Introduction:
Glioblastoma (GBM), the most aggressive form of brain cancer, exhibits formidable resistance to treatment despite exhaustive therapeutic efforts due to its extensive heterogeneity, and infiltrative nature, resulting in a poor patient prognosis marked by frequent tumor recurrence1. Nanomedicines represent an innovative approach to cancer treatment, leveraging nano-sized carriers to enhance drug delivery and improve therapeutic outcomes by exhibiting better cell-specific toxicity against lesions, minimizing off-target effects, and reducing systemic toxicity2. Still, a main challenge is the extrapolation of in vitro data to clinically relevant information. Computational models serve as robust representations of biological systems, functioning as ‘virtual laboratories’, offering researchers a platform to illuminate intricate mechanistic aspects of diseases such as GBM and scenarios resulting from fundamental cell hypotheses and the assessment of novel therapeutic approaches that may be difficult to test in vivo. A tumor growth model simulating GBM is presented for evaluation of nanomedicine formulations' effects.

Methods:
A hybrid discrete-continuous mathematical approach is adopted. Cells are described as discrete variables following biologically-inspired rules. Tumor microenvironment, including drug concentration, is described as a continuous variable. We incorporate diffusion gradients and spatial competition (e.g. mechanical cell-contact inhibition) among cancer cells to accurately mimic 3D in vitro growth. By fitting the model parameters with experimental data, we simulate various treatment schedules to optimize therapeutic efficacy. We use data from bortezomib-loaded nanoparticles as an example in GBM therapy, considering three different scenarios of pharmacological action: 1) cell-cycle arrest, 2) apoptosis, and 3) mixed-effects.

Results:
Our simulations translate dose-response curves obtained from monolayer experiments into a probabilistic representation of cell susceptibility to drug-induced effects, factoring in both drug dosage and exposure duration. We demonstrate optimized treatment scheduling of nanomedicine formulations to restrain tumor growth, incorporating information from biological experiments. We present results regarding the effect of bortezomib-loaded nanoparticles in relation to temozolomide, the commonly administered chemotherapy drug for GBM.

Conclusions:
This study highlights the potential of pharmacological models to advance our understanding on GBM therapy, particularly through the optimization of nanomedicine formulations. By leveraging computational simulations treatment schedules and therapeutic outcomes are assessed offering insights into personalized medicine approaches for GBM. The incorporation of pharmacokinetic and toxicokinetic data can further enhance the accuracy and predictive power of our models. By integrating these data, we can refine our simulations to better mimic real-world scenarios, advancing research for novel nanomedicines.

Conflicts of interest:
The authors declare that they have no conflict of interest in the publication of this article. The authors have no conflicts of interest to report in this work. Abstract was not submitted elsewhere and was first published here.

Funding:
This research received no external funding from any funding agency in the public, commercial, or not-for-profit sectors.