Towards polypotent natural products: The Derringer desirability approach and nonparametric ranking for multicriteria evaluation of essential oils

Abstract

Chemical complexity of natural products often results in their pharmacological polypotency. However, selecting a natural product with desirable activity profile is not a straightforward task, especially if optimization of one feature results in deterioration of other facets. Recently, in the field of multiobjective optimization, the sum of ranking differences (SRD) has emerged as a simple and statistically sound method for fusion of multiple criteria. However, the data pretreatment seems to strongly influence the ranking outcome, which may lead to ambiguous or even false interpretations. Therefore, in the present study, the data of 55 essential oils originated from different plant species and tested on multiple bacterial and fungal strains as well as 4 antioxidative assays were studied. Essential oils were ranked using the classical Deringer desirability approach, and results were compared with the SRD analysis of primary activity data as well as the previously row-wise standardized data, normalized data, and the data scaled to fit the preferences. Ultimately, the most promising candidate (polypotent) essential oils, as well as the most resilient and most sensitive bacterial and fungal strains, and antioxidative assays were identified. Data transformation based on the Deringer desirability approach, compared with the data that have not been previously transformed or those that transformed using a row-wise standardization or normalization to the unit length vector, seemed to be the crucial step providing the sound and meaningful SRD ranking. The data of 55 essential oils tested against multiple bacterial and fungal strains and 4 antioxidative assays were studied. Oils were ranked, according to their polypotency, using the classical Derringer desirability approach, and results were compared with ranking by the sum of ranking differences (SRD). Eventually, the SRD performed on the data scaled to fit the preferences proved to be crucial step in finding the most polypotent samples, as well as the most resilient and sensitive bacterial and fungal strains.