In silico
Meetings & conferences
HelpathonsHealthToxicologyIn silico
ONTOX Hackathon: Hack To Save Lives And Avoid Animal Suffering
Artificial Intelligence (AI) in toxicology – a potential driver for reducing or replacing laboratory animals in the future. ONTOX project is looking for solutions and innovative ideas to move forward. Are you going to help ONTOX to hack into these complex challenges?
The hackathon will be held from 21 to 23 April 2024 in Utrecht Science Park. The whole event is open to a diverse community of forward-thinkers and problem-solvers interested in the intersection of AI and ethical toxicology. The goal is to bring together passionate individuals who seek innovative solutions to critical challenges in toxicology.
Read more about the hackathon and register here (https://ontox-project.eu/hackathon/).
Innovation examples
HealthInnovationIn silico
Using data and computational modelling in biomedical research
Bioinformatics and systems biology hold great promise to translate the wealth of biological data into meaningful knowledge about human health and disease. The group of Bas Teusink helps biologists to deal with high throughput data, for example metabolomics (how cell metabolism works) and proteomics (how protein networks work) from patient material or cell cultures. This can help to better understand disease mechanisms and aid drug targeting or personalised medicine. In the future, combining data from different models (in vitro, in vivo and human data) could become a digital model of humans, or a “ digital twin”.
Click on the link in the video to watch more or read the interview with Bas (and Jaap Heringa) he[https://vu.nl/en/research/more-about/using-data-and-computational-modelling-in-biomedical-research]re.
Meetings & conferences
HealthIn vitroIn silicoAdvanced
A hybrid in silico-in vitro cardiorespiratory simulator for medical device testing
Cardiovascular medical devices (CMDs) (e.g. artificial hearts, ventricular assist devices, ECMO, heart valves) support the cardiac and/or the respiratory function of patients. Large challenges are encountered when assessing CMDs interaction with the human body and the effects on the heart and vessels. Especially CMDs with new designs require an extensive evaluation concerning their effectiveness and safety under different pathophysiological conditions. We propose a high fidelity cardiorespiratory simulator for the testing of the hemodynamic performance of CMDs. The proposed simulator merges the flexibility of the in silico system with a hydraulic interface to test CMDs. As such, the simulator embeds a high fidelity cardiorespiratory model, allowing the reproduction of pathologies at both cardiac and respiratory level. The simulator works as a test bench for the assessment of CMDs, from prototype stage to pre-clinical stage. Thanks to its flexibility and high-fidelity, the simulator helps reducing animal testing and provides insights on how to improve CMD design to better suit different patient’s needs.
Contact: https://www.kuleuven.be/wieiswie/en/person/00098489
RE-place database: https://www.re-place.be/method/cardiovascular-modelling-medical-device-testing
Expert interviews
In silico
Brett Lidbury, The Australian National University: Using machine learning to predict human health
Brett Lidbury is associate professor at the Research School of Population Health of The Australian National University. He applies machine learning to make predictions about health using human big data rather than animal experiments. For more information, go to www.anu.edu.au and search “Lidbury”.
Expert interviews
ToxicologyIn silicoPolicy
Tox 21: A New Way to Evaluate Chemical Safety and Assess Risk
Tox21 is a US federal research collaboration focused on driving the evolution of Toxicology in the 21st Century by developing methods to rapidly and efficiently evaluate the safety of commercial chemicals, pesticides, food additives/contaminants, and medical products. The goals of Tox21 are to (1) identify mechanisms of chemically-induced biological activity; (2) prioritize chemicals for more extensive testing; and (3) develop more relevant and predictive models of in vivo toxicological responses.