Advanced Preclinical Models for Therapeutic Discovery and Validation

To bridge discovery and clinical benefit, the lab develops and uses patient-derived organoids (PDOs). These 3D "mini-tumors" grown from a patient's own tumor tissue, overcome the limitations of traditional 2D cell lines. PDOs offer high fidelity, recapitulating the genetics, histology, and heterogeneity of the original tumor, and can be established with high success rates from small biopsy samples.

The laboratory uses these PDOs as personalized "avatars" for clinical prediction in a high-throughput screening (HTS) environment. Traditional drug screening assays are often limited to a single endpoint, providing a static snapshot of drug effect. To overcome this, the lab employs automated platforms for label-free, time-lapse monitoring of organoid response to therapy.

Using brightfield imaging coupled with AI-driven analysis software, this approach kinetically tracks organoid growth, morphology, and death over time without the need for fluorescent labels that can be toxic or interfere with the assay. This dynamic data provides deeper mechanistic insights, distinguishing between cytotoxic (cell killing) and cytostatic (growth-stopping) drug effects, and can identify the emergence of resistant clones within the organoid population. While PDOs are powerful models of the tumor epithelium, they often lack the full complexity of the tumor microenvironment (TME). To create more physiologically relevant human platforms, the laboratory is developing Organ-on-a-Chip (OOC) models. These microfluidic devices enable the co-culture of patient-derived organoids with other critical cell types, including endothelial cells to form vascular networks, immune cells, and cancer associated fibroblasts. This approach recapitulates the 3D architecture and dynamic conditions of the TME, such as fluid flow and shear stress, allowing for more accurate studies of drug delivery, immune cell infiltration, and tumor-stroma interactions. The high-throughput nature of OOC platforms offers new opportunities for large-scale drug screening and personalized medicine, providing a more relevant human platform for preclinical testing.