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构建印度尼西亚草药成分数据库及其在随机森林模型中的应用,以寻找醛糖还原酶抑制剂。

Construction of an Indonesian herbal constituents database and its use in Random Forest modelling in a search for inhibitors of aldose reductase.

机构信息

Institute of Pharmaceutical Science, Franklin-Wilkins Building, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom.

出版信息

Bioorg Med Chem. 2012 Feb 1;20(3):1251-8. doi: 10.1016/j.bmc.2011.12.033. Epub 2011 Dec 30.

Abstract

Data on phytochemical constituents of plants commonly used in traditional Indonesian medicine have been compiled as a computer database. This database (the Indonesian Herbal constituents database, IHD) currently contains details on ∼1,000 compounds found in 33 different plants. For each entry, the IHD gives details of chemical structure, trivial and systematic name, CAS registry number, pharmacology (where known), toxicology (LD(50)), botanical species, the part(s) of the plant(s) where the compounds are found, typical dosage(s) and reference(s). A second database has been also been compiled for plant-derived compounds with known activity against the enzyme, aldose reductase (AR). This database (the aldose reductase inhibitors database, ARID) contains the same details as the IHD, and currently comprises information on 120 different AR inhibitors. Virtual screening of all compounds in the IHD has been performed using Random Forest (RF) modelling, in a search for novel leads active against AR-to provide for new forms of symptomatic relief in diabetic patients. For the RF modelling, a set of simple 2D chemical descriptors were employed to classify all compounds in the combined ARID and IHD databases as either active or inactive as AR inhibitors. The resulting RF models (which gave misclassification rates of 21%) were used to identify putative new AR inhibitors in the IHD, with such compounds being identified as those giving RF scores >0.5 (in each of the three different RF models developed). In vitro assays were subsequently performed for four of the compounds obtained as hits in this in silico screening, to determine their inhibitory activity against human recombinant AR. The two compounds having the highest RF scores (prunetin and ononin) were shown to have the highest activities experimentally (giving ∼58% and ∼52% inhibition at a concentration of 15μM, respectively), while the compounds with lowest RF scores (vanillic acid and cinnamic acid) showed the lowest activities experimentally (giving ∼29% and ∼44% inhibition at a concentration of 15μM, respectively). These simple virtual screening studies were thus helpful in identifying novel inhibitors of AR, but yielded compounds with only very modest (micromolar) potency.

摘要

已将有关传统印度尼西亚医学常用植物的植物化学成分的数据编译成计算机数据库。该数据库(印度尼西亚草药成分数据库,IHD)目前包含在 33 种不同植物中发现的约 1000 种化合物的详细信息。对于每个条目,IHD 都提供了化学结构、俗名和系统名、CAS 注册号、药理学(已知情况下)、毒理学(LD(50))、植物物种、化合物所在植物部位、典型剂量和参考文献的详细信息。还为具有已知对抗醛糖还原酶(AR)活性的植物衍生化合物编译了第二个数据库。该数据库(醛糖还原酶抑制剂数据库,ARID)包含与 IHD 相同的详细信息,目前包含 120 种不同的 AR 抑制剂信息。使用随机森林 (RF) 建模对 IHD 中的所有化合物进行虚拟筛选,以寻找针对 AR 的新型先导化合物,为糖尿病患者提供新的症状缓解形式。对于 RF 建模,使用一组简单的 2D 化学描述符将 ARID 和 IHD 联合数据库中的所有化合物分类为 AR 抑制剂的活性或非活性。由此产生的 RF 模型(错误分类率为 21%)用于识别 IHD 中的潜在新 AR 抑制剂,这些化合物被鉴定为 RF 评分>0.5 的化合物(在三种不同的 RF 模型中开发的每一种)。随后,对作为该计算机筛选命中的四种化合物进行了体外测定,以确定它们对人重组 AR 的抑制活性。两种具有最高 RF 评分的化合物(prunetin 和 ononin)在实验中显示出最高的活性(在 15μM 浓度下分别抑制约 58%和 52%),而 RF 评分最低的化合物(香草酸和肉桂酸)在实验中显示出最低的活性(在 15μM 浓度下分别抑制约 29%和 44%)。这些简单的虚拟筛选研究有助于识别 AR 的新型抑制剂,但产生的化合物仅具有非常适度(微摩尔)的效力。

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