Discovery of novel antimalarial drugs based on thiosemicarbazone derivatives: An in silico approach

Arjun Anant, K. Kaur, Vivek Asati
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Abstract

Thiosemicarbazones belongs to the group of semicarbazides which contains sulfur atom instead of the oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi and other parasites. The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been selected for the virtual screening study. DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 =0.91 and. Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids ARG-265, PHE-227, and LEU-531 required for activity. The developed pharmacophore model has been used for screening of ZINC compounds where ZINC26244107, ZINC13469100, ZINC01290725and ZINC01350173 showed thebest XP docking scores (-11.60, -11.27, -11.35, -10.52, consecutively) with binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID: 5TBO. These results wereevaluated with thestandard antimalarial drug chloroquine. ADME analysis showed the drug-likeness properties of the compounds. The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.
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基于硫代氨基脲衍生物的新型抗疟药物的发现:一种计算机方法
氨基硫脲属于含有硫原子而非氧原子的氨基脲类。几项研究表明,它们对阴道毛滴虫、恶性疟原虫、克鲁兹锥虫和其他寄生虫等细胞外原生动物有效。目前的研究涉及药效团模型设计、3-D-QSAR、虚拟筛选和对接研究,所有这些都是使用各种参数进行评估的。本研究采用薛定谔软件进行。共选择了40个配体用于开发3D QSAR模型。为了预测3D-QSAR分析中的pIC50值,将整个数据集按7:3的比例分为两组,即训练集和测试集。已选择药效团假说进行虚拟筛选研究。DHHRR_ 1为最佳药效团模型,存活率为5.80。3D QSAR研究显示了一个显著的模型,R2=0.91和。Q2=0.73。该系列得分最高的化合物7e具有-10.44的对接得分,并且显示出与活性所需的氨基酸ARG-265、PHE-227和LEU-531的相互作用。所开发的药效团模型已用于筛选锌化合物,其中锌26244107、锌13469100、锌01290725和锌01350173与结合重要氨基酸ARG265、HIE185和LEU531对恶性疟原虫PDB ID:5TBO的XP对接得分最高(连续为-11.60、-11.27、-11.35、-10.52)。这些结果用标准抗疟药物氯喹进行了评价。ADME分析显示了这些化合物的药物相似性。本研究的结果可能有助于未来开发抗恶性疟原虫的抗疟化合物。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
18
审稿时长
>12 weeks
期刊介绍: In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders. The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.
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