N-oxidation Regioselectivity and Risk Prediction Using DFT-ALIE Calculations.

Area of Science:

• Computational Chemistry
• Drug Development
• Medicinal Chemistry

Background:

• N-oxide degradants pose significant risks in new drug development, affecting pharmacological activity, solubility, stability, toxicity, and efficacy.
• Chemical transformations leading to N-oxides can also compromise physicochemical properties essential for drug manufacturability.
• Early identification and control of N-oxide formation are critical for successful therapeutic development.

Purpose of the Study:

• To develop an in-silico computational approach for identifying potential N-oxide formation in Active Pharmaceutical Ingredients (APIs).
• To assess the susceptibility of nitrogen atoms to autoxidation using computational methods.

Main Methods:

• Utilized molecular modeling and Density Functional Theory (DFT) at the B3LYP/6-31G(d,p) level of theory.
• Performed Average Local Ionization Energy (ALIE) calculations.
• Analyzed 257 nitrogen atoms across 15 different oxidizable nitrogen types.

Main Results:

• Demonstrated that ALIE reliably predicts nitrogen atoms most susceptible to N-oxide formation.
• Developed a risk scale to categorize nitrogen oxidative vulnerabilities as low, medium, or high.
• The method provides a quantitative measure for assessing N-oxidation risks.

Conclusions:

• The developed in-silico process is a powerful tool for identifying structural susceptibilities to N-oxidation.
• Enables rapid structure elucidation and helps resolve potential experimental ambiguities in drug development.
• Facilitates proactive control of N-oxide formation, improving drug safety and manufacturability.