Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor. Its evolution is governed by complex and time-dependent interactions between tumor cells and their surrounding environment, which are difficult to replicate in vivo. Hence, in vitro approaches—particularly microfluidic devices—are widely employed to enable controlled experimental conditions. However, an accurate description of the rich interactions between the different agents involved remains challenging, therefore encouraging the integration of mathematical modelling tools. Following this approach, the work in [1] presents a mathematical model describing the response of tumour cells to their environment. However, comprehensive 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, leading to a rapid increase of computational costs. 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, hence reducing the computational effort. 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 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 function, a POD-based reconstruction of the high-fidelity solution is obtained. This contribution constitutes an initial step towards a computationally efficient ROM model, suited to explore a wide range of parameter values.