Development of an R-language tool to enhance in silico drug discovery from ethnopharmacologically used plant sources: The example of androgenetic alopecia.

Herbal medicines have been, are, and will always be a major asset in drug discovery. A freely available, user-friendly suite of computation tools has been developed to enhance a variety of in silico drug discovery processes. The investigation focused on discovering novel leads for the treatment of androgenetic alopecia (AGA) from natural sources already used ethnopharmacologically. A set of twenty-two R code snippets (termed ‘Tool Services’) was created. Tool Services focus on collecting, manipulating, and analysing data from a variety of sources. These sources include general information, ethnopharmacology, chemistry, pharmacology, targets, diseases, pathways and predictive QSAR modelling data. Sixty-nine plants with established use in AGA were studied and their 2,157 phytochemical ingredients recorded. Taxonomically, more than a third of these plants belong to four families that share many similarities in terms of DNA and phytochemical content. Structural similarity studies on 34 phytochemicals chosen based on their frequency of occurrence in the plants revealed similarities between them and with UV-protectants, vascular protectants and anti-inflammatory agents. Seven drugs currently marketed as monotherapy of AGA were structurally compared against our phytochemicals and were also assessed for drug-drug interactions, side effects, adverse drug reactions, and their metabolic fate. During this phase of the study, studies of alopecia as a side effect and as an adverse drug reaction of drugs were also prepared. A study on 48 targets revealed a strong relation with pathways that are implicated in hair follicle growth and development. More than half of these genes were linked to diseases such as hypotrichosis. Finally, the actual binding sites of these targets and the binding affinities of chemicals for these targets were revealed. Undoubtedly, the androgen receptor (AR) is one of the most studied target in AGA. A QSAR classification model was built for AR using 206 active and 1600 inactive compounds in terms of AR antagonism, utilising both Random Forest and Naïve Bayes algorithms