Metals are considered by many as toxic to humans and therefore unsuitable to be used in medicine. In the Frei Lab we are working towards changing this perception by developing novel metal-based antimicrobials to fight the global antibiotic resistance crisis.
In the Frei Lab we are exploring new classes of metal complexes for their antimicrobial potential.
One focus is the elucidation of the Mode of Action of these compounds. Understanding this will help us design even better metalloantibiotics
We are exploring metal-based photosensitizers for aPDT and investigating their potential to avoid resistance in bacteria
To efficiently explore the vast metal-complex space we are adapting machine-learning based solutions to coordination compounds
The WHO warns that “without urgent, coordinated action“, 10 million people will die each year due to antimicrobial resistance (AMR) by 2050. If this becomes reality, AMR would surpass cancer as the leading cause of death in the western world and become deadlier than the current COVID-19 pandemic. COVID-19 is likely to have significantly accelerated the spread of AMR as the use of antibiotics has skyrocketed in the last 18 months with some reports describing indiscriminate prescription of broad spectrum antibiotics to all hospitalized patients.
Despite this urgency, there are currently only 43 antibiotics in clinical trials, with a minority representing new compound classes, none of which are effective against critical Gram-negative pathogens. In comparison, there are currently over 1000 compounds in clinical trials as cancer treatments and over 5000 clinical trials have been initiated for potential COVID-19 treatment since 2020.
The vast majority of drugs today are purely organic molecules consisting of elements that make up less than 10% of the periodic table. Transition metals can form coordination compounds with organic ligands, forming highly diverse three-dimensional structures, exceeding the covalent limitations of a typical carbon atom. The use of metal complexes as drugs was initiated by the discovery and application of the anticancer drug cisplatin in 1978, with analogues still used in the majority of chemotherapeutic treatments today. Since then, several human clinical trials have been conducted with drugs containing metals such as titanium, iron, copper, gallium, ruthenium, palladium, silver, gold, or bismuth (with several more in development), proving that metal complexes are viable as non-cytotoxic drug candidates. One of the unique characteristics of metal complex drugs is their access to many different and unique modes of actions, ranging from redox reactions, generation of reactive oxygen or catalytic generation of other active species, ligand exchange or triggered ligand release, to general competitive or covalent inhibition of enzymes or binding to proteins. Many of these are difficult or even impossible to achieve with organic compounds alone.
In recent studies we have established that metal complexes are potentially better candidates for antibiotics than purely organic compounds. A comparative analysis of a library containing antimicrobial data on 300'000 compounds, including ~1000 metal complexes showed that the latter had a 10x higher hit-rate. At the same time we could show that the metal complexes did not display higher in vitro toxicity rates compared to organic molecules, contradicting the notion of general metal toxicity.
Since then we have started to investigate several metal complex classes more closely, gathering more data on their activity against a broader panel of drug-resistant bacteria, as well as preliminary in vivo data. In our current work we are both exploring new metal complexes for their antimicrobial potential as well as investigating the Mode of Action of our lead compounds to guide further development.
Almost all antibiotics are optimised to hit a single molecular target within a bacterium and hit it effectively. While this makes them potent, this is also their Achilles’s heel, as it applies strong selective pressure for mutations that alter the target or remove the antibiotic from inside the bacterial cell. Photodynamic therapy (PDT) functions via the localized generation of reactive oxygen species (ROS) by a photosensitizer (PS) when it is irradiated with specific wavelengths of light. Precise targeting of the PS to diseased tissue ensures that the irradiation event delivers ROS to a tightly defined area, leading to promiscuous damage of multiple cellular targets. PDT is already successfully used in cancer therapy in clinics today. The application of these principles in the design of next-generation antimicrobial agents (antimicrobial PDT; aPDT) is a nascent research area with exciting potential.
We are working towards the preparation of targeted and efficient metal-based photosensitizers for aPDT against localized bacterial infections.
Chemical space is incredibly vast. Estimates put the number of “drug-like” organic molecules at around 1060, while excluding all transition metals. It is easy to see that if one iterates all possible ligands over all possible metals the permutations are nearly endless. Given this daunting uncharted space of metal complexes, an efficient and strategically guided approach to its exploration is required. Machine Learning algorithms have enabled many advances in the fields of chemical exploration and medicine but have only sparingly been applied to metal complexes and barely at all in the field of inorganic medicinal chemistry. We aim to use state-of-the art machine learning algorithms together with newly developed representations for metal complexes to efficiently explore the metal complex space for antimicrobial activity.
Metals in Medicine
Machine Learning in Antibiotic Research
The Frei Lab is hiring! We have a 1 year postdoc position open. If you are interested in the discovery and understanding of new metalloantibiotics using robots and machine learning please apply.
DMs & eMail open for questions.
Please mention your concrete previous research experience as well as why you are interested in working on metalloantibiotics in the cover letter!
Check out our recent two preprints to get an idea of what we are doing: Combinatorial Synthesis of 420 MnCO3 complexes (MnCO3 Preprint) and ML applied to metalloantibiotics (Machine Learning Preprint)