Research Tim Van De Looverbosch

Abstract

Glioblastoma (GBM) remains one of the most lethal and treatment‑resistant cancers, notorious for its extensive cellular heterogeneity and plasticity. Conventional therapies, which primarily target rapidly dividing cells, often fail to eliminate the resilient glioma stem‑like cell populations that fuel recurrence. Addressing this unmet clinical need requires a paradigm shift beyond traditional viability‑based screening toward therapeutic strategies that deliberately exploit and manipulate cellular plasticity. The Lab of Cell Biology and Histology (UA) and the OncoRNA Lab (UG) have joined forces in a coordinated valorisation trajectory aimed at launching a drug discovery spin‑off focused on next‑generation GBM combination therapies based on an innovative switch‑and‑kill paradigm. This concept relies on smart pairing of compounds that first push GBM cells into vulnerable states ("switch") and subsequently eliminate them effectively ("kill"). The initiative has already gained substantial momentum, supported by secured IOF funding at UG for high‑throughput hit identification and a confirmed investment intent. The STRIKE‑3D project (acronym for Switch‑TRIggered Kill Engine in 3D) is designed to establish the translational backbone of the future spin‑off by bridging the critical gap between high‑throughput discovery and clinical relevance. Hereto, we will evaluate compounds in patient‑derived GBM tumour models using a robust high‑content profiling pipeline. This workflow will act as a standardized engine to rigorously de‑risk therapeutic candidates by assessing efficacy, specificity, and safety in physiologically relevant human systems. Within this project, we will deploy this engine to validate a prioritized portfolio of cell‑state‑aware lead candidates, including three promising switch‑and‑kill combinations already identified in preliminary screens. By confirming the performance of these hits, we will generate a high‑value asset package ready for early commercialization while simultaneously validating the predictive power and scalability of our discovery approach. Successful completion of STRIKE‑3D will deliver a reproducible, data‑driven, and translational pipeline, positioning the spin‑off as a specialized innovator in precision oncology with a sustainable engine for continuous asset generation.

Researcher(s)

• Promoter: De Vos Winnok
• Co-promoter: Van De Looverbosch Tim

Research team(s)

• Laboratory of cell biology and histology

Project type(s)

• Research Project

Abstract

Despite technological improvements, drug discovery programs have become less successful and more expensive over time. This can in part be attributed to the rigid implementation of sub-optimal preclinical screening platforms that mainly use simple cell cultures, and toxicity and pharmacokinetics experiments with animal models. Organoids are the promise of next-generation model systems for preclinical research. The main roadblocks for organoid adoption are their lack of reproducibility and the absence of technology to characterise them in depth. We believe that robust and reproducible organoid production and analysis can only be guaranteed when organoids are characterized in toto with cellular resolution. With this project, we intend to develop a pipeline for fast cellular phenotyping of intact organoids and prepare for launching a spin-off company that offers this as a service platform to the pharma and biotech industry.

Researcher(s)

• Promoter: De Vos Winnok
• Co-promoter: Sijbers Jan
• Fellow: Van De Looverbosch Tim

Research team(s)

• Laboratory of cell biology and histology

Project type(s)

• Research Project

Abstract

Within the classical drug discovery pipeline, early target selection and compound validation are based on simple readouts from technologies that average across large populations of cells. This strategy negates much of the total information content in the biological sample at hand, causing selection bias and attrition of promising leads. High-content microscopy holds large potential for refined mode-of-action (MoA) analysis of pharmaco-genomic perturbations. An especially information-rich readout can be obtained with Cell Painting (CP), a pipeline that is implemented in our lab and consists of automated microscopy and morphological analysis of cells stained with inexpensive fluorescent dyes. The resulting cell phenotypic signatures can be used to predict the MoA of compound treatments with high fidelity. However, by design, predictions are limited to known MoA encountered in the dataset. Furthermore, confounding factors, such as experimental noise and intercellular heterogeneity may obscure relevant biological properties. Hence, we envision a more comprehensive MoA documentation by adding a complementary information layer based on transcriptomics of the same cell culture at hand. To this end, we have teamed up with the OncoRNA lab of Prof. Mestdagh (University of Ghent), who has developed a cost-effective platform for parallelized shotgun transcriptomics, which offers high genome coverage. Together, we intend to deploy the combination of CP and transcriptomics for systematic gene silencing screens based on CRISPRi technology. As proof-of-concept, we will perform a targeted knockdown screen for a set of genes with known MoA in a panel of disease-relevant cell lines. By associating specific genes with simultaneous changes in cell morphology and gene expression profile, we aim to establish an enrichment analysis that allows unbiased MoA prediction. We will offer this platform as a service to biotechnology and pharmaceutical companies seeking to enhance their preclinical R&D lines. At the same time, we will build biological data capital, with which we intend to redesign the target discovery process and position ourselves in the vanguard of data-driven biotech at the European level.

Researcher(s)

• Promoter: De Vos Winnok
• Co-promoter: Van De Looverbosch Tim

Research team(s)

• Laboratory of cell biology and histology

Project type(s)

• Research Project