Not Drug-like, but Like Drugs: Cnidaria Natural Products.

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210-265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <-7 in 19% of the Cnidaria natural products.

Identification of Ecdysone Hormone Receptor Agonists as a Therapeutic Approach for Treating Filarial Infections.

BACKGROUND: A homologue of the ecdysone receptor has previously been identified in human filarial parasites. As the ecdysone receptor is not found in vertebrates, it and the regulatory pathways it controls represent attractive potential chemotherapeutic targets. METHODOLOGY/ PRINCIPAL FINDINGS: Administration of 20-hydroxyecdysone to gerbils infected with B. malayi infective larvae disrupted their development to adult stage parasites. A stable mammalian cell line was created incorporating the B. malayi ecdysone receptor ligand-binding domain, its heterodimer partner and a secreted luciferase reporter in HEK293 cells. This was employed to screen a series of ecdysone agonist, identifying seven agonists active at sub-micromolar concentrations. A B. malayi ecdysone receptor ligand-binding domain was developed and used to study the ligand-receptor interactions of these agonists. An excellent correlation between the virtual screening results and the screening assay was observed. Based on both of these approaches, steroidal ecdysone agonists and the diacylhydrazine family of compounds were identified as a fruitful source of potential receptor agonists. In further confirmation of the modeling and screening results, Ponasterone A and Muristerone A, two compounds predicted to be strong ecdysone agonists stimulated expulsion of microfilaria and immature stages from adult parasites. CONCLUSIONS: The studies validate the potential of the B. malayi ecdysone receptor as a drug target and provide a means to rapidly evaluate compounds for development of a new class of drugs against the human filarial parasites.

Virtual target screening: validation using kinase inhibitors.

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.