Discovery and Development of Metal-Based Antibiotics through a Combinatorial Synthesis and Machine Learning Pipeline

 

Discovery and Development of Metal-Based Antibiotics through a Combinatorial Synthesis and Machine Learning Pipeline

 

Date: 29^th November, 2024 (Friday)

Time: 11:30 am - 12:30 pm (HKT)

 

Venue: Lecture Room 3, Library Extension Building

 

Prof. Angelo Frei

 

Department of Chemistry, Biochemistry and Pharmaceutical Sciences

University of Bern

 

Dr. Angelo Frei is a Junior Group Leader at the University of Bern in Switzerland. He studied Chemistry and Biochemistry at the University of Zurich, where he did his Master Thesis with Prof. Gilles Gasser working on ruthenium polypyridyl complexes as photosensitizers for photodynamic therapy. He got his PhD in 2018 with Prof. Roger Alberto on the development of multifunctional cyclopentadiene ligands for theranostic applications. For his first postdoc he was awarded a Swiss National Science Foundation Early.Postdoc Mobility Fellowship to join the group of Prof. Mark Blaskovich at the University of Queensland in Australia. There he started investigating metal complexes as potential antimicrobial agents. In 2020 he joined the group of Prof. Nicholas Long at Imperial College in London to work on novel radioimaging agents for cancer and bacterial infections before being awarded a SNSF Ambizione Fellowship at the University of Bern in 2022. From 2025 he will be joining the Department of Chemistry at the University of York in the UK as a Lecturer/Assistant Professor in Inorganic Chemistry.

 

His research interests involve the systematic exploration of the transition metal chemical space for compounds with promising (biological) properties such as for example metalloantibiotics and the investigation of their mechanism of action.

 

Antimicrobial resistance (AMR) is already causing over 1 million deaths each year. With the clinical pipeline for novel antibiotics with new modes of actions very sparse, this number is expected to further increase in the coming years. As conventional approaches to bacterial infections are failing to provide novel and effective drugs, alternative treatment modalities need to be considered. Over the last decade, metal-based compounds (metalloantibiotics), have emerged as potential new classes of antimicrobial agents.

 

In this talk I will highlight how our work has recently shown that libraries of metal complexes out-perform purely organic molecules as both antibacterial and antifungal agents. I will show how we employ combinatorial metal complex synthesis to efficiently prepare large libraries of compounds to rapidly identify promising ones with antimicrobial activity. The biological data is then further used to train machine learning models capable of predicting if a given metal compound will be active against bacteria, thereby guiding subsequent synthesis into promising areas of chemical space.