DZL Scientist Fabian Theis and DZL/ILH Scientist Herbert Schiller (DZL site Munich, CPC-M) and Boehringer Ingelheim have started a research collaboration to identify new avenues for the treatment of idiopathic pulmonary fibrosis. They could improve outcomes in people living with this severe and progressive lung disease. Supported by around 1 million euro in funding from Boehringer Ingelheim, the project will apply and further develop single-cell genomics techniques and AI models to analyze drug effects on cultured human lung tissue outside the body.
Combining Experimental and Computational Expertise
Idiopathic pulmonary fibrosis, short IPF, leads to scarring of lung tissue, resulting in a loss in lung function. This affects patients’ ability to perform daily activities and can even be life-threatening. While current antifibrotic treatments can slow disease progression, there is a high remaining patient need with no treatments capable of stopping or reversing the disease available to date. This collaboration aims to help close this gap by developing a more physiologically relevant platform for drug discovery, using human lung tissue combined with single-cell transcriptomic analysis and AI models.
At the CPC-M/Helmholtz Munich, two research groups are leading the collaboration: the Research Unit for Precision Regenerative Medicine headed by lung expert Prof. Herbert Schiller, and the Institute for Computational Biology, led by biomedical AI specialist Prof. Fabian Theis.
The Schiller Lab has developed advanced methods to analyze drug effects at single-cell resolution in precision-cut human lung slices (hPCLS). This approach will now support pre-clinical testing of new anti-fibrotic candidates. “By working directly with human lung tissue and integrating advanced computational models, we hope to gain a detailed understanding of the cellular circuits driving fibrosis and identify novel ways to target them,” says Schiller.
Meanwhile, the Theis Lab contributes its expertise in machine learning and biomedical AI. The team develops models that analyze and predict the effects of drug candidates at single-cell resolution. “This collaboration allows us to combine experimental biology and data science to better predict how potential drugs, and their combinations might behave in complex human tissues,” Theis explains.
