Castro Larissa Henriques Evangelista, Sant'Anna Carlos Mauricio R
Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil.
Departamento de Química Fundamental, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil.
Curr Top Med Chem. 2022 Mar 4;22(5):333-346. doi: 10.2174/1568026621666211129140958.
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice due to its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated with the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
诸如癌症和糖尿病等多因素疾病,由于其复杂的致病机制,对传统的“一靶点、一疾病”模式提出了挑战。尽管可以使用联合用药,但多靶点药物因其疗效、较低的不良反应和较低的耐药性发生几率可能是更好的选择。这些多靶点药物的计算机辅助设计可以探索用于单靶点药物设计的相同技术,但获取能够以相似疗效调节两个或更多靶点的药物所涉及的困难带来了新的挑战,解决这些挑战需要对已知技术进行调整,也需要开发新的技术,包括机器学习方法。在本综述中,讨论了一些用于多靶点药物设计的基于结构的药物设计(SBDD)和基于配体的药物设计(LBDD)技术,以及一些应用这些技术产生有效多靶点配体的案例。