Modeling tumor growth inhibition in Glioblastoma spheroids after exposure to RC-106, a novel sigma receptors modulator
Ludovica Aiello1, Elena Maria Tosca1, Davide Ronchi1, Gabriele Ceccarelli2, Roberta Listro3, Simona Collina3, Paolo Magni1
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 for which there is no effective cure [1,2]. Therefore, there is an urgent need of identifying innovative drugs for GMB. Sigma receptors (SRs) are overexpressed in GBM cells and represent a promising therapeutic target. Recently, a novel modulator of SRs, RC-106, was identified that showed an in vitro cytotoxic effect on 2D cell cultures of GBM in the micromolar range (IC50=50-64μM) [3]. Before proceeding to animal experimentation, the antitumor activity of RC-106 needs to be further characterized in in vitro tumor models. Spheroids are 3D in vitro cultures emerging as an attractive platform to model human cancer, owing their ability of more faithfully mimicking in vivo tumor features and in vivo treatment response observed in xenograft mice, than 2D cultures. However, a quantitative and robust evaluation method for tumor growth and drug efficacy in spheroids is currently lacking [4].
Objectives: In this work a model describing the tumor growth and the RC-106 anticancer effect in GBM spheroids has been developed. It can be used to derive drug-specific and experiment-independent metrics of drug efficacy, facilitating the prediction of effective exposures to further test in vivo in animal models.
Methods: 3 experiments were carried out. In E1, spheroids were untreated (i.e., control) and in E2 and E3 they were both untreated and treated with RC-106 concentrations, respectively of 30μM and 40μM. Spheroids were monitored for 14 days using bright-field microscopy imaging. Collected images were processed through a semi-automatic pipeline based on a literature-validated rendering software [5] to extract morphological parameters such as volume. 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 treatment, using Monolix. First, the median volumes stratified by study and treated group were analyzed performing, both separate and simultaneous fitting, and considering a proportional error model. Then, the individual volume data from each replicate were modeled using a nonlinear mixed-effects population approach.
Results: Using an optimized version of ReViSP software, spheroid volumes were successfully reconstructed from 2D bright-field images. The obtained tumor volume time-profiles were then used for modeling purpose. On 3 studies, the linear growth phase of the Simeoni growth model was challenging to be identified as it was not observed in the experimental time period. Thus, a reduced Simeoni TGI model, accounting only for the exponential growth phase, was applied. Obtained results were consistent across the 3 experiments and allowed to simultaneously fit the model on all the data introducing only study-dependent W0. Once the adequacy of the reduced Simeoni TGI model was assessed on the median volumes, the population model was developed on 52 individual volume profiles by the three studies. Inter-spheroid variability was added on parameters λ0, K2, W0, and a correlation between λ0 and W0 was also included. Inter-spheroid variability (ω) related to single parameters of the model (λ0, W0, k2, k1), were estimated, demonstrating consistency across studies. In E1, λ0=0.22, W0=0.024 with ω_λ0= 0.017 and ω_W0=0.068. In E2, λ0=0.21, W0=0.005, k1=0.75, and k2=0.011 with ω_λ0 = 0.053, ω_W0 = 0.1 and ω_k2 = 0.44. In E3, λ0=0.16, W0=0.041, k1=1.35 and k2=0.0087 ω_λ0 = 0.04, ω_W0 = 0.12 and ω_k2 = 0.021. Finally, the minimum concentration threshold (Ct= λ0/ K2) to guarantee tumor eradication [7] was computed from the obtained model parameter estimates. The resulted range was [5.43-12.78μM], consistent with effective concentration range identified on 2D U87 cell cultures [3].
Conclusion: A TGI model describing the anticancer effect of RC-106 on U87 GBM spheroids was successfully developed. The proposed approach was able to successfully grasp the unperturbed tumor growth and the anticancer efficacy exerted by RC-106, also identifying Ct values in accordance with efficacy range observed on 2D cell cultures. Results strongly encouraged to explore the potential of modeling and simulation (M&S) to derive quantitative measurements of anticancer drug efficacy from spheroid studies. M&S can contribute to increase the establishment of spheroid models in nonclinical practice and improve in vitro in vivo translatability, embracing the FDA's 3R guidelines [8].
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] S. Collina, E. Bignardi, M. Rui, D. Rossi, R. Gaggeri, A. Zamagni, M. Cortesi, and A. Tesei, “Are sigma modulators an effective opportunity for cancer treatment? a patent overview (1996-2016),” Expert Opinion on Therapeutic Patents, vol. 27, no.5, pp. 565-578, 2017.
[3] M. Rui, D. Rossi, A. Marra, M. Paolillo, S. Schinelli, D. Curti, A. Tesei, M. Cortesi, A. Zamagni, E. Laurini, S. Pricl, D. Schepmann, B. Wunsch, E. Urban, V. Pace, and S. Collina, “Synthesis and biological evaluation of new aryl-alkyl(alkenyl)-4-benzylpiperidines, novel Sigma Receptor (SR) modulators, as potential anticancer-agents,” European Journal of Medicinal Chemistry, vol. 124, pp. 649–665, 2016.
[4] 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.
[5] 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.
[6] 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.
[7] P. Magni, M. Simeoni, I. Poggesi, M. Rocchetti, and G. De Nicolao, “A mathematical model to study the effects of drugs administration on tumor growth dynamics,” Mathematical Biosciences, vol. 200, no. 2, pp. 127–151, 2006.
[8] JJ. Han, "FDA Modernization Act 2.0 allows for alternatives to animal testing," Artificial Organs, vol. 47, no. 3, pp. 449-450, 2023.