Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors.

Leishmaniasis is a disease caused by a protozoan of the genus Leishmania, affecting millions of people, mainly in tropical countries, due to poor social conditions and low economic development. First-line chemotherapeutic agents involve highly toxic pentavalent antimonials, while treatment failure is mainly due to the emergence of drug-resistant strains. Leishmania arginase (ARG) enzyme is vital in pathogenicity and contributes to a higher infection rate, thus representing a potential drug target. This study helps in designing ARG inhibitors for the treatment of leishmaniasis. Py-CoMFA (3D-QSAR) models were constructed using 34 inhibitors from different chemical classes against ARG from L. (L.) amazonensis (LaARG). The 3D-QSAR predictions showed an excellent correlation between experimental and calculated pIC50 values. The molecular docking study identified the favorable hydrophobicity contribution of phenyl and cyclohexyl groups as substituents in the enzyme allosteric site. Molecular dynamics simulations of selected protein-ligand complexes were conducted to understand derivatives' interaction modes and affinity in both active and allosteric sites. Two cinnamide compounds, 7g and 7k, were identified, with similar structures to the reference 4h allosteric site inhibitor. These compounds can guide the development of more effective arginase inhibitors as potential antileishmanial drugs.

Quinoxalines against Leishmania amazonensis: SAR study, proposition of a new derivative, QSAR prediction, synthesis, and biological evaluation.

Neglected tropical diseases, such as leishmaniasis, lead to serious limitations to the affected societies. In this work, a structure-activity relationship (SAR) study was developed with a series of quinoxaline derivatives, active against the promastigote forms of Leishmania amazonensis. As a result, a new quinoxaline derivative was designed and synthesized. In addition, a quantitative structure-activity relationship (QSAR) model was obtained [pIC50 = - 1.51 - 0.96 (EHOMO) + 0.02 (PSA); N = 17, R2 = 0.980, R2Adj = 0.977, s = 0.103, and LOO-cv-R2 (Q2) = 0.971]. The activity of the new synthesized compound was estimated (pIC50 = 5.88) and compared with the experimental result (pIC50 = 5.70), which allowed to evaluate the good predictive capacity of the model.

LIV-3D-QSAR models for PGI2 receptor ligands using multiple conformations.

A new 3D descriptor, the local intersection volume (LIV), was developed by our group and applied to the construction of 3D-QSAR models for ligands of the PGI(2) receptor (IP). The target compounds are a set of 42 aromatic heterocyclic derivatives [Meanwell et al., J. Med. Chem. 36 (1993), 3884], which show agonist activities in the IP receptor and are inhibitors of platelet aggregation. The LIV-3D-QSAR models were obtained through the analysis of 30% of the generated conformations for each compound, using a combined Genetic Algorithm (GA) and Partial Least Square (PLS) approach [Rogers and Hopfinger, J. Inf. Comput. Sci. 34 (1994) 854]. Statistically, Model 3 is the best as well as the most comprehensive in a mechanistic sense. Furthermore, it can be applied to design new IP ligands.