Dr. Alessio Gamba is a researcher in computational toxicology at the University of Liège in Belgium, where he is working on the Horizon 2020 ONTOX project to develop Physiological Maps and ontologies. He holds a degree in Biotechnology and a Master’s degree in Functional Genomics and Bioinformatics from the University of Milan in Italy, where he also completed courses in computer science and systems biology, learning various approaches and programming languages used in computational biology.
His international research experience includes work on computational genome-scale metabolism at the University of Madrid, supported by an Erasmus fellowship, as well as studies on metabolic analysis and omics data during a research period at Oxford Brookes University.
During his Ph.D. in Biochemical Sciences at the University of Milan, Dr. Gamba specialized in the biophysical study of protein interactions, collaborating with the Systems Biology Lab at the Mario Negri Institute to explore the connections between protein complexes and genetic diseases. He later joined Dr. Emilio Benfenati’s lab at the Mario Negri Institute, where he focused on computational toxicology and the development of QSAR and PBPK models applied to New Approach Methodologies (NAMs).
Since 2021, Dr. Gamba has been contributing to the ONTOX project, working in Prof. Liesbet Geris’s lab at the University of Liège, where he applies systems biology and physiology-based computational approaches to study chemical toxicities.
The ONTOX Physiological map and its use in model development for better understanding chemical toxicities in the kidney
Alessio Gamba1, Luiz Ladeira1, Devon A. Barnes2, Manoe J. Janssen2, Rosalinde Masereeuw2, Liesbet Geris1,3, Bernard Staumont1
1. Biomechanics Research Unit, GIGA Molecular and Computational Biology, University of Liège, Liège, Belgium;
2. Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands;
3. Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.
The kidney filters blood and maintains the body's chemical balance through the nephron, its essential functional unit. Understanding nephron toxicities is crucial for chemical and drug safety. Advances in machine learning, like QSAR and read-across approaches, have led to predictive models for toxicological endpoints, including nephrotoxicity. However, these models often lack insight into biological mechanisms of action.
To address this, we developed a systems biology approach within the H2020 ONTOX project (Vinken, M. et al. 2021), creating a Nephron Physiological Map (PM) and two related disease ontologies. These tools collect toxicological data and present it in a user-friendly graphical interface. The PM, built using CellDesigner software and Systems Biology Graphical Notation, depicts biological processes and interactions. Inspired by the Disease Maps community (Mazein, A. et al. 2018), our workflow includes data extraction from literature and databases, followed by expert review. The PM covers genes, proteins, and metabolites in pathways like urine production and vitamin D metabolism, with a focus on drug transporters.
The ontologies extend the PM to study kidney crystallopathy and tubular necrosis, organizing knowledge to identify gene-disease associations, drug targets, and pathways. They help in suggesting new in vitro tests and in developing AOPs and PBPK models. As a New Approach Methodology (NAM), these maps aim to reduce animal testing. The PM and ontologies are visualized using the MINERVA platform, enabling easy navigation and toxicity identification.
By providing a detailed view of cellular and molecular processes, the PM and ontologies enhance understanding of toxicological mechanisms. This innovative approach can improve the accuracy of toxicological predictions, offering new insights into human toxicities and advancing next-generation risk assessment.
Acknowledgement: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 963845. ONTOX is part of the ASPIS project cluster.
