2D-quantitative structure–activity relationships model using PLS method for anti-malarial activities of anti-haemozoin compounds

Abstract

Background Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.

Design of N-11-Azaartemisinins Potentially Active against <i>Plasmodium falciparum</i> by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

“…The interactions between N-11-azaartemisinins (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) and heme in their entirety occur preferentially orienting Fe 2+ to heme. The distance d(Fe-O 1 ) is smaller than d(Fe-O 2 ) in all ligand-heme interactions and, in general, d(Fe-O1) has higher values for N-11-azaartemesinins cha, when compared to N-11-azaartemisinins cla.…”

“…The relative activities were obtained with the expression: relative IC 50 = IC 50 (artemisinin)/IC 50 (N-11-azaartemisinin) [51], where IC 50 corresponds to 50% of the inhibitory concentration of the compounds. The following hypothesis was considered in the research: N-11-azaartemisinins with relative IC50 ≥ 0.24 correspond to cha (1-8) and N-11-azaartemisinins with relative IC50 < 0.24 correspond to cla (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19).…”

“…Derivative 6, an acylurea, is the compound with the highest activity against P. falciparum among the N-11-azaartemisinins, about 1.5 times more active than artemisinin. In the N-sulfonyl derivatives, which present monosubstituted aromatic rings (9)(10)(11)(12)(13)(14), it is observed that the activity increases with the increase of the electronegativity of the substituent.…”

“…About the investigated N-11-azaartemisinis) also exhibit the endoperoxide linkage necessary for antimalarial activity. Figure 3 shows the MEP maps for the N-11-azaartemisinins of the training set obtained by the inclusion of substituents in the N atom of the lactam function (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). As one can see in this figure, the MEP maps are similar to artemisinin and 11-azaartemisinin in the 1, 2, 4 trioxane ring region, with the electron density of some molecules more concentrated in this region, indicating greater biological activity.…”

QSAR, Molecular Docking and Molecular Dynamics Simulation Studies for the prediction of Camptothecin derivatives as anticancer agents

“…Data set of 46 CPT derivatives with their antiproliferative activity on 3 different cell lines were taken from the work of Yang et al,2019 [10]. For the dependent variable in Hansch analysis, cytotoxicity activity in half-maximal concentration IC 50 (nM) was converted into the pIC 50 (Table 1) for ease of calculation [11].…”