Many drugs fail clinical trials, often because preclinical animal models fall short of replicating human physiology. To improve animal welfare, speed up drug development, and reduce costs, we need to rely less on animal models, while also minimizing the number of failures early in the drug development process. Artificial intelligence and machine learning are powerful tools that can help us achieve these goals by predicting a drug’s efficacy, safety, and uptake in preclinical studies. These technologies can help researchers to make informed decisions and optimize testing strategies, improving drug development for both animals and people.

Footage capturing unacceptable cruelty and abuse on animals in a Spanish testing facility  has recently made headlines and prompted many reactions. The 24th of April is celebrated as the World Day of Animals in Laboratories. Its objective is to promote alternatives to animal testing that are currently revolutionising toxicology, a domain of research where the European Union has significantly increased funding to advance chemical safety assessment.

Nemen computers binnen enkele jaren de 'lijdende' plek van veel proefdieren in? Binnenkort start een internationaal team onder leiding van de Vlaamse toxicoloog Mathieu Vinken (VUB) met de ontwikkeling van zo'n 'proefcomputer'. "Daarmee kunnen op de lange termijn behoorlijk wat proefdieren gespaard worden", hoopt professor Vinken.

Een nieuw internationaal project zal vijf jaar lang alternatieven voor proefdieren onderzoeken. Dankzij artificiële intelligentie zouden op termijn honderdduizenden dieren kunnen worden gespaard in Europa.