Ludovica Aiello1, Gabriele Ceccarelli2, Davide Ronchi1, Andrea Fantinato3, Giulia Fiorentino3, Simona Collina4, Elena Maria Tosca1, Paolo Magni1
1Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 2Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 3Department of Biology and Biotechnology “Lazzaro Spallanzani”, University of Pavia, 4Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia
Introduction: Glioblastoma (GBM) is an aggressive brain tumor for which there is no cure, highlighting the urgent need for new drugs. Sigma receptors, which are overexpressed in GBM cells, represent a promising therapeutic target. RC-106, a sigma receptor modulator, has demonstrated antiproliferative activity in 2D cell cultures [1]. Before advancing to in vivo studies, its anticancer activity must be further investigated using 3D in vitro models such as tumor spheroids, which better recapitulate tumor structure and microenvironment than 2D cultures. However, quantitative methods to assess drug efficacy on spheroids remain limited. Mathematical modeling and simulation (M&S) offer a powerful approach to quantitatively analyze experimental data from 3D in vitro tumor models, enhancing preclinical evaluation and accelerating drug development [2]. Objectives: This study aimed to develop a mathematical model to describe tumor growth and the antitumor effect of RC-106 on GBM spheroids by analyzing how the spheroids cell counts change as a function of treatment. Methods: Spheroids were generated from U87 GBM cell line, seeded in ultra-low attachment (ULA) plates and treated with RC-106 at concentration of 10, 13, 17, 20, and 30 µM. Spheroids were monitored for 35 days by bright-field microscopes images. A pre-existing neural network was used for spheroid segmentation [3]. Obtained masks were then processed with a 3D rendering algorithm to extract morphological parameters such as volume [4]. In addition, at specific time points histological protocols were applied to obtain spheroid sections and derive cell density for all tested experimental conditions. The collected density data were used to develop an exponential model describing the change in spheroids density over time as a function of RC-106 exposures. Tumor volume profiles were normalized to spheroids density, enabling the calculation of cell counts for untreated (control) and treated spheroids. Temporal profiles of cell counts were modeled using a naive pooled approach. A logistic model was selected to describe cell dynamics in control group, as it accounts for growth saturation. The RC-106 antitumor effect was described as proportional to well concentration and a three-compartment transit model representing three progressive levels of cell damage was included to describe delay in cell death [5]. A combined residual error model was applied. All analyses were performed using R, Python and Monolix. Results: The implementation of histological protocols allowed to derive temporal cell counts dynamics, providing a more relevant dataset for quantifying the RC-106 effect compared to spheroid volume. The use of a neural network for bright-field images segmentation optimized processing time and improved rendering method performance for spheroid volume reconstruction. The TGI model accurately described the dynamics of cell counts data in both control and treated groups. The results show that the logistic growth model perfectly fits the unperturbed growth of control groups. For RC-106 treated spheroids, the model captured the decrease in cell growth induced by the antitumor effect of the compound. The model parameters were estimated with a good precision: Zmax = 35176.44 [N] (RSE% = 0.998), ß = 0.31 [?day?^(-1)] (RSE% = 2.97) W0 = 4140 [N] (RSE% = 3.76), k1 = 0.74 [?day?^(-1)] (RSE% = 7.32), k2 = 0.014 [?(µMgiorni)?^(-1)] (RSE% = 1.03). The developed TGI model was then validated against data from previous experiments where spheroids were treated with 10, 20, and 30 µM for 22 days. The agreement between model predictions and observations confirmed the robustness of the model. Conclusions: A TGI model was successfully developed to describe the antitumor efficacy of RC-106 in GBM U87 spheroid cells by analyzing the change in cell counts over time. This approach highlights the potential of M&S to derive quantitative measurements of anticancer drug efficacy using cell counts data from spheroids. M&S can facilitate the adoption of spheroid models in non-clinical practice and improve in vitro-in vivo translatability, in line with the FDA’s 3R guidelines [6].
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Reference: PAGE 33 (2025) Abstr 11317 [www.page-meeting.org/?abstract=11317]
Poster: Drug/Disease Modelling - Oncology