Scientists have unveiled a groundbreaking method to test how thousands of active substances influence cellular metabolism simultaneously.
By using high-throughput metabolomics and mass spectrometry, they identified unexpected effects of existing medications, paving the way for repurposing drugs and accelerating drug discovery. This approach could one day align patient-specific metabolic data with tailored treatments.
Understanding Active Substances and Cell Metabolism
How do active substances affect metabolic processes in cells? Answering this question could unlock valuable insights for developing new medications. However, investigating how a library of compounds interacts with cellular metabolism has historically been a resource-intensive task.
Now, researchers from the Department of Biomedicine at the University of Basel have introduced a groundbreaking method for testing the metabolic effects of thousands of substances simultaneously. Their findings, based on a technique called high-throughput metabolomics, were published today (January 28) in the journal Nature Biotechnology.
Predicting Side Effects and Drug Interactions
“When we have a better understanding of exactly how active substances intervene in cell metabolism, the development of medication can be accelerated,” explains Professor Mattia Zampieri. “Our method provides additional characterization of the substances, from which we can infer possible side effects or interactions with other medications.”
The researchers, led by Dr. Laurentz Schuhknecht, lead author of the study, grew cells in thousands of little wells in cell culture plates. They then treated the cells in each well with one of over 1500 substances from a compound library, and used a method called mass spectrometry to measure how thousands of small biomolecules inside the cells (known as metabolites) change upon treatment.
This allowed the research team to gather data on the changes of over 2000 metabolic products in the cells for each active compound. They then compared these changes with those obtained from untreated cells via computer-aided analysis. This resulted in an overview of the effects on cell metabolism of each active substance, which gave them a very accurate picture of its respective mode of action.
Surprising Discoveries in Drug Mechanisms
“Commercially available drugs can influence cell metabolism much more than we had imagined,” says Zampieri, summing up the results of the experiments. Particularly of note were the previously unknown modes of action of common medications. For example, the team discovered that tiratricol, a drug for treating a rare condition involving the thyroid gland function, aside its primary mode of action also influences the production of certain nucleotides, the building blocks for DNA synthesis.
“This medication would therefore potentially be a good candidate for a new field of application: modulating nucleotide biosynthesis and hence being used for instance in cancer therapy to inhibit tumor growth,” says Schuhknecht.
Leveraging Data for AI-Driven Drug Design
Comprehensive data from high-throughput methods such as this, can help train artificial intelligence for designing new medications. “Our long-term vision is to match patient-specific metabolic profiles of a disease with the mode of metabolic interference of thousands of compound candidates to unravel the best medication able to revert the molecular changes induced by the disease,” says Zampieri.
In order to get closer to this vision, it is not only important to understand the action of the substances on metabolism, the pharmacologist emphasizes. It is equally important how the human body processes the active substances and thus how it changes their effect. The scientists are therefore conducting further research to examine the interaction between the body and active substances more closely.
Reference: “A human metabolic map of pharmacological perturbations reveals drug modes of action” by Laurentz Schuhknecht, Karin Ortmayr, Jürgen Jänes, Martina Bläsi, Eleni Panoussis, Sebastian Bors, Terézia Dorčáková, Tobias Fuhrer, Pedro Beltrao and Mattia Zampieri, 28 January 2025, Nature Biotechnology.
DOI: 10.1038/s41587-024-02524-5
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