大象传媒

This project focuses on the utilization of聽newly developed technology for transcriptomic analysis of archival kidney biopsies and investigates which molecular features of HN increase biopsy accuracy and/or can be recovered in serum and urine specimens. Then, statistical machine learning methods will be developed to integrate these omics-based findings with routine clinical and laboratory patient features to define diagnostic and/or prognostic panels. Individual panel factors will also serve as novel drug targets for the identification of drugs that we can repurpose to treat HN. We will evaluate the biological effect of these drugs in vitro using the new kidney-on-chip technology, which we will refine to better simulate HN. We will further investigate the drugs with the best in vitro effect for their capability to attenuate the progression of HN in silico using pharmacometrics disease progression modelling. Finally, we will design a clinical trial that will begin after this project.

This project will deliver patient-tailored management of HN by improving the accuracy of renal biopsy, developing a non-invasive diagnostic/prognostic HN test allowing early diagnosis and sequential disease monitoring of disease course, and by identifying novel medical therapy.