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When attempting to screen plant-matrixes, the large dynamic range and diversity of metabolites still hamper their identification and biological correlation. So far, however, the chemical and biological potential of these medicinal plants remains little explored. To date, the natural product database NAPRALERT has reported over 152 plant genera with a historical record of antimalarial properties, thus offering unlimited possibilities for the identification of novel hits/targets (Graham and Farnsworth 2010 Mojab 2012 Wells 2011). A recent review reporting the new drugs available on the market during the last 34 years showed that approximately 60% of these new antiparasitic drugs have a natural origin/pharmacophore (Newman and Cragg 2016). In this context, natural products provide a high degree of lead-/drug-similarity, remaining undoubtedly the best source of native drugs or structural templates for antimalarial compounds development (Cargnin et al. The need to discover new prototypes of drugs is thus very important. In addition, malaria therapy faces some emerging problems such as parasite multidrug resistance, mosquito resistance to insecticides and the shortage of the time and money required for the development of new synthetic and natural leads (Achan et al. Despite efforts to develop vaccines, the antigenic variability of these parasites allows only a partial and decreasing protection against clinical malaria and only in children and infants (Olotu et al. Even if a reduction of 37% cases has been recently reported, it remains the most severe parasitic disease worldwide, particularly for children under the age of five and pregnant women (World Health Organisation (WHO) 2018). Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.Įvery year several 100 million of people get malaria, 1.2 million of which die. NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. OPLS–DA based on Keetia species correlated triterpene signals to K. Unsupervised 1H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. The extracts of twigs and leaves of Keetia species were measured by 1H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation. ObjectiveĬreate a multivariate model to identify antiplasmodial metabolites from 1H NMR data of two African medicinal plants, Keetia leucantha and K. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products. The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine.