A new artificial intelligence tool has been created in Germany that could significantly reduce the number of animals used in preclinical drug research. Researchers at Goethe University Frankfurt and Philipps University of Marburg, working with the Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), have developed a generative AI system called genESOM, designed to cut the use of non‑human animals in early‑stage drug testing.
The study by Jörn Lötsch, Benjamin Mayer, Natasja de Bruin, and Alfred Ultsch has been published in the journal Pharmacological Research, under the title “Self-organizing neural network-based generative AI with embedded error inflation control enhances effective knowledge extraction from preclinical studies with reduced sample size.”
The tool is trained on relatively small experimental datasets and then generates additional synthetic data points that mirror real laboratory results closely enough to be used in place of many live‑animal experiments. According to the scientists involved, this approach could reduce the number of animals used when testing new active ingredients by 30 to 50%.
GenESOM has been conceived within a wider context of growing scientific and ethical criticism of animal experimentation, especially over how well such tests translate to human outcomes. Regulators and the pharmaceutical sector are increasingly interested in alternative testing methods, not only to spare animals but also to tackle the high dropout rates in clinical trials that make drug development slower and more expensive. Government bodies in several countries are beginning to support this shift, and in Germany, the federal centre BfR3R, which coordinates the development of animal testing alternatives, is actively promoting replacement and reduction strategies throughout preclinical research. Prof. Jörn Lötsch, a data scientist at Goethe University and one of the authors of the study describing genESOM, said, “[genESOM] can make an important contribution to reducing the number of animal experiments in large areas of preclinical research.”
