Nossa González Diana L, Gómez Castaño Jovanny A, Rozo Núñez Wilson E, Duchowicz Pablo R
Grupo Química-Física Molecular y Modelamiento Computacional (QUIMOL), Facultad de Ciencias, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central Del Norte, Tunja, Boyacá, Colombia.
Grupo Química-Física Molecular y Modelamiento Computacional (QUIMOL), Facultad de Ciencias, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central Del Norte, Tunja, Boyacá, Colombia.
J Mol Graph Model. 2021 Mar;103:107821. doi: 10.1016/j.jmgm.2020.107821. Epub 2020 Dec 7.
Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, remains a neglected endemic infection that affects around 8 million people worldwide and causes 12,000 premature deaths per year. Traditional chemotherapy is limited to the nitro-antiparasitic drugs Benznidazole and Nifurtimox, which present serious side effects and low long-term efficacy. Several research efforts have been made over the last decade to find new chemical structures with better effectiveness and tolerance than standard anti-Chagas drugs. Among these, new sets of thiosemicarbazone and thiazole derivatives have exhibited potent in vitro activity against T. cruzi, especially for its extracellular forms (epimastigote and trypomastigote). In this work, we have developed three antiprotozoal quantitative structure-relationship (QSAR) models for Chagas disease based on the in vitro activity data reported as IC (μM) and CC (μM) over the last decade, particularly by Lima-Leite's group in Brazil. The models were developed using the replacement method (RM), a technique based on Multivariable Linear Regression (MLR), and external and internal validation methodologies, like the use of a test set, Leave-one-Out (LOO) cross-validation and Y-Randomization. Two of these QSAR models were developed for trypomastigotes form of the parasite Trypanosoma cruzi, one based on IC and the other on CC data; while the third QSAR model was developed for its epimastigotes form based on CC activity. Our models presented sound statistical parameters that endorses their prediction capability. Such capability was tested for a set of 13 hitherto-unknown structurally related aromatic cyclohexanone derivatives.
恰加斯病由原生动物寄生虫克氏锥虫引起,仍然是一种被忽视的地方性感染病,全球约有800万人受其影响,每年导致12000人过早死亡。传统化疗仅限于硝基抗寄生虫药物苯硝唑和硝呋替莫,这些药物存在严重的副作用且长期疗效不佳。在过去十年中,人们进行了多项研究,以寻找比标准抗恰加斯病药物具有更好疗效和耐受性的新化学结构。其中,新的硫代氨基脲和噻唑衍生物组对克氏锥虫表现出强大的体外活性,尤其是对其细胞外形式(前鞭毛体和锥鞭毛体)。在这项工作中,我们基于过去十年报道的作为IC(μM)和CC(μM)的体外活性数据,特别是巴西利马 - 莱特小组的数据,开发了三种用于恰加斯病的抗原生动物定量构效关系(QSAR)模型。这些模型是使用替换方法(RM)开发的,该方法基于多元线性回归(MLR)以及外部和内部验证方法,如使用测试集、留一法(LOO)交叉验证和Y随机化。其中两个QSAR模型是针对克氏锥虫的锥鞭毛体形式开发的,一个基于IC数据,另一个基于CC数据;而第三个QSAR模型是针对其前鞭毛体形式基于CC活性开发的。我们的模型呈现出良好的统计参数,证明了它们的预测能力。这种能力针对一组13种结构相关的迄今未知的芳香环己酮衍生物进行了测试。