Obtaining Microbiological and Pharmacokinetic Highly Predictive Equations.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Antibacterial quinolones are crucial therapeutics.
• Predicting their physicochemical and biological properties is vital for drug development.
• Existing methods may require extensive experimental data.

Purpose of the Study:

• To develop accurate prediction equations for quinolone properties using topological indices.
• To establish reliable models for forecasting biological activities and pharmacokinetic parameters.
• To leverage literature data for robust quantitative structure-activity relationship (QSAR) development.

Main Methods:

• Multilinear Regression (MLR) analysis was employed.
• Topological indices were used as structural descriptors.
• Internal validation techniques including intercorrelation, Y-randomization, and leave-one-out cross-validation were performed.

Main Results:

• Five optimal regression functions were selected based on statistical parameters.
• Reliable predictions were achieved for Minimum Inhibitory Concentration 50 (MIC50) against Staphylococcus aureus, Streptococcus pyogenes, and Bacteroides fragilis.
• Accurate predictions were also obtained for Mean Residence Time (MRT) and Volume of Distribution (VD).

Conclusions:

• The integration of molecular topology and MLR offers a powerful approach for predicting pharmacological properties.
• This method facilitates efficient drug discovery and development of novel antibacterial agents.
• The validated models provide a reliable tool for assessing quinolone efficacy and behavior.