Abstract:
Medicinal plants containing complex mixtures of several compounds with various potential
beneficial biological effects are attractive treatment interventions for a complex multi-faceted disease
like diabetes. In this study, compounds identified from African medicinal plants were evaluated for
their potential anti-diabetic activity. A total of 867 compounds identified from over 300 medicinal
plants were screened in silico with the DIA-DB web server (http://bio-hpc.eu/software/dia-db/) against
17 known anti-diabetic drug targets. Four hundred and thirty compounds were identified as
potential inhibitors, with 184 plants being identified as the sources of these compounds. The plants
Argemone ochroleuca, Clivia miniata, Crinum bulbispermum, Danais fragans, Dioscorea dregeana, Dodonaea
angustifolia, Eucomis autumnalis, Gnidia kraussiana, Melianthus comosus, Mondia whitei, Pelargonium
sidoides, Typha capensis, Vinca minor, Voacanga africana, and Xysmalobium undulatum were identified
as new sources rich in compounds with a potential anti-diabetic activity. The major targets
identified for the natural compounds were aldose reductase, hydroxysteroid 11-beta dehydrogenase
1, dipeptidyl peptidase 4, and peroxisome proliferator-activated receptor delta. More than
30% of the compounds had five or more potential targets. A hierarchical clustering analysis
coupled with a maximum common substructure analysis revealed the importance of the flavonoid
backbone for predicting potential activity against aldose reductase and hydroxysteroid 11-beta
dehydrogenase 1. Filtering with physiochemical and the absorption, distribution, metabolism,
excretion and toxicity (ADMET) descriptors identified 28 compounds with favorable ADMET
properties. The six compounds—crotofoline A, erythraline, henningsiine, nauclefidine, vinburnine,
and voaphylline—were identified as novel potential multi-targeted anti-diabetic compounds, with
favorable ADMET properties for further drug development.
Description:
Supplementary Materials:
Table S1—SMILES notations of all compounds evaluated in the study. Table S2—Assigned numerical identity of predicted active compounds, their plant sources and predicted targets. Figure S1—Individual predicted active compound–protein target networks. Table S3—Plants having scientific anti-diabetic evidence and evidence of traditional use only identified by virtual screening and their predicted bioactive compounds. Figure S2—Dendrograms of hierarchical clustering analysis.
