Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.

Sekhar Talluri
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Abstract

Introduction: Epilepsy is a common disorder of the Central Nervous System (CNS). The rational design of small-molecule drugs for disorders of the CNS is a difficult process because the majority of small molecules are unable to cross the Blood-Brain-Barrier. An efficient method for the design of inhibitors that have high permeability through the Blood-Brain-Barrier has the potential for application in drug design for CNS disorders such as Addiction, Alzheimer's disease, Bipolar disorder, Depression, Epilepsy, Gliomas, and Tuberculous meningitis.

Method: Supervised learning was used to model the Blood-Brain-Barrier permeability of drugs like small organic molecules. This information was utilized to guide an evolutionary algorithm for the design of inhibitors with increased affinity for the target as well as higher Blood-Brain-Barrier permeability.

Results: The ligands designed with guided evolution were predicted to have higher binding affinity for the target as well as higher permeability across the Blood-Brain-Barrier compared to an evolutionary algorithm without the guidance. The guided evolutionary method was applied to design a set of drug-like ligands that were predicted to bind to GABA-T with high affinity, to be BBB permeable, and to be chemically synthesizable.

Discussion: Despite the availability of several drugs that are approved for the treatment of epilepsy, there are many cases that do not respond to available drugs or experience adverse effects. The novel ligands designed as part of this work have the potential to address the limitations of available drugs.

Conclusion: Guided evolution is an efficient computational approach for the design of CNS drugs. The de novo design of drugs by application of the guided evolution algorithm, developed as part of this work, has resulted in the generation of ligands that are potential drugs for the cure of epilepsy. However, the effectiveness of these drugs for the cure of epilepsy has to be validated experimentally.

应用一种新的导引进化算法增强血脑屏障通透性的癫痫药物计算设计。
癫痫是一种常见的中枢神经系统(CNS)疾病。合理设计治疗中枢神经系统疾病的小分子药物是一个困难的过程,因为大多数小分子药物不能穿过血脑屏障。设计具有高血脑屏障渗透性的抑制剂的有效方法,有可能应用于成瘾、阿尔茨海默病、双相情感障碍、抑郁症、癫痫、胶质瘤和结核性脑膜炎等中枢神经系统疾病的药物设计。方法:采用监督学习方法模拟有机小分子等药物的血脑屏障通透性。这些信息被用来指导进化算法设计抑制剂,增加对目标的亲和力,以及更高的血脑屏障通透性。结果:与没有指导的进化算法相比,用引导进化设计的配体对靶标具有更高的结合亲和力,并且通过血脑屏障的渗透性更高。应用引导进化方法设计了一组药物样配体,预测其与GABA-T具有高亲和力,可渗透血脑屏障,并且可化学合成。讨论:尽管有几种被批准用于治疗癫痫的药物,但仍有许多病例对现有药物没有反应或出现不良反应。作为这项工作的一部分,设计的新型配体有可能解决现有药物的局限性。结论:引导进化是一种有效的神经系统药物设计方法。作为这项工作的一部分,通过应用引导进化算法来重新设计药物,已经产生了潜在的治疗癫痫的药物配体。然而,这些药物治疗癫痫的有效性还有待实验验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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