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Computational Modeling of Therapy with the NMDA Antagonist in Neurodegenerative Disease: Information Theory in the Mechanism of Action of Memantine

Author

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  • Dariusz Świetlik

    (Division of Biostatistics and Neural Networks, Medical University of Gdańsk, Dębinki 1, 80-211 Gdańsk, Poland)

  • Aida Kusiak

    (Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland)

  • Agata Ossowska

    (Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland)

Abstract

(1) Background: in patients with neurodegenerative diseases, noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists provide neuroprotective advantages. We performed memantine therapy and proved mathematical and computer modeling of neurodegenerative disease in this study. (2) Methods: a computer simulation environment of the N-methyl-D-aspartate receptor incorporating biological mechanisms of channel activation by high extracellular glutamic acid concentration. In comparison to controls, pathological models were essentially treated with doses of memantine 3–30 µM. (3) Results: the mean values and 95% CI for Shannon entropy in Alzheimer’s disease (AD) and memantine treatment models were 1.760 (95% CI, 1.704–1.818) vs. 2.385 (95% CI, 2.280–2.490). The Shannon entropy was significantly higher in the memantine treatment model relative to AD model ( p = 0.0162). The mean values and 95% CI for the positive Lyapunov exponent in AD and memantine treatment models were 0.125 (95% CI, NE–NE) vs. 0.058 (95% CI, 0.044–0.073). The positive Lyapunov exponent was significantly higher in the AD model relative to the memantine treatment model ( p = 0.0091). The mean values and 95% CI for transfer entropy in AD and memantine treatment models were 0.081 (95% CI, 0.048–0.114) vs. 0.040 (95% CI, 0.019–0.062). The transfer entropy was significantly higher in the AD model relative to the memantine treatment model ( p = 0.0146). A correlation analysis showed positive and statistically significant correlations of the memantine concentrations and the positive Lyapunov exponent (correlation coefficient R = 0.87, p = 0.0023) and transfer entropy (TE) (correlation coefficient R = 0.99, p < 0.000001). (4) Conclusions: information theory results of simulation studies show that the NMDA antagonist, memantine, causes neuroprotective benefits in patients with AD. Our simulation study opens up remarkable new scenarios in which a medical product, drug, or device, can be developed and tested for efficacy based on parameters of information theory.

Suggested Citation

  • Dariusz Świetlik & Aida Kusiak & Agata Ossowska, 2022. "Computational Modeling of Therapy with the NMDA Antagonist in Neurodegenerative Disease: Information Theory in the Mechanism of Action of Memantine," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4727-:d:793518
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    References listed on IDEAS

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    1. Agata Ossowska & Aida Kusiak & Dariusz Świetlik, 2022. "Artificial Intelligence in Dentistry—Narrative Review," IJERPH, MDPI, vol. 19(6), pages 1-10, March.
    2. Dariusz Świetlik & Jacek Białowąs, 2019. "Application of Artificial Neural Networks to Identify Alzheimer’s Disease Using Cerebral Perfusion SPECT Data," IJERPH, MDPI, vol. 16(7), pages 1-9, April.
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    1. Agata Ossowska & Aida Kusiak & Dariusz Świetlik, 2022. "Artificial Intelligence in Dentistry—Narrative Review," IJERPH, MDPI, vol. 19(6), pages 1-10, March.

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