DRUG DESIGN AGAINST COVID-19: A REMOTE TEACHING EXPERIENCE IN PHARMACEUTICAL CHEMISTRY

Infront of with the difficulties faced in making a new drug available to the population, it is essential to seek ways to simplify the process. In silico methodologies are alternatives to benchtop experiments, being frequently used due to their speed and low cost. The present study aimed to formulate a theoretical-practical activity in the Pharmaceutical Chemistry course, where students applied their knowledge of structural modeling and molecular docking to propose bioactive compounds against molecular targets of the SARS CoV-2 virus. The class was divided, and each group presented a drug candidate, the precursors being natural molecules. In total, seven derivatives were designed and tested against different macromolecules, and then an in silico prediction of their physicochemical characteristics was performed. The docking results were positive for all derivatives, in terms of binding energy, mainly GEND with -9.0 kcal mol-1. In addition, the prototypes exhibited good interactions with the amino acids of the respective targets, mainly KAED, QUED and GEND, in addition to presenting adequate physicochemical properties for meeting the Lipinski restrictions. Therefore, this study presented at least three potential inhibitors of SARS-CoV-2, showing the importance of using computational tools in drug design and development, as well as in teaching practice.

针对新药向民众推广过程中面临的重重困难，探寻简化研发流程的途径至关重要。计算机模拟（in silico）方法可作为实验室湿实验的替代方案，因其兼具高效性与低成本而被广泛应用。本研究旨在药物化学课程中开设一项理实结合的教学活动，让学生运用结构建模与分子对接（molecular docking）知识，提出针对新型冠状病毒（SARS-CoV-2）病毒分子靶点的生物活性化合物。课堂分组开展本次教学活动，每组需提出一款候选药物，其前体均为天然分子。本研究共设计并测试7种衍生物，针对不同的大分子靶点，随后对其理化性质开展计算机模拟预测。从结合能指标来看，所有衍生物的对接结果均表现优异，其中GEND的结合能达-9.0 kcal mol⁻¹。此外，这些原型化合物与对应靶点的氨基酸之间存在良好的相互作用，尤以KAED、QUED与GEND为甚，且其理化性质符合Lipinski规则（Lipinski restrictions）。综上，本研究至少筛选出3种新型冠状病毒潜在抑制剂，证明了计算工具在药物设计研发以及教学实践中的重要价值。