Design of Hybrid Quinoline–Chalcone Compounds Against Leishmania amazonensis Based on Computational Techniques: 2D- and 3D-QSAR with Experimental Validation

Abstract

Background: Leishmania amazonensis, one of the causative agents of cutaneous leishmaniasis,is responsible for a neglected tropical disease affecting nearly one million individuals worldwide. Although clinical treatments are available, their effectiveness is often compromisedby high toxicity and limited selectivity. Methods: From a database, 64 chalcone derivativeswere studied using Comparative Molecular Similarity Indices Analysis (CoMSIA) andHansch analysis (2D-QSAR) to construct predictive computational models. Internal andexternal validation criteria were applied to identify structural determinants associatedwith antileishmanial activity. Based on these insights, twelve novel quinoline–chalconehybrids were designed, synthesized, and biologically evaluated against L. amazonensis.Results: The most robust CoMSIA model combined steric and hydrogen-bond acceptorfields (CoMSIA-SA), while Hansch analysis highlighted electronic descriptors—specificallyLUMO energy and the global electrophilicity index—as critical for parasite growth inhibition. Guided by these computational findings, a new series of 12 hybrid quinoline–chalconederivatives (E001–E012) was synthesized through a two-step procedure, achieving overallyields of 43–71%. Biological assays demonstrated that four compounds displayed inhibitoryactivity comparable to amphotericin B. Furthermore, physicochemical profiling and insilico pharmacokinetic predictions for the most active compounds (E003, E005, E006, andE011) indicated favorable biocompatibility and drug-like properties. Conclusions: Theseresults underscore the value of an integrative computational–experimental approach in the rational design of next-generation L. amazonensis inhibitors, reinforcing chalcone-basedscaffolds as promising pharmacophoric frameworks for antileishmanial drug discovery.