This investigation aims to evaluate strategies for an efficient selection of bioactive compounds from the multitude and biodiversity of the plant kingdom. Statistics prove natural products (NPs) as a source leading most consistently to successful development of new drugs. However, there are several reasons why the interest in finding bioactive NPs has generally declined at several major pharmaceutical companies. Their substantial argument is that the research in this field is time-consuming, highly complex and ineffective. A more rational and economic search for new lead structures from nature must therefore be a priority in order to overcome these problems. In this paper, different strategies are described to exploit the molecular diversity of bioactive secondary metabolites, namely classical pharmacognostic approaches and computational methods. The latter include various data mining tools, like virtual screening filtering experiments using pharmacophore models, docking studies, and neural networks, which help to establish a relationship between chemical structure and biological activity. The strengths and weaknesses of these methods will be shown in this review. Focusing on selected targets within the arachidonic acid cascade (phospholipase A2, 5-lipoxygenase, cyclooxygenase-1 and -2), several studies of successful discoveries in the field of anti-inflammatory NPs were scrutinized for the applied strategies. Both the compilation of relevant published data and recent studies supported by our own research clearly demonstrate the benefits of the synergistic effect of a hybridization of these strategies for an effective drug discovery from natural ingredients.
Keywords: Drug discovery, natural products, virtual screening, chemometrics, pharmacophore modeling, cyclooxygenase, lipoxygenase, anti-inflammatory drugs
Rights & PermissionsPrintExport