Research article

Bayesian analysis of the effect of exosomes in a mouse xenograft model of chronic myeloid leukemia


  • Received: 08 August 2023 Revised: 19 September 2023 Accepted: 09 October 2023 Published: 24 October 2023
  • The aim of this work is to estimate the effect of Imatinib, exosomes, and Imatinib-exosomes mixture in chronic myeloid leukemia (CML). For this purpose, mathematical models based on Gompertzian and logistic growth differential equations were proposed. The models contained parameters representing the effects of the three components on CML proliferation. Parameters estimation was performed under the Bayesian statistical approach. Experimental data reported in the literature were used, corresponding to four trials of a human leukemia xenograft in BALB/c female rats over a period of forty days. The models were fitted to the following growth dynamics: normal tumor growth, growth with exosomes, growth with Imatinib, and growth with exosomes-Imatinib mixture. For the proposed logistic growth model, it was determined that when using Imatinib treatment the growth rate is 0.93 (95% CrI: 84.33–99.64) slower and reduces the tumor volume to approximately 10% (95% CrI : 8.67–10.81). In the presence of exosome treatment, the growth rate is 0.83 (95% CrI: 1.52–16.59) faster and the tumor volume is expanded by 40% (95% CrI: 25.36–57.28). Finally, in the presence of Imatinib-exosomes mixture treatment, the growth rate is 0.82 (95% CrI: 76.87–88.51) slower and the tumor volume is reduced by 95% (95% CrI: 86.76–99.85). It is concluded that the presence of exosomes partially inactivates the effect of the Imatinib drug on tumor growth in a mouse xenograft model.

    Citation: Rafael Martínez-Fonseca, Cruz Vargas-De-León, Ramón Reyes-Carreto, Flaviano Godínez-Jaimes. Bayesian analysis of the effect of exosomes in a mouse xenograft model of chronic myeloid leukemia[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19504-19526. doi: 10.3934/mbe.2023864

    Related Papers:

  • The aim of this work is to estimate the effect of Imatinib, exosomes, and Imatinib-exosomes mixture in chronic myeloid leukemia (CML). For this purpose, mathematical models based on Gompertzian and logistic growth differential equations were proposed. The models contained parameters representing the effects of the three components on CML proliferation. Parameters estimation was performed under the Bayesian statistical approach. Experimental data reported in the literature were used, corresponding to four trials of a human leukemia xenograft in BALB/c female rats over a period of forty days. The models were fitted to the following growth dynamics: normal tumor growth, growth with exosomes, growth with Imatinib, and growth with exosomes-Imatinib mixture. For the proposed logistic growth model, it was determined that when using Imatinib treatment the growth rate is 0.93 (95% CrI: 84.33–99.64) slower and reduces the tumor volume to approximately 10% (95% CrI : 8.67–10.81). In the presence of exosome treatment, the growth rate is 0.83 (95% CrI: 1.52–16.59) faster and the tumor volume is expanded by 40% (95% CrI: 25.36–57.28). Finally, in the presence of Imatinib-exosomes mixture treatment, the growth rate is 0.82 (95% CrI: 76.87–88.51) slower and the tumor volume is reduced by 95% (95% CrI: 86.76–99.85). It is concluded that the presence of exosomes partially inactivates the effect of the Imatinib drug on tumor growth in a mouse xenograft model.



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    [1] National Cancer Institute, What Is Cancer?, 2023. Available from: https://www.cancer.gov/about-cancer/understanding/what-is-cancer.
    [2] M. Drack, Ludwig von Bertalanffy's organismic view on the theory of evolution, J. Exp. Zool. Part B, 324 (2015), 77–90. https://doi.org/10.1002/jez.b.22611 doi: 10.1002/jez.b.22611
    [3] A. Tsoularis, J. Wallace, Analysis of logistic growth models, Math. Biosci., 179 (2002), 21–55. https://doi.org/10.1016/s0025-5564(02)00096-2 doi: 10.1016/s0025-5564(02)00096-2
    [4] P. K. Maini, M. A. Chaplain, M. A. Lewis, J. A. Sherratt, Special Collection: Celebrating J.D. Murray's Contributions to Mathematical Biology, Bull. Math. Biol., 84 (2021), 1–2. https://doi.org/10.1007/s11538-021-00955-8 doi: 10.1007/s11538-021-00955-8
    [5] A. R. Anderson, M. A. Chaplain, E. L. Newman, R. J. Steele, A. M. Thompson, Mathematical Modelling of Tumour Invasion and Metastasis, J. Theor. Med., 2 (2000), 129–154. https://doi.org/10.1080/10273660008833042 doi: 10.1080/10273660008833042
    [6] A. Philip, B. Samuel, S. Bhatia, S. Khalifa, H. El-Seedi, Artificial intelligence and precision medicine: A new frontier for the treatment of brain tumors, Life, 13 (2022), 24. https://doi.org/10.3390/life13010024 doi: 10.3390/life13010024
    [7] Y. Ochi, Genetic landscape of chronic myeloid leukemia, Int. J. Hematol., 117 (2022), 30–36. https://doi.org/10.1007/s12185-022-03510-w doi: 10.1007/s12185-022-03510-w
    [8] Q. Lin, L. Mao, L. Shao, L. Zhu, Q. Han, H. Zhu, et al., Global, regional, and national burden of chronic myeloid leukemia, 1990–2017: A systematic analysis for the global burden of disease study 2017, Front. Oncol., 10 (2020), 580759. https://doi.org/10.3389/fonc.2020.580759 doi: 10.3389/fonc.2020.580759
    [9] American Cancer Society, Key Statistics for Chronic Myeloid Leukemia, 2023. Available from: https://www.cancer.org/cancer/types/chronic-myeloid-leukemia/about/statistics.html.
    [10] J. A. Adattini, A. S. Gross, N. W. Doo, A. J. McLachlan, Real-world efficacy and safety outcomes of imatinib treatment in patients with chronic myeloid leukemia: An Australian experience, Pharmacol. Res. Perspect., 10 (2022), e01005. https://doi.org/10.1002/prp2.1005 doi: 10.1002/prp2.1005
    [11] T. H. Brümmendorf, J. E. Cortes, D. Milojkovic, C. Gambacorti-Passerini, R. E. Clark, P. L. Coutre, et al., Bosutinib versus imatinib for newly diagnosed chronic phase chronic myeloid leukemia: final results from the BFORE trial, Leukemia, 36 (2022), 1825–1833. https://doi.org/10.1038/s41375-022-01589-y doi: 10.1038/s41375-022-01589-y
    [12] X. Zhang, Y. Yang, Y. Yang, H. Chen, H. Tu, J. Li, Exosomes from bone Marrow microenvironment-derived mesenchymal stem cells affect CML cells growth and promote drug resistance to tyrosine kinase inhibitors, Stem Cells Int., 2020 (2020), 1–13. https://doi.org/10.1155/2020/8890201 doi: 10.1155/2020/8890201
    [13] D. K. Jeppesen, A. M. Fenix, J. L. Franklin, J. N. Higginbotham, Q. Zhang, L. J. Zimmerman, et al., Reassessment of exosome composition, Cell, 177 (2019), 428–445.e18. https://doi.org/10.1016/j.cell.2019.02.029 doi: 10.1016/j.cell.2019.02.029
    [14] L. Mashouri, H. Yousefi, A. R. Aref, A. M. Ahadi, F. Molaei, S. K. Alahari, Exosomes: composition, biogenesis, and mechanisms in cancer metastasis and drug resistance, Mol. Cancer, 18 (2019), Article Number 75. https://doi.org/10.1186/s12943-019-0991-5 doi: 10.1186/s12943-019-0991-5
    [15] M. A. Rodríguez-Parra, C. Vargas-De-León, F. Godinez-Jaimes, C. Martinez-Lázaro, Bayesian estimation of parameters in viral dynamics models with antiviral effect of interferons in a cell culture, Math. Biosci. Eng., 20 (2023), 11033–11062. https://doi.org/10.3934/mbe.2023488 doi: 10.3934/mbe.2023488
    [16] M. Betancourt, A conceptual introduction to Hamiltonian Monte Carlo, preprint, arXiv: 1701.02434v2, 2017. https://doi.org/10.48550/arXiv.1701.02434
    [17] C. P. Robert, G. Casella, Introducing Monte Carlo Methods with R, Springer, New York, 2010.
    [18] M. L. Rizzo, Statistical Computing with R, Chapman and Hall/CRC, 2019. https://doi.org/10.1201/9780429192760
    [19] A. Vehtari, A. Gelman, J. Gabry, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC, Stat. Comput., 27 (2016), 1413–1432. https://doi.org/10.1007/s11222-016-9696-4 doi: 10.1007/s11222-016-9696-4
    [20] J. Bezanson, A. Edelman, S. Karpinski, V. B. Shah, Julia: A fresh approach to numerical computing, SIAM Rev., 59 (2017), 65–98. https://doi.org/10.1137/141000671 doi: 10.1137/141000671
    [21] C. Rackauckas, Q. Nie, DifferentialEquations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia, J. Open Res. Software, 5 (2017), 1–5. https://doi.org/10.5334/jors.151 doi: 10.5334/jors.151
    [22] H. Ge, K. Xu, Z. Ghahramani, Turing: a language for flexible probabilistic inference, in International Conference on Artificial Intelligence and Statistics, PMLR, (2018), 1682–1690.
    [23] M. Besançon, T. Papamarkou, D. Anthoff, A. Arslan, S. Byrne, D. Lin, et al., Distributions. jl: Definition and modeling of probability distributions in the JuliaStats ecosystem, J. Stat. Software, 98 (2021), 1–30. https://doi.org/10.18637/jss.v098.i16 doi: 10.18637/jss.v098.i16
    [24] R. Kumar, C. Carroll, A. Hartikainen, O. Martin, ArviZ: A unified library for exploratory analysis of Bayesian models in Python, J. Open Source Software, 4 (2019), 1143. https://doi.org/10.21105/joss.01143 doi: 10.21105/joss.01143
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