N-function Heterocycles as Promising Anticancer Agents: A Case Study with a Decision Model in a Fuzzy Environment

Objective: This study aimed to evaluate the data according to five accepted criteria for the effects of twenty promising anticancer agents on five different cancer types and determine the most effective compounds for further in vitro and in vivo studies with a 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 decisionmaking methods, and a comparison was made with the criteria weights obtained as a result of the Complex Proportional Assessment (COPRAS) method. Moreover, the effects of criteria weights calculated with PFAHP on evaluating alternatives were analyzed using different scenarios.Results: Experimentally, twenty N-heterocyclic quinoline derivatives with different substituents were identified as promising anticancer agents. In this study, the multi-criteria decision-making (MCDM) model was proposed 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 were generated and analyzed to prove the consistency of the proposed methodology.Conclusion: MCDM strongly suggests that compounds 20, 17, 19, and 7 can be further investigated for in vivo</Quinoline, anticancer effect, IC50, LDH, pythagorean fuzzy sets, MCDM.i> studies.