molecules Using Quantum Mechanical/Density Functional Theory...

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug development

Background:

• Autoxidation of drug molecules, particularly those containing sulfur, is a significant challenge in pharmaceutical development.
• Sulfur-containing active pharmaceutical ingredients (APIs) can form sulfoxides, impacting drug stability and safety.
• Predicting oxidative susceptibility accelerates compound stability assessments.

Purpose of the Study:

• To develop a predictive method for assessing the oxidative stability of sulfur-containing drug molecules.
• To establish a risk scale for sulfide oxidation based on computational predictions.
• To aid in the early identification of potentially unstable drug candidates.

Main Methods:

• Density functional theory (DFT) methods were employed to calculate S-O bond dissociation enthalpies (BDEs) for sulfoxides.
• B3LYP/6-31+G(d) was used for geometry optimization and frequency calculations.
• B3P86/6-311++G(2df,2p) was utilized for single-point energy calculations to determine electronic energies.

Main Results:

• A risk scale for sulfide oxidation was developed using data from 84 drug-like molecules and 50 sulfide scaffolds.
• Low oxidative susceptibility was observed for S-O BDE values below 69 kcal/mol.
• High oxidation risk was identified for S-O BDE values above 75 kcal/mol.

Conclusions:

• The developed risk scale accurately predicts the relative propensity of sulfide oxidation in organic molecules and commercial drugs.
• Computational assessment of S-O BDE provides a valuable tool for evaluating drug stability.
• This approach can significantly accelerate the drug development process by identifying potential stability issues early on.