LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

Abstract

In order to accelerate the isolation and characterisation of structurally new or novel secondary metabolites, it is crucial to develop efficient strategies that prioritise samples with greatest promise early in the workflow so that resources can be utilised in a more efficient and costeffective manner. We have developed a metrics-based prioritisation approach using exact LC-HRMS which uses data for 24,618 marine natural products held in the PharmaSea database. Each sample was evaluated and allocated a metric score by a software algorithm based on the ratio of new masses over the total (sample novelty), ratio of known masses over the total (chemical novelty), number of peaks above a defined peak area threshold (sample complexity), and peak area (sample diversity). Samples were then ranked and prioritized based on these metric scores. To validate the approach, 8 marine sponges and 6 tunicate samples collected from the Fiji Islands were analysed, metric scores calculated and samples targeted for isolation and characterisation of new compounds. Structures of new compounds were elucidated by spectroscopic techniques, including 1D and 2D NMR, MS and MS/MS. Structures were confirmed by Computer Assisted Structure Elucidation methods (CASE) using the ACD/Structure Elucidator Suite.

Characterization of Anti-Inflammatory and Antioxidant Constituents from Scutellaria baicalensis Using LC-MS Coupled with a Bioassay Method

“…Natural products, which have a variety of chemical structures and are produced by diverse organisms (microorganisms, plants, animals, and humans), possess potential therapeutic properties and are fascinating drug leads [1]. The isolation of active components from natural products is normally achieved by bioassay-guided purification methodologies, but this often results in higher rates of unnecessary reisolation and dissipation of bioactive compounds from repeated processes [1,2].…”

Innovative omics-based approaches for prioritisation and targeted isolation of natural products – new strategies for drug discovery

“…Their size is compatible with concomitant metabolite proling and mining and with the capacities of tools currently available for NP prioritisation studies as discussed below. However, to maximise the chances of discovering new bioactive NPs from complex mixtures, a systematic preliminary rapid enrichment step of CNEs using sequential extractions, 15 solid phase extraction (SPE) 16 or coarse generic semi-preparative HPLC fractionation 17 seems necessary. Such enrichment procedures may improve biological and chemical proling screening steps.…”

“…Another metrics-based prioritisation strategy has recently been developed. 16 The methodology is based on the use of a dedicated tool integrated with the LC-HRMS database allowing for the novelty, complexity and diversity of samples to be ranked according to various calculated metric scores. It has been applied to a set of eight marine sponges and to six tunicate samples previously fractionated by solid-phase extraction (SPE).…”

“…It has been applied to a set of eight marine sponges and to six tunicate samples previously fractionated by solid-phase extraction (SPE). 16 Although the study presents some limitations, the proposed strategy's efficiency was demonstrated through the isolation and characterisation of two new eudistomins and two new nucleosides. In this context, biochemometrics approaches, which rely on the use of statistical modelling tools to correlate metabolite proles with biological datasets, are very useful for assigning a biological activity to a particular compound detected from complex mixtures.…”

Bacterial pathogens: threat or treat (a review on bioactive natural products from bacterial pathogens)

“…Furthermore, improvements in analytical platforms (mass spectrometry, NMR) coupled with recent advancements in metabolomics enable the detection and identication of compounds in minute quantities from complex biological samples. [359][360][361] Application of recent machine learning tools for structure recognition, bioactivity prediction, drug-target interactions 362 such as the NMR-based Small Molecule Accurate Recognition Technology (SMART 2.0) 363 further accelerates the drug discovery process.…”