Glycosaminoglycans and Oligonucleotides Joining Forces to Break Tumor Resistance (GOBREAK)

Purpose and goal:

We developed chemically modified siRNA drugs that were formulation in a hyaluronan coated micelles as well as developed doxorubicin-loaded micelles. We screened a series of drug resistant cancer cell types and determined the CD44 expression levels. With Cy3 as the imaging modality, we obtained confocal images that indicated different intracellular biodistribution of the drug molecules.

These images were used to develop and optimizing the AI tool to develop predictive tools to identify the delivery approach and intracellular localization. Expected results and effects:

We first developed hyaluronan-based delivery formulation as well as non-formulated fluorescently labelled drug molecules. Developed drug formulation for both oligonucleotide drugs (siRNA) as well as for small molecule drug (doxorubicin or DOX). The cellular uptake was quantified by fluorescence-based cell sorting experiments as well as confocal microscopy.

These images were used to develop AI-based imaging platform. Partners from Frankfurt developed drug resistant cell lines that also overexpress CD44 receptors. Approach and implementation:

Design & implementation of the project involved collaborative efforts between researchers in nanotechnology, cancer biology, and machine learning. The rigorous experimental design and delivery approaches allowed the integration of machine learning algorithms into the analysis pipeline, enabling data-driven insights and predictive modeling. Major takeaways from the project, are the development of drug formulation, drug resistant cell lines as well as image-based AI models for screening intracellular biodistribution of the drug molecules.