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Statistical Model Checking in Drug Repurposing for Alzheimer’s Disease

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Formal Methods: Foundations and Applications (SBMF 2020)

Abstract

Dementia is a disease that is characterized by the gradual loss of memory and cognition of patients due to the death of neurons. The future perspective is that the number of patients will increase, due to the aging of the population, reaching up to one third of the world population over 65 years. Alzheimer’s disease is the most common form of dementia and there is no medication to prevent or cure the disease. In this sense, the discovery of an efficient treatment for the disease is a real need, and the repositioning of drugs and in silico techniques can contribute to this purpose. Computational methods, such as Statistical Model Checking, which is a formal verification technique, contribute to this field of research, aiding to analyze the evolution of the protein and drugs interactions at a lower cost than the laboratory experiments. In this work, we present a model of the PI3K/AKT/mTOR pathway and we connected it with Tau and A\(\beta \), which are two important proteins that contribute to the evolution of Alzheimer’s disease. We analyzed the effect of rapamycin, an immunosuppressive drug, on those proteins. Our results show that this medicine has the potential to slow down one of the biological processes that causes neuronal death. In addition, we could show the formal model verification technique can be an efficient tool to design pharmacological strategies reducing experimental cost.

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References

  1. Agha, G., Palmskog, K.: A survey of statistical model checking. ACM Trans. Model. Comput. Simul. 28(1), 1–39 (2018)

    Article  MathSciNet  Google Scholar 

  2. Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bakir, M.E., Konur, S., Gheorghe, M., Krasnogor, N., Stannett, M.: Automatic selection of verification tools for efficient analysis of biochemical models. Bioinformatics 34, 3187–3195 (2018)

    Article  Google Scholar 

  4. Baumgartner, G., Renner, K.H.: Humor in the elderly with dementia: development and initial validation of a behavioral observation system. Curr. Psychol., 1–14 (2019)

    Google Scholar 

  5. Bellozi, P.M.Q., et al.: NVP-BEZ235 (dactolisib) has protective effects in a transgenic mouse model of Alzheimer’s disease. Front. Pharmacol. 10, 1–11 (2019)

    Article  Google Scholar 

  6. Bellozi, P.M.Q., et al.: Neuroprotective effects of the anticancer drug NVP-BEZ235 (dactolisib) on amyloid-\(\beta \) 1-42 induced neurotoxicity and memory impairment. Sci. Rep. 6, 25226 (2016)

    Google Scholar 

  7. örg Bormann, J., Lohse, J., Payer, M., Vezin, G.: Model checking in industrial hardware design. In: 32nd Design Automation Conference, pp. 298–303. IEEE (1995)

    Google Scholar 

  8. Bulychev, P., et al.: Monitor-based statistical model checking for weighted metric temporal logic. In: Bjørner, N., Voronkov, A. (eds.) LPAR 2012. LNCS, vol. 7180, pp. 168–182. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28717-6_15

    Chapter  Google Scholar 

  9. Campos, S., Clarke, E., Marrero, W., Minea, M., Hiraishi, H.: Computing quantitative characteristics of finite-state real-time systems. In: Proceedings Real-Time Systems Symposium REAL-94, pp. 266–270. IEEE Comput. Soc. Press (1994)

    Google Scholar 

  10. Campos, S., Clarke, E.M., Minea, M.: Symbolic techniques for formally verifying industrial systems. Sci. Comput. Program. 29, 79–98 (1997)

    Article  Google Scholar 

  11. Christensen, B.D.D.: Alzheimer’s disease: progress in the development of anti-amyloid disease-modifying therapies. CNS Spectr. 12(2), 113–123 (2007)

    Article  Google Scholar 

  12. Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  13. David, A., et al.: Statistical model checking for networks of priced timed automata. In: Fahrenberg, U., Tripakis, S. (eds.) FORMATS 2011. LNCS, vol. 6919, pp. 80–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24310-3_7

    Chapter  Google Scholar 

  14. David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B.: UPPAAL SMC tutorial. Int. J. Softw. Tools Technol. Transf. 17(4), 397–415 (2015). https://doi.org/10.1007/s10009-014-0361-y

    Article  Google Scholar 

  15. De Strooper, B., Vassar, R., Golde, T.: The secretases: enzymes with therapeutic potential in Alzheimer disease. Nat. Rev. Neurol. 6(2), 99–107 (2010)

    Article  Google Scholar 

  16. Dorvash, M., et al.: Dynamic modeling of signal transduction by mTOR complexes in cancer. J. Theor. Biol. 483, 109992 (2019)

    Article  MATH  Google Scholar 

  17. Durães, F., Pinto, M., Sousa, E.: Old drugs as new treatments for neurodegenerative diseases. Pharmaceuticals 11(2), 1–21 (2018)

    Article  Google Scholar 

  18. Ferreira, B., et al.: Intelligent service to perform overtaking in vehicular networks. In: Proceedings - IEEE Symposium on Computers and Communications 2016-Febru, pp. 669–676 (2016)

    Google Scholar 

  19. Gabbouj, S., et al.: Altered insulin signaling in Alzheimer’s disease brain - special emphasis on PI3K-Akt pathway. Front. Neurosci. 13, 1–8 (2019)

    Article  Google Scholar 

  20. Goltsov, A., Tashkandi, G., Langdon, S.P., Harrison, D.J., Bown, J.L.: Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002. Eur. J. Pharm. Sci. 97, 170–181 (2017)

    Article  Google Scholar 

  21. Gong, H., Zuliani, P., Clarke, E.M.: Model checking of a diabetes-cancer model. AIP Conf. Proc. 1371, 234–243 (2011)

    Article  Google Scholar 

  22. Hao, W., Friedman, A.: Mathematical model on Alzheimer’s disease. BMC Syst. Biol. 10(1), 108 (2016)

    Article  Google Scholar 

  23. Heras-Sandoval, D., Pérez-Rojas, J.M., Hernández-Damián, J., Pedraza-Chaverri, J.: The role of PI3K/AKT/mTOR pathway in the modulation of autophagy and the clearance of protein aggregates in neurodegeneration. Cell. Signal. 26(12), 2694–2701 (2014)

    Article  Google Scholar 

  24. Hurd, M.D., Martorell, P., Delavande, A., Mullen, K.J., Langa, K.M.: Monetary costs of dementia in the United States. N. Engl. J. Med. 368(14), 1326–1334 (2013)

    Article  Google Scholar 

  25. Konur, S., Dixon, C., Fisher, M.: Analysing robot swarm behaviour via probabilistic model checking. Robot. Auton. Syst. 60(2), 199–213 (2012)

    Article  Google Scholar 

  26. Kubota, H., et al.: Temporal coding of insulin action through multiplexing of the AKT pathway. Mol. Cell 46(6), 820–832 (2012)

    Article  Google Scholar 

  27. Kwiatkowska, M., Norman, G., Parker, D.: Stochastic model checking. In: Bernardo, M., Hillston, J. (eds.) SFM 2007. LNCS, vol. 4486, pp. 220–270. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72522-0_6

    Chapter  Google Scholar 

  28. Le Novère, N.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16, 146–158 (2015)

    Article  Google Scholar 

  29. Lee, Y.S., Chow, W.N.V., Lau, K.F.: Phosphorylation of FE65 at threonine 579 by GSK3\(\beta \) stimulates amyloid precursor protein processing. Sci. Rep. 7(1), 1–10 (2017)

    Article  Google Scholar 

  30. Levine, B., Kroemer, G.: Autophagy in the pathogenesis of disease. Cell 132(1), 27–42 (2008)

    Article  Google Scholar 

  31. Li, J., Kim, S.G., Blenis, J.: Rapamycin: one drug, many effects (2014)

    Google Scholar 

  32. Lin, A.L., et al.: Rapamycin rescues vascular, metabolic and learning deficits in apolipoprotein E4 transgenic mice with pre-symptomatic Alzheimer’s disease. J. Cereb. Blood Flow Metab. 37(1), 217–226 (2017)

    Article  Google Scholar 

  33. Liu, Y., et al.: Rapamycin decreases Tau phosphorylation at Ser214 through regulation of cAMP-dependent kinase. Neurochem. Int. 62(4), 458–467 (2013)

    Article  Google Scholar 

  34. Llorens-Martín, M., Jurado, J., Hernández, F., Ávila, J.: GSK-3\(\beta \), a pivotal kinase in Alzheimer disease. Front. Mol. Neurosci. 7, 1–11 (2014)

    Google Scholar 

  35. Majd, S., Power, J., Majd, Z.: Alzheimer’s disease and cancer: when two monsters cannot be together. Front. Neurosci. 13, 1–11 (2019)

    Article  Google Scholar 

  36. McMillan, K.L.: A methodology for hardware verification using compositional model checking. Sci. Comput. Program. 37(1–3), 279–309 (2000)

    Article  MATH  Google Scholar 

  37. Ozcelik, S., et al.: Rapamycin attenuates the progression of Tau pathology in P301S Tau transgenic mice. PLoS ONE 8(5), 2–8 (2013)

    Article  Google Scholar 

  38. Patel, A.N., Jhamandas, J.H.: Neuronal receptors as targets for the action of amyloid-beta protein (a [beta]) in the brain. Expert. Rev. Mol. Med. 14 (2012)

    Google Scholar 

  39. Pezze, P.D., et al.: A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation. Sci. Signal. 5(217), 1–18 (2012)

    Google Scholar 

  40. Proctor, C.J., Gray, D.A.: GSK3 and p53 - is there a link in Alzheimer’s disease? Mol. Neurodegener. 5(1), 1–15 (2010)

    Article  Google Scholar 

  41. Ryu, S.H., et al.: Incidence and course of depression in patients with Alzheimer’s disease. Psychiatry Investig. 14(3), 271 (2017)

    Article  Google Scholar 

  42. Saxton, R.A., Sabatini, D.M.: mTOR signaling in growth, metabolism, and disease. Cell 168(6), 960–976 (2017)

    Article  Google Scholar 

  43. Selles, M.C., Oliveira, M.M., Ferreira, S.T.: Brain inflammation connects cognitive and non-cognitive symptoms in Alzheimer’s disease. J. Alzheimer’s Dis. 64(s1), S313–S327 (2018)

    Article  Google Scholar 

  44. Siegel, G.J.: Basic Neurochemistry: Molecular, Cellular, and Medical Aspects, 7th edn. Elsevier, Amsterdam (2006)

    Google Scholar 

  45. Siman, R., Cocca, R., Dong, Y.: The mTOR inhibitor rapamycin mitigates perforant pathway neurodegeneration and synapse loss in a mouse model of early-stage Alzheimer-type tauopathy. PLoS ONE 10(11), 1–21 (2015)

    Article  Google Scholar 

  46. Singh, A.K., Kashyap, M.P., Tripathi, V.K., Singh, S., Garg, G., Rizvi, S.I.: Neuroprotection through rapamycin-induced activation of autophagy and PI3K/Akt1/mTOR/CREB signaling against amyloid-\(\beta \)-induced oxidative stress, synaptic/neurotransmission dysfunction, and neurodegeneration in adult rats. Mol. Neurobiol. 54(8), 5815–5828 (2017)

    Article  Google Scholar 

  47. Spilman, P., et al.: Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-\(\beta \) levels in a mouse model of Alzheimer’s disease. PLoS ONE 5(4), 1–8 (2010)

    Article  Google Scholar 

  48. Sulaimanov, N., Klose, M., Busch, H., Boerries, M.: Understanding the mTOR signaling pathway via mathematical modeling. WIREs Syst. Biol. Med. 9 (2017)

    Google Scholar 

  49. Tenazinha, N., Vinga, S.: A survey on methods for modeling and analyzing integrated biological networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(4), 943–958 (2011)

    Article  Google Scholar 

  50. Varusai, T.M., Nguyen, L.K.: Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR. Sci. Rep. 8(1), 1–14 (2018)

    Article  Google Scholar 

  51. Vilallonga, G.D., De Almeida, A.C.G., Ribeiro, K.T., Campos, S.V., Rodrigues, A.M.: Hypothesized diprotomeric enzyme complex supported by stochastic modelling of palytoxin-induced Na/K pump channels. R. Soc. Open Sci. 5(3) (2018)

    Google Scholar 

  52. Wang, J., Gu, B.J., Masters, C.L., Wang, Y.J.: A systemic view of Alzheimer disease - Insights from amyloid-\(\beta \) metabolism beyond the brain. Nat. Rev. Neurol. 13(10), 612–623 (2017)

    Article  Google Scholar 

  53. Wang, Q., Clarke, E.M.: Formal methods for biological systems : languages, algorithms, and applications. Ph.D. thesis, Carnegie Mellon University (2016)

    Google Scholar 

  54. Younes, H.L.S.: Verification and Planning for Stochastic Processes with Asynchronous Events (2005)

    Google Scholar 

  55. Zuliani, P.: Statistical model checking for biological applications. Int. J. Softw. Tools Technol. Transf. 17(4), 527–536 (2014)

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Fundação de Amparo à Pesquisa de Minas Gerais – FAPEMIG, Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superios – CAPES for partially funding this research.

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Correspondence to Herbert Rausch Fernandes .

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Fernandes, H.R., Gomes, G.F., de Oliveira, A.C.P., Campos, S.V.A. (2020). Statistical Model Checking in Drug Repurposing for Alzheimer’s Disease. In: Carvalho, G., Stolz, V. (eds) Formal Methods: Foundations and Applications. SBMF 2020. Lecture Notes in Computer Science(), vol 12475. Springer, Cham. https://doi.org/10.1007/978-3-030-63882-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-63882-5_5

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