News of the week: novel method to improve potency of potential drugs

Find out more about some of the top stories in medicinal chemistry this week, including novel therapeutic targets to treat malaria and machine learning technology to predict reaction yields.

Go to the profile of Jasmine Harris
Feb 23, 2018
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The news highlights:

Potential therapeutic targets could form basis of novel antimalarial drugs
Novel method could improve potency of drugs to treat cancer
Advances in the development of orally available peptide drugs
Predicting reaction yields with machine learning technology

Potential therapeutic targets could form basis of novel antimalarial drugs 

Scientists at The Francis Crick Institute (London, UK) have discovered a potential new therapeutic target for the treatment of malaria by understanding how the parasites escape red blood cells and infect other cells. The research, published in Nature Microbiology, identifies two key proteins required for this process.

"Over 400,000 people die of malaria each year, and resistance to common antimalarial drugs is growing," commented Mike Blackman, Group Leader at the Francis Crick Institute, who led the research. "We're studying the deadliest malaria parasite, Plasmodium falciparum, to try to find new drug targets that work in a different way to existing treatments."

The proteins, SERA6 and SUB1, were shown by genetic knockout experiments to be essential for the parasite to be able to escape red blood cells.

Sources: 
Thomas JA, Tan MYS, Bisson C, et al. A protease cascade regulated release of the human malaria parasite Plasmodium falciparum from host red blood cells. Nature Microbiology (2018) doi: 10.1038/s41564-018-0111-0; https://www.crick.ac.uk/news/science-news/2018/02/20/scientists-find-new-antimalarial-drug-targets/

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Novel method could improve potency of drugs to treat cancer 

Scientists at The Scripps Research Institute (CA, USA) have developed a method to simultaneously modify dozens of molecules with the aim of improving their potencies. Using a ‘click chemistry’ reaction, Sulfur (VI) Flouride Exchange, the team transformed phenol groups to fluorosulfate in 39 cancer drugs.

‘Click chemistry’ is a term for simple molecular reactions that occur in one vessel, generating one stable product at high yield. The research team demonstrate that the sulfuryl fluoride gas required for this exchange can be dissolved in an organic solvent to make a liquid form of the reagent, enabling it to be used in high-throughput experiments.

"Usually you have to make thousands or millions of molecules and go through a big screening process to find one or two molecules that are interesting and might work," explains Peng Wu, one of the study's lead authors. "With this new approach, you can save time and money by starting with drugs and molecules you know are already active and asking whether a quick modification makes any of them any better."

"Our results suggest we will be able take a drug and make it more potent, faster acting, and hopefully with better bioavailability," adds Nobel laureate K. Barry Sharpless, who co-led the study. 

Sources:
Liu Z, Li J, Li S, et al. SuFEx click chemistry enabled late-stage drug functionalization. J. Am. Chem. Soc. (2018) doi: 10.1021/jacs.7b12788; https://www.scripps.edu/news/press/2018/20180216wu_sharpless.html

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Advances in the development of orally available peptide drugs

A group led by Horst Kessler, Professor of Chemistry at the Technical University of Munich (Munich, Germany) have shown how a methylated peptide hexamer can be used hijack existing transport systems to allow its uptake through the intestinal cell wall. The team used this method to administer an integrin binding motif, which could be used for future cancer therapeutics. 

Read more on MedChemNet

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Predicting reaction yields with machine learning technology

Researchers from Princeton University (NJ, USA)  have developed machine learning software to accurately predict reaction yields in reactions varying up to four components. This will enable chemists to identify high-yielding combinations of reactants and prevent unnecessary expenditure of time and resources when synthesizing compounds.

"The software that we developed can work for any reaction, any substrate," remarked Abigail Doyle, leader of the study. "The idea was to let someone apply this tool and hopefully build on it with other reactions."

"Many of these machine learning algorithms have been around for quite some time," concluded Jesús Estrada, a graduate who contributed to the research and the paper. "However, within the synthetic organic chemistry community, we really haven't tapped into the exciting opportunities that machine learning offers." 

Sources:
Ahneman DT, Estrada JG, Lin S, Dreher SD and Doyle AG, Predicting reaction performance in C-N cross-coupling using machine learning. Science (2018) doi: 10.1126/ science.aar5169; https://www.eurekalert.org/pub_releases/2018-02/pu-cha021318.php

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Go to the profile of Jasmine Harris

Jasmine Harris

Digital Editor, Future Science Group

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