Serral Federico, Castello Florencia A, Sosa Ezequiel J, Pardo Agustín M, Palumbo Miranda Clara, Modenutti Carlos, Palomino María Mercedes, Lazarowski Alberto, Auzmendi Jerónimo, Ramos Pablo Ivan P, Nicolás Marisa F, Turjanski Adrián G, Martí Marcelo A, Fernández Do Porto Darío
Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
Front Pharmacol. 2021 Jun 9;12:647060. doi: 10.3389/fphar.2021.647060. eCollection 2021.
Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.
数十年来抗生素的成功使用目前正受到细菌耐药菌株不断出现的挑战。迫切需要新型药物,但是在新抗菌药物研发方面的私人投资不断下降的情况下,对抗耐药感染的努力成为一个全球性的公共卫生问题。新抗菌药物研发项目失败的原因包括分子靶点选择不当以及缺乏创新等。在此背景下,多种病原体的组学数据越来越多,为抗击传染病创造了新的药物发现和开发机会。目前,确定合适的分子靶点被认为是药物发现过程中的关键步骤。在此,我们综述了如何结合结构/功能分析和系统生物学,利用多组学数据的不同层面来确定最佳候选蛋白的优先级。一旦选定靶点,虚拟筛选可作为一种强大的方法来探索可作为抑制剂的分子支架,指导新型药物先导化合物的开发。本综述重点关注组学的出现以及生物信息学策略的开发和应用如何开启一个“大数据时代”,从而在节省成本的同时缩短时间线,改进靶点选择和先导化合物鉴定。