Integration of resolving topological indices and ADMET analysis with MCDM methods (TOPSIS and SAW) for ranking breast cancer therapeutics

Recent studies have established a relationship between the topological structure of chemical compounds and their pharmacokinetic properties. These properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters, are essential for evaluating drugs. In this study, 15 breast cancer drug molecules, namely, tamoxifen, elacestrant, fulvestrant, toremifene, megestrol acetate, anastrozole, exemestane, letrozole, alpelisib, ixabepilone, abemaciclib, capivasertib, ribociclib, neratinib, and olaparib, were transformed into molecular graphs; Mβ-polynomials were used to calculate resolving topological indices. The study utilizes resolving topological indices to characterize the molecular structures of breast cancer drugs and formulates Quantitative Structure–Property Relationship (QSPR) models to forecast their ADMET properties. It involves the computation of resolving topological indices, conducting regression analyses to determine significant indices, and applying these indices in multicriteria decision-making methods to rank the drugs. The drugs were then ranked using decision-making approaches, especially Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW), using the computed indices and ADMET data. Based on the model findings, fulvestrant, neratinib, and ribociclib were ranked highest, whereas letrozole and exemestane were placed lowest. However, this ranking cannot accurately represent clinical outcomes and is only based on the mathematical model outputs. The consistency of the study was supported by a comparison plot that showed strong agreement between the two approaches. This study demonstrates how a systematic and quantitative framework for evaluating and ranking drug candidates may be obtained by merging chemical graph theory with decision-making techniques. This methodology may be extended to bigger datasets in future studies.