Log D analysis using dynamic approach.

Area of Science:

• * Pharmaceutical Sciences
• * Physical Chemistry
• * Computational Chemistry

Background:

• * Lipinski's rule uses Log D (logarithm of the distribution coefficient) to predict drug-likeness.
• * Molecular distribution between aqueous and organic phases depends on pH.
• * Understanding Log D is crucial for pharmaceutical formulation development.

Purpose of the Study:

• * To model the distribution coefficient (Log D) of various molecules.
• * To compare conventional algebraic and generalized 'dynamic' modeling approaches.
• * To validate these methods using experimental data for amphoteric molecules.

Main Methods:

• * Employed conventional algebraic methods for Log D calculation.
• * Utilized a generalized 'dynamic' approach to model Log D.
• * Analyzed experimental Log D data for specific amphoteric compounds.

Main Results:

• * Demonstrated the equivalence between algebraic and 'dynamic' Log D modeling.
• * Successfully applied both methods to predict Log D for diverse molecular types.
• * Validated the models using experimental data for nalidixic acid, mebendazole, benazepril, and telmisartan.

Conclusions:

• * The 'dynamic' approach provides an equivalent and effective alternative to algebraic Log D modeling.
• * These validated methods enhance the prediction of molecular distribution in pharmaceutical contexts.
• * The study confirms the applicability of these modeling techniques across different molecule types and conditions.