Alexander Vahrmeijer

LUMC

In short, the research of Alexander Vahrmeijerentails the following:

Computationally designed peptide-based probes: Artificial intelligence-enhanced targeting of pancreatic cancer biomarkers for tumor visualization to support diagnosis and surgery (AI-PEP)

Pancreatic cancer remains one of the most lethal malignancies, mainly because of the lack of early symptoms. Surgical resection, combined with chemo(radio)therapy, is currently the only potentially curative treatment. However, even after a complete surgical resection, survival rates remain low due to incomplete tumor resections and local tumor recurrence. Complete intraoperative removal of the tumor tissue is the cornerstone of successful treatment across pancreatic cancer types, including the rare subtypes.

Using real-time fluorescence-guided surgery, surgeons can more precisely identify and visualize the tumors during surgery. Indocyanine green is an example of a fluorophore that accumulates in tumor tissue and is used for real-time fluorescence-guided surgery. However, as a non-targeted fluorophore, its specificity in pancreatic cancer is limited. Therefore, tumor-targeted near-infrared fluorescent probes are required to improve intraoperative imaging.

The aim of this project is the development of peptide-based probes with a high affinity and specificity for pancreatic tumors using computational modelling approaches. Multiple in silico tools will be applied to predict peptide interactions with tumor-associated biomarkers and to design novel peptide-based probes. This framework supports the development of near-infrared fluorescent probes with improved biochemical stability and target selectivity. The resulting probes are intended to support improved precision of fluorescence-guided surgery and support surgical management of pancreatic cancer.