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An investigation of the antileishmanial properties of semi-synthetic saponins.

ABSTRACT: Leishmaniasis is a neglected tropical disease caused by insect-vector borne protozoan parasites of the, Leishmania species. Whilst infection threatens and affects millions of the global poor, vaccines are absent and drug therapy limited. Extensive efforts have recently been made to discover new leads from small molecule synthetic compound libraries held by industry; however, the number of new chemical entities identified and entering development as anti-leishmanials has been very low. This has led to increased interest in the possibility of discovering naturally derived compounds with potent antileishmanial activity which may be developed towards clinical applications. Plant-derived triterpenoid and steroidal saponins have long been considered as anti-microbials and here we describe an investigation of a library of 137 natural (9) and semi-synthetic saponins (128) for activity against Leishmania mexicana, a causative agent of cutaneous leishmaniasis. The triterpenoid sapogenin, hederagenin, readily obtained in large quantities from Hedera helix (common ivy), was converted into a range of 128 derivatives. These semi-synthetic compounds, as well as saponins isolated from ivy, were examined with a phenotypic screening approach to identify potent and selective anti-leishmanial hits. This led to the identification of 12 compounds, including the natural saponin gypsogenin, demonstrating high potency (ED₅₀ < 10.5 ?M) against axenic L. mexicana amastigotes, the mammalian pathogenic form. One of these, hederagenin disuccinate, was sufficiently non-toxic to the macrophage host cell to facilitate further analyses, selectivity index (SI) > 10. Whilst this was not active in an infected cell model, the anti-leishmanial properties of hederagenin-derivatives have been demonstrated, and the possibility of improving the selectivity of natural hederagenin through chemical modification has been established.

SUBMITTER: Anderson O

PROVIDER: S-EPMC7651632 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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An investigation of the antileishmanial properties of semi-synthetic saponins.

Anderson Orlagh O Beckett Joseph J Briggs Carla C CC Natrass Liam A LA Cranston Charles F CF Wilkinson Elizabeth J EJ Owen Jack H JH Mir Williams Rhodri R Loukaidis Angelos A Bouillon Marc E ME Pritchard Deiniol D Lahmann Martina M Baird Mark S MS Denny Paul W PW

RSC medicinal chemistry 20200609 7

Leishmaniasis is a neglected tropical disease caused by insect-vector borne protozoan parasites of the, <i>Leishmania</i> species. Whilst infection threatens and affects millions of the global poor, vaccines are absent and drug therapy limited. Extensive efforts have recently been made to discover new leads from small molecule synthetic compound libraries held by industry; however, the number of new chemical entities identified and entering development as anti-leishmanials has been very low. Thi ...[more]

PMID: 33479679

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Project description:BackgroundGenetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.ResultsIn this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.ConclusionsWe developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm.

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An investigation of the antileishmanial properties of semi-synthetic saponins.

Publications

An investigation of the antileishmanial properties of semi-synthetic saponins.

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