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These studies led to the release of Zanamivir, an Food and Drug Administration FDA -approved drug in , used to treat infections caused by influenza viruses. These exemplars show that CADD methods are useful to identify and select molecules able to interact with high affinity and selectivity against a specific molecular target. As both strategies require an extensive discussion, only SBDD will be highlighted in this manuscript. Among SBDD methods, molecular docking, highlighted by its practice, is fast and has low computation cost.
The docking between ligand and the molecular target is the first step in any cell signaling pathway. In this study, docking is referred to as target-based virtual screening TBVS. In addition, TBVS can be evaluated against several molecular target data banks to predict the molecular target of ligands, similar to high-throughput screening HTS experiments.
Virtual screening VS became widely used in during the initial phases of research into the drug discovery process for the identification and selection of new bioactive molecules. Specifically, vHTS is a computational method of molecular docking to predict the molecular target of ligands by estimating their binding affinity. Even though genomics, proteomics, and ligands databanks have been established, the platform to perform automated TBVS is still unknown, including for neglected diseases.
This data bank permits in silico vHTS experiments against a pool of P. In this paper, the BraMMT was evaluated through docking tools and a set of known antimalarial compounds. Selection and preparation of molecular targets - The three-dimensional structures of the receptors were obtained from the PDB database from their respective codes 14 using the key word: Plasmodium falciparum. For the molecular target with several entries in the PDB, the structure with the lowest values crystallographic resolution was considered, which had the ligand in the binding site.
In addition, the molecular targets P. Thereafter, the molecular targets were prepared by removing the replicate residue present at the binding site.
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Moreover, only water molecules that carried out at least two interactions between the ligand and molecular target were kept. This characteristic predicts whether a protein can bind with high affinity and specificity to small compounds. Evaluation of molecular docking - Re-docking methodology was carried out to evaluate the AutoDock Vina program. In addition, the crystallographic structures without ligand, a search for equivalent structure belongs to another organism, was performed using the BLAST program.
The crystallographic and re-docking ligands were overlaid for calculation of root mean square deviation RMSD using the Discovery Visualizer 4. Additionally, the receiver-operator characteristic ROC curve and the area under the ROC curve AUC were established for each molecular target to evaluate the ability of the molecular docking methodology to differentiate the active molecules from decoys false positives.
Active compounds and decoys were submitted to the molecular docking calculations in the AutoDock Vina program 11 , 17 using OCTOPUS, 12 in which the configuration files were determined through a re-docking step.
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Equation 1. Molecular dynamics - The molecular dynamics of selected compounds were performed by MolAr in-house software.
In the simulations, ligand flexibility was recorded. Flexible simulations were performed in steps and energy minimisation cycles. In vitro schizonticidal activity of the clioquinol against P. The parasites were synchronised using sorbitol treatment, and the parasitaemias were evaluated microscopically with Giemsa-stained blood smears. Infected red blood cells were plated in a well plate at 0.
Different concentrations of the clioquinol were added in triplicate, and twelve drug-free wells were used as controls six frozen after 24 h as the HRPII background. The results were expressed as the mean of the half-maximal inhibitory dose IC 50 of three assays performed in triplicate, compared with drug-free controls. Curve fitting was performed using OriginPro 8.
Negative control groups were constituted of cells without treatment. Then, the supernatant was discarded, and the insoluble formazan product was dissolved in DMSO. The optical density OD of each well was measured using a microplate spectrophotometer at nm. All assays were performed in triplicate. Selection and preparation of molecular targets - The molecular targets were retrieved from PDB by considering the lowest values resolution among similar structures and the presence of a ligand in the binding site.
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As a result, the BraMMT was constructed using 35 molecular targets. Table I shows the PDB code of the selected targets with their respective resolutions. Among them, seven were hydrolases, four isomerases, eight oxidoreductases, eight transferases, four lyases, one cell signaling protein, two transporter proteins, and one cytokine inhibitory protein. In general, all protonation states were kept at pH 7. The inclusion of water molecules in the binding site was carefully checked by visual inspection.
The inclusion of water molecules is a crucial issue since such molecules can affect the ability to predict ligand binding, in which coordination is stabilised by the presence of an explicit water molecule. In addition, the TDR targets platform was used to obtain the druggability of major pathogens of tropical diseases, including Mycobacterium leprae and M. As seen in Table I , Evaluation of molecular docking - The docking methodology was evaluated by re-docking and the ROC curve. It consists of the structural alignment between the crystallographic ligand and the pose obtained by the docking simulation.
This alignment is analysed by RMSD of the heavy atoms positions. The threshold value is 2. In addition, because of the random character of Autodock Vina, there was no significant difference in the binding energy after simulation in triplicate, suggesting that it is robust for virtual screening experiments 11 [see Supplementary data Table II ]. Crystallographic and re-docking ligands, in black and red, respectively. The most attractive confirmed hits in terms of potency, selectivity, and predicted drug-like physicochemical properties between these two HTSs were picked up for further development and the data obtained from them have served as the basis for different initiatives Figure 1  — .
The access to high-quality large chemical libraries will be of fundamental importance for the advance of drug discovery against neglected diseases, as it will be to get the resources and know-how to mine and test them.
In this perspective, collaboration between pharmaceutical companies and academic laboratories becomes a key aspect for the successful development of future drugs. Chemical structures of anti— T. High content screening HCS allows the automated acquisition and analysis of multiple cellular features simultaneously  , . HCS is a powerful screening tool, the use of which has expanded in the last years because of high assay throughput, automatic image acquisition and analysis provided by user-friendly software interfaces and the possibility of robotization  , .
The availability of this technology has already permitted the development of image-based assays to identify active compounds against Leishmania donovani  ,  — . Similarly to HTS based on recombinant reporter parasites, automatic quantification is not biased by the observer visual parasite counting, as it is performed by an image analysis script specifically developed for this purpose  ,  , which once set up has been shown to be extremely useful, providing very accurate readings and saving valuable time .
In the drug development process against T. Additionally, this assay provides the information on host cell cytotoxicity of each compound in a single experiment, delivering immediate results on the selectivity index. More recently, scientists from Institute Pasteur in Korea have developed an algorithm to aid in the image analysis process of a T. There are yet some disadvantages related to HCS that need to be improved, like the storage and handling of the high amount of data generated and the speed of both picture acquisition and computing analysis that can have an impact on the developed assays HTS scale up  , .
A drawback specific to the reported anti— T. Besides the identification of new hits, HCS could also provide a major input in the hit-to-lead-optimization process and play a key part in deciphering new mechanisms of action  , . In the future, fluorescently tagged ligands or specific staining with antibodies could be incorporated in the assays to provide mechanistic information of compounds.
A fundamental step in the drug discovery path is the selection and qualification of hits to be progressed into leads .
A requirement for this upgrade is consistent efficacy in animal models  , . Although various animal infection models have been studied in Chagas disease research, by far the most used has been the mouse model . The mouse is the most preferred in vivo model because it adequately reproduces the main pathogenic features of human T. Transgenic parasites have also been used with success for in vivo studies. Bio-imaging of mouse models infected with transgenic T.
Before the implementation of transgenic parasites and in vivo imaging techniques, animals had to be killed to obtain end-point data on parasite detection and organ dissemination, which was very labor-intensive, presented risk for human infection, and required the use of large animal groups, all contributing to increased costs  , .
Furthermore, the techniques used PCR, in situ hybridization, and microscopic blood or tissue sections parasitic counting were cumbersome and presented certain limitations . By means of luminescent or fluorescent transgenic parasites and the appropriate detection equipment, it is now possible to accurately evaluate in vivo the infection process and the drug responses that might inhibit it. In comparison with measurements relying on sacrifice, bio-imaging has the great advantage of providing continuous readouts on anesthetized animals, which has greatly contributed to the understanding of infection dynamics and parasite tropism  , .