Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor. Its evolution is governed by complex and dynamic interactions between tumor cells and their microenvironment, which are difficult to reproduce in vivo. As a result, in vitro approaches—particularly microfluidic devices—are widely employed to enable controlled experimental conditions. Although in vitro systems constitute a valuable approach, efficiently quantifying the multiple factors involved in these phenomena remains a ubiquitous challenge, thus motivating the integration of such experiments with mathematical modelling. Following this approach, the work in [1] presents a mathematical model describing the response of tumour cells to their environment. However, exploration and evaluation of GBM dynamics across all physiological and phenomenological parameter ranges requires solving a transient, non-linear coupled PDE system which depends on many material parameters, hence, computational demands rapidly increase. Reduced order modelling (ROM), and in particular, Proper Orthogonal Decomposition (POD) [2] provides an effective strategy to accelerate parametric exploration. By projecting the solution onto a low-dimensional subspace created from representative full-order snapshots, the number of degrees of freedom can be drastically reduced thus alleviating the computational demand. Despite the growing use of ROM in biomechanics, their application to transient, non-linear and strongly coupled PDEs where variables present very different scales remains elusive. Therefore, GBM models constitute a challenging ground on which ROM methodologies can be assessed and the boundaries of their applicability can be examined. In this present work, the applicability of ROM in GBM modelling is tested in a 1D setting. By using a minimally modified version of Matlab’s function pdepe, we are able to obtain a POD-based reconstruction of the high-fidelity solution. This work constitutes a first step towards a computationally efficient ROM model, useful for efficient evaluation across different parameter ranges.