a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil.
b Laboratory for Molecular Modeling, Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , NC , USA.
Expert Opin Drug Discov. 2016 Jun;11(6):557-67. doi: 10.1080/17460441.2016.1178230. Epub 2016 May 3.
The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects.
Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors.
Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.
几乎只使用吡喹酮治疗血吸虫病引起了人们对可能出现耐药血吸虫的担忧。因此,迫切需要新的抗血吸虫药物。鉴定先导化合物和生成高质量数据是血吸虫病药物发现项目早期阶段的关键步骤。
本文作者重点介绍了抗血吸虫先导化合物发现的最新进展,特别是提到了自动化体外基于靶点和全器官筛选以及化学数据库虚拟筛选的应用。他们强调了上述方法各自的优缺点,并提出了可能的路线图,以整合多种策略,这可能有助于优化研究成果,并使药物发现工作更加成功和具有成本效益。
近年来,增加药物发现方面的合作和获取资金为抗击血吸虫病的斗争提供了助力。然而,作者认为这场战斗还包括克服科学挑战的创新策略。在这种情况下,体外筛选和计算机辅助药物发现的显著进展有助于提高药物发现活动的成功率并降低成本。尽管这些方法中的一些已经用于当前的抗血吸虫先导化合物发现管道中,但将这些策略整合到一个可靠的工作流程中应该能够在不久的将来为血吸虫病提供新的治疗方法。