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整个雨林都可以在制药行业的实验室进行筛选。

[An entire rain forest can be screened at pharmaceutical industry's laboratories].

作者信息

Rosell S

机构信息

Astra, Södertälje.

出版信息

Lakartidningen. 1997 Dec 17;94(51-52):4938-41.

PMID:9454020
Abstract

The pharmaceutical industry has long been heavily reliant on natural products, and today more than half of the twenty best-selling pharmaceuticals are derived from natural sources. Hitherto sample collection from fauna and flora has been based on an ethnobotanical approach involving traditional healers and oral histories of indigenous peoples as sources of information on folk uses of plants and organisms. In the future, however, random sampling combined with automated high-throughput screening (HTS) may come to the forefront in drug design, enabling extensive libraries of active compounds to be built up, and rendering local knowledge largely irrelevant. New technological advances in chemistry, molecular biology and data processing are combined in automated systems whereby HTS, based on bioassay-guided fractionation procedures, is used to isolate active compounds, and enabling the chemical structure of isolated compounds to be determined within 24 hours. The combination of the speed of HTS, and a remarkable decrease in the amount of sample required for the isolation and structure determination of natural compounds, has improved our ability to find unique natural products for drug development. Moreover, it has already been demonstrated that, owing to the relatively minute amounts of material required for HTS, this random sampling approach to bioprospecting for the purposes of drug will benefit efforts to conserve such sensitive biosystems as the rain forests and marine ecosystems. Thus, this technology would seem to be more compatible with the needs of modern drug design in the pharmaceutical industry that the ethnobotanical approach.

摘要

制药行业长期以来一直严重依赖天然产物,如今二十种最畅销的药品中有一半以上源自天然资源。迄今为止,从动植物中采集样本一直基于一种民族植物学方法,该方法涉及传统治疗师以及原住民的口述历史,以此作为关于植物和生物体民间用途的信息来源。然而,未来随机抽样与自动化高通量筛选(HTS)相结合可能会在药物设计中占据主导地位,从而能够建立大量的活性化合物文库,并使当地知识在很大程度上变得无关紧要。化学、分子生物学和数据处理方面的新技术进步在自动化系统中得以结合,通过基于生物测定引导分级分离程序的高通量筛选来分离活性化合物,并能在24小时内确定分离出的化合物的化学结构。高通量筛选的速度与天然化合物分离和结构测定所需样品量的显著减少相结合,提高了我们为药物开发寻找独特天然产物的能力。此外,已经证明,由于高通量筛选所需的材料量相对较少,这种用于药物生物勘探的随机抽样方法将有利于保护雨林和海洋生态系统等敏感生物系统。因此,这项技术似乎比民族植物学方法更符合制药行业现代药物设计的需求。

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