An integrative MCDM framework using topological indices for ranking vitamins based on solubility properties

Area of Science:

• Computational Chemistry
• Decision Science
• Nutritional Science

Background:

• Vitamins are essential micronutrients with varying solubility impacting their bioavailability and application.
• Systematic ranking of vitamins based on physicochemical properties like solubility is crucial for formulation and drug design.
• Existing methods may not fully capture the complex interplay of molecular structure and solubility.

Purpose of the Study:

• To develop an integrated framework for ranking vitamins based on solubility.
• To utilize graph-theoretic topological indices and multi-criteria decision-making (MCDM) techniques for systematic vitamin ranking.
• To provide a transparent and reproducible tool for optimizing vitamin selection in various applications.

Main Methods:

• Molecular structures of eleven essential vitamins were converted into quantitative descriptors using six topological indices.
• Three MCDM methods (VIKOR, TOPSIS, SAW) were applied using topological indices as criteria.
• Four weighting strategies (point allocation, standard deviation, entropy, mean weight) were employed for criterion importance.
• Consensus across methodologies was assessed to ensure robust rankings.

Main Results:

• A high degree of consensus was observed across different MCDM methods and weighting strategies.
• $\alpha$ -tocopherol (vitamin E) was consistently ranked as the most soluble vitamin.
• Nicotinic acid (niacin) was consistently ranked as the least soluble vitamin.
• Nuanced differences in mid-tier rankings were observed, dependent on the MCDM approach and weighting scheme.

Conclusions:

• The integrated framework effectively ranks vitamins based on solubility properties.
• Combining topological indices with MCDM provides a powerful approach for complex ranking problems in nutrition and pharmacology.
• The proposed framework can aid in optimizing vitamin selection for dietary formulations and pharmaceutical design.
• This methodology has potential applications for ranking other classes of chemical compounds.