Combinatorial Chemistry & High Throughput Screening - Volume 18, Issue 2, 2015

Malaria, the most virulent parasitic disease, has become a devastating health problem in tropical and subtropical regions, especially in Africa, due to favorable temperature and rainfall conditions for the development of the causative vector. Due to the spread of multidrug resistance to the marketed antimalarial drugs including the “magic bullet” artemisinin, discovery and development of new antimalarial drugs is one of the utmost challenges. Different government and non-government chemical regulatory authorities have recommended the application of non-animal, alternative techniques and in particular, in silico, methods in order to provide information about the basic physicochemical properties as well as the ecological and human health effects of chemicals before they reach into the market for public use. In this aspect, application of chemometric methods along with structure-based approaches may be useful for the design and discovery of new antimalarial compounds. The quantitative structureactivity relationship (QSAR) along with molecular docking and pharmacophore modeling techniques play a crucial role in the field of drug design. QSAR focuses on the chemical attributes influencing the activity and thereby allows synthesis of selective potential candidate molecules. In this communication, we have reviewed the QSAR reports along with some pharmacophore modeling and docking studies of antimalarial agents published during the year 2011 to 2014 and attempted to focus on the importance of physicochemical properties and structural features required for antimalarial activity of different chemical classes of compounds. Note that this is not an exhaustive review and all the given examples should be considered as the representative ones. The reader will gain an insight of the current status of QSAR and related in silico models developed for different classes of antimalarial compounds. This review suggests that combination of both ligand and structure-based drug designing approaches may be a promising tool for the discovery and development of new molecules with potential antimalarial activity.

Antimalarial drug discovery process is progressively carried out by a combination of innovation and knowledge based methods that include computational and experimental approaches to achieve potent leads. Among the various computational approaches, chemoinformatics plays a critical role in the discovery of new leads or drug candidates. Chemoinformatics provides researchers tools to derive information on substructures, chemical space, similarity and diversity. It also helps to manage and store chemical data, study important molecular properties and filter libraries with regard to specified criteria in the database. To accomplish these ends it uses various tools amongst which are docking, 3D-QSAR, similarity search, virtual screening, database mining and pharmacophore mapping. This review is a perspective of the utility of chemoinformatic approaches in antimalarial drug design. It covers various facets such as targets that have been exploited for antimalarial drug discovery by chemoinformatic methods; potential antimalarial targets that have not yet been explored; the challenges faced in antimalarial drug discovery, and future directions for discovery of novel antimalarial agents.

Among the various parasitic diseases, malaria is the deadliest one. Due to the emergence of high drug resistance to the existing drug candidates there is a global need for development of new drug candidates which will be effective against resistant strains of malaria parasite. In silico molecular modeling approaches have been playing an important role in the discovery of novel lead molecules having antimalarial activity. Present review is an effort to cover all the developments related to the application of computational techniques for the design and discovery of novel antimalarial compounds since the year 2011 onwards.

3D-QSAR has become a very important tool in the field of Drug Discovery, especially in important areas like malarial research. The 3D-QSAR is principally a ligand-based drug design but the bioactive conformation of the ligand can also be taken into account in constructing a 3D-QSAR model. The induction of receptor-based 3D-QSAR has been proven to give more robust statistical models. In this review, we have discussed the various 3D-QSAR works done so far which were aimed at combating malaria caused by Plasmodium falciparam. We have also discussed the various enzymes/receptors (targets) in Plasmodium falciparam for which the 3D-QSAR had been generated. The enzymes - wild and mutated dihydrofolate reductase, enoyl acyl protein carrier protein reductase, farnesyltransferase, cytochrome bc1, and falcipains were the major targets for pharmacophore-based drug design. Apart from the above-mentioned targets there were many scaffolds for which the target macromolecule was undefined and could have single/multiple targets. The generated 3D-QSAR model can be used to identify hits by screening the pharmacophore against a chemical library. In this review, the hits identified against various targets of plasmodium falciparam that have been discussed along with their basic scaffold. The various software programs and chemical databases that have been used in the generation of 3D-QSAR and screening were given. From this review, we understand that there is a considerable need to develop novel scaffolds that are different from the existing ligands to overcome cross-resistance.

The emerging cases of artemisinin and endoperoxide drug resistance are becoming a challenge to antimalarial drug discovery and therapy. The exact mode of action of this class of antimalarials is still unknown which presents a bottleneck for the understanding of drug resistance as well as designing new lead molecules of this class. To address this issue, the molecular docking and scoring studies of a homogeneous and structurally diverse dataset of artemisinin derived trioxanes have been performed on each of the two plausible targets of this class viz. heme and PfATP6. Since the crystal structure of PfATP6 is unknown, its homology model was built utilizing the human SERCA1 protein crystallized structure as a template. The binding energies of the heme binding site of the docked artemisinin derivatives showed very good correlation with the antimalarial activity (r2 = 0.69), whereas the same study with the binding site of pfATP6 showed a very poor correlation (r2 = 0.12), suggesting heme to be the possible target of artemisinin derived endoperoxides.

A series of quinolon-4(1H)-imines have been recently discovered as antimalarials, targeting both the exoerythrocytic and erythrocytic stages of the parasite’s development stages, which correspond to the phase of clinical symptoms. Endowed with chemical and metabolic stability, the quinolon-4(1H)- imines are thus presented as promissory dual-stage antimalarials. Three versions of multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) methods, namely traditional MIA-QSAR, augmented MIA-QSAR (aug-MIA-QSAR) and color-encoded aug-MIA-QSAR (aug- MIA-QSARcolor), were applied to model the antimalarial activities in this series of compounds. The multiple linear regression models indicated that the aug-MIA-QSAR method is more predictive and reliable than the others (R2 = 0.8079, R2cv = 0.6647 and R2pred = 0.9691) for this series of compounds. The selected aug- MIA-QSAR descriptors were used for pattern recognition using discriminant analysis by partial least squares (PLS-DA), in order to separate compounds with low, moderate and high bioactivities.

P. falciparum dihydroorotate dehydrogenase (PfDHODH) of the pyrimidine biosynthetic pathway offers a promising target for the development of antimalarial drugs in the scenario of widespread P. falciparum resistance. In this background, we have made an effort to decipher the structural requirements for the inhibition of PfDHODH using regression-based 2DQSAR, 3D-pharmacophore modeling and energy-based pharmacophoric (e-pharmacophore) studies. The 2D-QSAR and 3D-pharmacophore models were built from a structurally diverse set of 38 dihydrothiophenone derivatives, while the e-pharmacophore models were developed from two different co-crystal structures (PDB ID: 3O8A, 3I68) with varied scaffolds (benzimidazole, IC50: 22 nM and triazolopyrimidine, IC50: 56 nM) showing an inhibitory activity against the PfDHODH. The 2D-QSAR modeling study depicted the contribution of constitutional (number of oxygen atoms), spatial (molar volume), structural (number of rotatable bonds), and electronic (dipole moment) descriptors in predicting the PfDHODH inhibitory activity. The regression model showed the maximum contribution of constitutional descriptor (number of oxygen atoms representing the hydrogen bond acceptor feature) in determining the inhibitory activity. The best 3D-pharmacophore model (Hypo-1) with a correlation coefficient of 0.960 showed two hydrogen bond acceptor (HBA) and one ring aromatic (RA) features as the essential structural requirements for predicting the inhibitory activity. The e-pharmacophores derived from two different co-crystal structures highlighted the energy-based contribution of one hydrogen bond acceptor (e-HBA), one hydrogen bond donor (e-HBD) and three/four ring aromatic (e-RA) features for the inhibitory activity. The screening of external sets by the e-pharmacophores showed that both the models are capable of identifying the structurally diverse and potent compounds.

Falcipain-3 (FP-3) is a cysteine protease of the malaria parasite Plasmodium falciparum which is a promising and attractive target enzyme for antiparasitic chemotherapy. In this study, a support vector machine (SVM) model based on fingerprint-based descriptors was developed on a dataset of 239 FP-3 inhibitors to identify the most active antimalarial compounds among the active compounds provided from similarity search. The satisfactory classification performance achieved by the SVM model shows its ability to use it as a further filter to distinguish the most active compounds. The accuracy in prediction for the training, test and external validation sets were 97.39%, 94.74% and 90.6%, respectively. Furthermore, the performance of the model was examined by plotting the receiver operating characteristic (ROC) curve, and the area under the ROC curve was 0.96 for the modeling set. The ability of a virtual screening scheme to scaffold hopping or lead hopping is known as a key ability of an effective method for virtual screening. Three diverse reference FP-3 structures were chosen as active antimalarial compounds to search the lead-like database of ZINC and retrieve the most similar compounds. Compounds having Tanimoto similarity coefficient above 0.8 were extracted for further analysis by classification model. The SVM model rendered five most active compounds and they were also analyzed by ADME/Tox and diversity measures. Maximum property-based distance between extracted compounds was found to be 0.70, which shows the importance of applying multiple diverse reference compounds in the similarity searching.