Ludovica Aiello 1, Gabriele Ceccarelli 2, Roberta Listro 3, Simona Collina 3, Elena Maria Tosca 1, Paolo Magni 1
1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia (Pavia, Italy), 2 Division of Human Anatomy, Department of Public Health, Experimental and Forensic Medicine, University of Pavia (Pavia, Italy), 3 Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia (Pavia, Italy)
Introduction: Glioblastoma (GBM) is an aggressive brain tumor with no effective cure. Temozolomide (TMZ) remains the standard chemotherapeutic agent; however, its clinical benefit is limited by modest efficacy and rapid drug resistance. These limitations highlight the need for novel therapeutic strategies. Sigma receptors are overexpressed in GBM cells and represent a promising pharmacological target. RC-106, a sigma receptor modulator, has demonstrated antiproliferative activity in two-dimensional (2D) cultures [1]. Before advancing to in vivo studies, its anti-tumor efficacy should be further evaluated in three-dimensional (3D) in vitro models, such as tumor spheroids, which better recapitulate tumor architecture and microenvironmental gradients. However, quantitative approaches for evaluating drug efficacy in spheroid models remain limited. Mathematical modeling and simulation (M&S) offer a robust framework to quantitatively analyze 3D tumor growth dynamics and treatment response, thereby strengthening preclinical evaluation [2] .
Objectives: This study aimed to develop a mathematical model describing tumor growth and the antitumor effects of RC-106 and TMZ in GBM spheroids exploiting volume data.
Methods: GBM spheroids were generated from U87 cells using ultra-low attachment (ULA) plates to promote 3D aggregation. Two independent studies were carried out in which spheroids were exposed to RC-106 (10, 17, and 30 μM) and to TMZ (25 and 51 μM). Spheroids were monitored for 19 days using bright-field microscopy. Images were analyzed using a previously developed neural network for automated spheroid segmentation [3]. The resulting masks were processed through a 3D rendering algorithm to extract quantitative morphological features including spheroid volume [4]. Then, the Simeoni tumor growth inhibition (TGI) model [6] was applied to describe the time profile of spheroid volumes in absence or presence of RC-106 and TMZ treatment. The antitumor effect of the two drugs was modeled as a killing effect on cancer cells that depends on drug concentration in the well through an Hill model for RC106 and Emax model for TMZ. Individual volumes from all the treatment arms of both studies and treatments were analyzed simultaneously. A combine residual error model was adopted. All analyses were performed using R, Python and Monolix.
Results: The use of neural network-based significantly facilitated image segmentation, reducing manual workload and improving the robustness of the resulting masks. Using the ReViSP software, 3D spheroid structures were successfully reconstructed from the 2D bright-field images, generating tumor volume time- subsequently used for modeling purposes. The proposed model adequately described the unperturbed tumor growth in the control groups and the non-linear treatment effects for both compounds. To ensure model identifiability, n_RC106 and k1_TMZ parameters were fixed during the estimation procedure. The estimated model parameters were: λ0 = 0.49 [𝑑𝑎𝑦−1], λ1 = 0.016 [mm^3/𝑑𝑎𝑦−1], W0 = 0.0012 [mm^3], k1_RC106 = 0.39 [𝑑𝑎𝑦−1], k2_ RC106 = 0.57[(μMgiorni)−1], EC50_RC106 = 20.83 [μM], n_RC106= 20 [(μMgiorni)−1], k1_TMZ = 0.39 [𝑑𝑎𝑦−1], k2_TMZ = 0.57 [(μMgiorni)−1], EC50_TMZ= 13.17 [μM]. From the estimated model parameters, the minimum concentration threshold ensuring tumor eradication (Ct_RC106=((λ0_RC106*(EC50_RC106)^(n_RC106))/(k2_RC106-λ0_RC106))^(-1/n_RC106) and Ct_TMZ= (λ0_TMZ*EC50_TMZ)/ (k2_TMZ-λ0_TMZ)) was calculated.
For RC106, the obtained Ct_RC106=22.8 μM is consistent with independent experimental evidence indicating that a clear antitumor effect emerges at this concentration [7]. Differently, for TMZ, the results indicated that no concentration can ensure complete tumor eradication. Overall, the results suggest that RC106 may represent an effective therapeutic option for the treatment of GBM, exhibiting greater inhibitory activity than the current gold-standard therapy, TMZ, on tumor spheroids and supporting its potential therapeutic advantage in this experimental setting. From a methodological perspective, this study represents one of the first applications of the Simeoni TGI model to volumetric data derived from 3D spheroid experiments. These findings further extend the applicability of the Simeoni model, which is already well established for the analysis of xenograft data and in 2D in vitro culture systems.
Conclusions: Building upon the seminal work of Simeoni et al., a TGI model was successfully developed to describe the antitumor efficacy of RC-106 and TMZ in GBM spheroids, quantitatively demonstrating the superior inhibitory activity of RC106 compared to the current gold-standard treatment. This approach highlights the potential of M&S to derive quantitative measurements of anticancer drug efficacy using 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 3R guidelines [6].
References:
[1] R. Listro, S. Stotani, G. Rossino, M. Rui, A. Malacrida, G. Cavaletti, M. Cortesi, C. Arienti, A. Tesei, D. Rossi, M. Di Giacomo, M. Miloso, and S. Collina, “Exploring the RC-106 Chemical Space: Design and Synthesis of Novel (E)-1-(3-Arylbut-2-en-1-yl)-4- (Substituted) Piperazine Derivatives as Potential Anticancer Agents,” Frontiers in Chemistry, vol. 8. pp. 945; 2020.
[2] E. M. Tosca, D. Ronchi, D. Facciolo, and P. Magni, “Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment,” Biomedicines, vol. 11, no. 4, pp. 1058, 2023.
[3] M. Streller, S. Michlíková, W. Ciecior, K. Lönnecke, L. A. Kunz Schughart, S. Lange, and A. Vos-Böhme, “Image segmentation of treated and untreated tumor spheroids bAAy Fully Convolutional Net works,” “ArXiv”, vol. 2405.01105, May 2024.
[4] I. De Santis, E. Tasnadi, P. Horvath, A. Bevilacqua, and F. Piccinini, “Open-Source Tools for Volume Estimation of 3D Multicellular Aggregates,” Applied Sciences, vol. 9, no. 8, pp. 1616,2019.
[5] M. Simeoni, P. Magni, C. Cammia, G. De Nicolao, V. Croci, E. Pesenti, M. Germani, I. Poggesi, and M. Rocchetti, “Predictive Pharmacokinetic-Pharmacodynamic Modeling of Tumor Growth Kinetics in Xenograft Models after Administration of Anticancer Agents,” Cancer Reserch, vol. 64, no.3, pp. 1094–110, 2004.
[6] JJ. Han, “FDA Modernization Act 2.0 allows for alternatives to animal testing,” Artificial Organs, vol. 47, no. 3, pp. 449-450, 2023.
[7] L. Aiello, G. Ceccarelli, D., A. Fantinato, G. Fiorentino, S. Collina, E.M. Tosca, P. Magni, “Quantitative evaluation of RC-106 antitumor activity in GBM spheroids through tumor growth inhibition modeling based on cell counts data”, PAGE 33 (2025) Abstr 11317 [www.page-meeting.org/?abstract=11317].
Reference: PAGE 34 (2026) Abstr 12096 [www.page-meeting.org/?abstract=12096]
Poster: Drug/Disease Modelling - Oncology