Predicting drug substances autoxidation.

Area of Science:

• Computational Chemistry
• Pharmaceutical Sciences
• Drug Development

Background:

• Chemical degradation and stability are critical challenges in pharmaceutical formulation.
• Autoxidation poses a significant risk to the stability of active pharmaceutical ingredients (APIs).
• Predictive modeling is essential for early-stage risk assessment in drug development.

Purpose of the Study:

• To develop an in silico risk assessment methodology for API stability concerning autoxidation.
• To evaluate computational approaches for predicting susceptibility to autoxidation.
• To establish a reliable method for identifying potential autoxidation risks in drug candidates.

Main Methods:

• Employed molecular modeling tools to investigate the autoxidation of 45 diverse organic compounds, including various APIs.
• Compared different density functional theory (DFT) functionals and settings (e.g., LDA, PBE) for accuracy and computational speed.
• Validated computational settings against known experimental data for APIs with documented autoxidation issues.

Main Results:

• Local Density Approximation (LDA) provided rapid calculations but systematically overestimated experimental values, indicating lower accuracy.
• Perdew-Burke-Ernzerhof (PBE) settings demonstrated a favorable balance between computational speed and predictive accuracy.
• The study identified PBE as a suitable computational approach for autoxidation risk assessment.

Conclusions:

• The developed in silico methodology is reliable for systematic risk assessment of drug stability.
• This approach can be confidently integrated into pharmaceutical development workflows.
• Enables proactive identification and mitigation of autoxidation-related stability issues in new drug entities.