title: Construction of an Indonesian herbal constituents database and its use in Random Forest modelling in a search for inhibitors of aldose reductase.
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.
authors.affiliation: Institute of Pharmaceutical Science, Franklin-Wilkins Building, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom.
authors.forename: Sadaf
authors.initials: S
authors.lastname: Naeem
authors.forename: Peter
authors.initials: P
authors.lastname: Hylands
authors.forename: David
authors.initials: D
authors.lastname: Barlow
mesh_terms: D000449:Aldehyde Reductase / Q000037:antagonists & inhibitors* / Q000378:metabolism; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D006801:Humans; D007214:Indonesia; D008954:Models, Biological; D010946:Plants, Medicinal / Q000737:chemistry*; D012984:Software
chemical_list: D004791:Enzyme Inhibitors; D000449:Aldehyde Reductase