Application of the Rumsfeld matrix to anticancer natural product target discovery

Abstract

For a long time, natural products (NP) contribute to the treatment of human pathologies, notably to cancer treatment with microbial and plant-derived products. The mechanism of action of new NP can be investigated using computational methods, boosted by artificial intelligence-assisted procedures, to guide target discovery. But there remain many bioactive NP with unknown targets and/or an incompletely understood or opaque mechanism of action. The most innovative compounds are those with a previously unknown chemical scaffold associated to an unknown mechanism of action, despite evidence of bioactivities in pharmacological assays. This challenging “unknown unknown” category of compounds requires major efforts to elucidate their mechanism of action, with the possibility to identify unprecedented first-in-class approaches to treat advanced cancers. There are also chemically well-known NP for which novel properties and medicinal applications are revealed without an associated target. Such compounds belong to the “known unknown” group, as it is the case for the anticancer drug etoposide and its potent anti-inflammatory action exploited to treat lymphohistiocytosis. The situation is different with new scaffolds for which a potential mechanism or molecular target can be predicted on the basis of functional analogies with other molecules. The “known unknown” and “unknown known” products can be classified using a Rumsfeld ignorance matrix by categorizing them into four subgroups. A Johari window-type classification of NP and associated targets is proposed. The matrix can help compound management and identification of research gaps to generate insights for further study. The review retraces a scientific excursion into the unknown of NP pharmacology.