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New workflow revolutionizes the discovery of candidate drugs from nature

San Diego, Calif., March 22, 2018 — Researchers at the University of California San Diego Center for Microbiome Innovation have developed a new workflow to accelerate the discovery of drug candidate molecules present in nature. The method fills a gap in the current process and cuts down on the amount of time it takes to identify candidate drug molecules. A paper describing the method was published recently in Journal of Natural Products.

Nature has inspired some most drugs we know best—from medicinal drugs like penicillin (mold) and the anti-inflammatory aspirin (willow bark) to morphine (Opium poppy). Believe it or not, there is a single scientific process behind the discovery of natural products that serve as leads for drug development, and it generally consists of the following steps: extraction of molecules from the biomass; separation of the molecules; screening of the molecules for any that might have an effect on another living thing (called “bioactive”); isolation and identification of those molecules. 

These steps have been in use by everyone from chemists and pharmacologists to academic and industrial researchers since the early 1900s, but each step can be prone to costly failure.

Louis-Félix Nothias, postdoctoral researcher in the laboratory of Skaggs School of Pharmacy and Pharmaceutical Sciences professor Pieter Dorrestein, says this method can fail at multiple points in the process due to things like degradation or low concentration of bioactive molecules.

“It’s important to identify the bioactive molecules early on in the process to decrease the likelihood of missing them,” said Nothias. “Unsuccessful attempts to retrieve bioactive molecule or the obtention of already known molecules are frequent outcomes of these efforts.”

The latter problem has been partially solved by searching for the presence of known molecules before starting the procedure. In fact, the recent deployment of the crowd-sourced Global Natural Product Social molecular networking Web-platform (GNPS) has made it possible for researchers to do this with massive amounts of data in a just a few hours.

Now, a team of international researchers has developed a workflow that fills in even more gaps by identifying bioactive candidate molecules prior to in-depth investigation. Nothias is the first author on the “Editors’ Choice” paper describing the new method, called bioactive molecular networking, which combines the classic approach to drug-discovery with bioinformatics tools on GNPS to accelerate the discovery of new drugs from nature.

To validate the new workflow, the researchers used a dataset obtained from a previous study of Euphorbia dendroides L., a Mediterranean tree spurge, in which they were looking for molecules that displayed activity against chikungunya virus.

Chikungunya virus is an emerging virus that’s causing massive outbreaks in South America and the Caribbean, for which no antiviral therapy has been found so far.

Using the classic, stepwise method, no bioactive compounds were found. Using the new method, however, researchers were able to isolate two compounds with demonstrated antiviral activity in primate cells.

“Bioactive molecular networking has the retroactive potential to reinvestigate previous efforts where investigators failed to find the active components,” said Nothias. “The number of studies in which this is the case is largely underestimated, since investigators generally don’t report them."

Nothias says the method can be adapted for a number of applications. “For example, it can be used in microbial ecology to annotate compounds impacted by an environmental variable, such as pH, or perhaps it could be used in exposomics to annotate biomarkers associated with a level of exposure to a chemical contaminant.”

The workflow is readily available at https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks

The paper is titled “Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation”.

Additional co-authors include Mélissa Nothias-Esposito, Ricardo da Silva, Mingxun Wang, Ivan Protsyuk, Zheng Zhang, Abi Sarvepalli, Pieter Leyssen, David Touboul, Jean Costa, Julien Paolini, Theodore Alexandrov, Marc Litaudon and Pieter C. Dorrestein.

This work was supported in part by NIH-UC San Diego Center for Computational Mass Spectrometry grant P41 GM103484 and the NIH grant on reuse of metabolomics data R03 CA211211.

About the Center for Microbiome Innovation

The objective of the Center for Microbiome Innovation (CMI) is to accelerate microbiome research and understanding, through partnerships with industry sponsors. Together we will develop novel tools and methods to improve human health and benefit the environment by analyzing and manipulating microbiomes — the distinct and diverse communities of bacteria, viruses and other microorganisms that live within and around us. This is a multidisciplinary center with access to all the latest omics tools (genomics, metagenomics, metatranscriptomics, metabolomics, mutiplex proteomics), processing hundreds of thousands of samples each year and analyzing and collecting data for some of the largest microbiome cohorts in the world. Applications range from human disease understanding, ag bio, pharmaceutical, nutraceutical, environmental research, to consumer goods.

Media Contacts

Erin Bateman
Center for Microbiome Innovation
858-408-6610
ebateman@eng.ucsd.edu

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