Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa.
School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana.
Int J Mol Sci. 2018 May 25;19(6):1578. doi: 10.3390/ijms19061578.
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.
自古以来,人们就认识到植物的治疗特性。许多病理状况都使用植物衍生的药物进行治疗。这些药物被用作制剂或浓缩植物提取物,而没有分离出活性化合物。然而,现代医学需要分离和纯化一种或两种活性化合物。然而,癌症、退行性疾病、艾滋病和糖尿病等全球性健康挑战仍然存在,现代医学难以提供治愈方法。很多时候,分离“活性化合物”会使化合物失效。药物发现是一个多维问题,需要评估天然和合成化合物的几个参数,如安全性、药代动力学和疗效,以在候选药物选择期间进行评估。人工智能等最新技术的出现增强了药物设计假设,使用“芯片上器官”和微流控技术,意味着自动化已成为药物发现的一部分。这导致药物发现速度加快,候选化合物的安全性、药代动力学和疗效得到评估,同时允许基于天然化合物的新型药物设计和合成方法。最近在分析和计算技术方面的进展为处理复杂天然产物开辟了新途径,并利用其结构衍生出新的创新药物。事实上,我们正处在应用于天然产物的计算分子设计时代。预测计算软件有助于发现天然产物及其衍生物的分子靶标。在未来,量子计算、计算软件和数据库在模拟分子相互作用以及预测药物开发所需的特征和参数(如药代动力学和药效动力学)方面的应用,将导致药物开发中很少出现假阳性先导化合物。本文讨论了基于植物的天然产物药物发现,以及创新技术如何在下一代药物发现中发挥作用。