N-function heterocycles as promising anticancer agents: A case study with a decision model in fuzzy environment

Abstract

Objective: In this study, it was aimed to evaluate the data according to five accepted criteria for the effects of twenty promising anticancer agents on five different cancer types and to determine the most effective compounds for futher in vitro and in vivo studies with multi criteria decision making method (MCDM), which rationalizes decision making in a fuzzy environment to avoid the high cost and time requirements of further preclinical and clinical studies. Methods: Within the scope of the study, the weights of the five criteria were evaluated with the Pythagorean fuzzy analytic hierarchy process (PFAHP), which is one of the multi criteria decision making methods, and a comparison was made with the criteria weights obtained as a result of the Complex proportional assessment (COPRAS) method. Moreover, the effect of criteria weights calculated with PFAHP on the ranking of alternatives was analyzed using different scenarios. Results: Experimentally, twenty N-heterocyclic quinoline derivatives with different substituents were identified as promising anticancer agents. In this study, a multicriteria decision making (MCDM) model is proposed under uncertainty to identify the most promising anticancer agents against all tested cell lines. Both the experimental and model results indicated that 20, 17, 19, and 7 are the most promising anticancer agents against the A549, HeLa, Hep3B, HT29 and MCF7 cell lines. Moreover, different scenarios are generated and analyzed to prove the consistency of the proposed methodology. Conclusion: MCDM strongly suggests that compounds 20, 17, 19, and 7 can be recommended to be further investigated for in vivo studies.