Trends in Immunotherapy

Review

Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects

Downloads

https://doi.org/10.24294/ti.v7.i2.2395
Patwekar, M., Patwekar, F., Sanaullah, S., Shaikh, D., Almas, U., & Sharma, R. (2023). Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects. Trends in Immunotherapy, 7(2). https://doi.org/10.24294/ti.v7.i2.2395
Volume 7 Issue 2 (2023)

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Trends in Immunotherapy (TI)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by TI agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source are properly cited, anyone may copy, redistribute, reuse, and transform the content.

Authors

  • Mohsina Patwekar Luqman College of Pharmacy, Gulbarga, Karnataka 585102, India
  • Faheem Patwekar Luqman College of Pharmacy, Gulbarga, Karnataka 585102, India
  • Syed Sanaullah Luqman College of Pharmacy, Gulbarga, Karnataka 585102, India
  • Daniyal Shaikh Luqman College of Pharmacy, Gulbarga, Karnataka 585102, India
  • Ustad Almas Luqman College of Pharmacy, Gulbarga, Karnataka 585102, India
  • Rohit Sharma
    Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the accumulation of misfolded proteins and impaired protein degradation mechanisms. Dysregulation of protein degradation processes, including autophagy and the ubiquitin-proteasome system, has been implicated in the pathogenesis of PD. Recently, artificial intelligence (AI) has emerged as a powerful tool to enhance our understanding of protein degradation in PD. This abstract provides an overview of the advancements in studying protein degradation in PD with the aid of AI. The integration of AI techniques, such as machine learning and data mining, has enabled the identification and characterization of protein degradation pathways involved in PD. By analyzing large-scale protein-protein interaction networks, AI algorithms have revealed key interactions and pathways underlying protein degradation dysfunction in PD. Furthermore, AI models can predict the efficiency of protein degradation processes and identify potential targets for enhancing protein degradation in PD, aiding in the development of novel therapeutic interventions.AI-based approaches have also been instrumental in drug discovery and target identification, as they can screen vast databases of compounds to identify potential drugs or small molecules that modulate protein degradation pathways relevant to PD. Additionally, deep learning algorithms have facilitated the analysis of protein structures, predicting protein stability and folding patterns that impact protein degradation. Moreover, AI has played a crucial role in the identification of protein biomarkers associated with protein degradation dysfunction in PD. These biomarkers can aid in early diagnosis and monitoring of the disease, enabling timely intervention and personalized treatment strategies. The advancements presented in this abstract highlight the transformative potential of AI in elucidating the intricate mechanisms of protein degradation in PD. Collaborations between AI researchers, biologists, and clinicians are essential to translate these findings into effective diagnostic tools and therapeutic interventions for PD patients.

Keywords:

Parkinson’s disease artificial intelligence neurodegenerative disorder pathways treatment optimization

References

  1. de Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. The Lancet Neurology 2006; 5(6): 525–535. doi: 10.1016/S1474-4422(06)70471-9
  2. Mutch WJ, Dingwall-Fordyce I, Downie AW, et al. Parkinson’s disease in a Scottish city. British Medical Journal (Clinical Research Ed.) 1986; 292(6519): 534–536. doi: 10.1136/bmj.292.6519.534
  3. Braak H, Braak E. Pathoanatomy of Parkinson’s disease. Journal of Neurology 2000; 247(S2): II3–II10. doi: 10.1007/PL00007758
  4. Dauer W, Przedborski S. Parkinson’s disease: Mechanisms and models. Neuron 2003; 39(6): 889–909. doi: 10.1016/S0896-6273(03)00568-3
  5. Prasath N, Pandi V, Manickavasagam S, Ramadoss P. A comparative and comprehensive study of prediction of parkinson’s disease. Indonesian Journal of Electrical Engineering and Computer Science 2021; 23(3): 1748–1760. doi: 10.11591/ijeecs.v23.i3.pp1748-1760
  6. Ebrahimi A, Ahmadi H, Ghasrodashti ZP, et al. Therapeutic effects of stem cells in different body systems, a novel method that is yet to gain trust: A comprehensive review. Bosnian Journal of Basic Medical Sciences 2021; 21(6): 672.
  7. Lyons KE, Pahwa R. The impact and management of nonmotor symptoms of Parkinson’s disease. American Journal of Managed Care 2011; 17(12): S308.
  8. Brown TP, Rumsby PC, Capleton AC, et al. Pesticides and Parkinson’s disease—Is there a link? Environmental Health Perspectives 2006; 114(2): 156–164. doi: 10.1289/ehp.8095
  9. Lees AJ. Hardy J, Revesz T. Parkinson’s disease. Lancet 2009; 373(9680): 2055–2066. doi: 10.1016/S0140-6736(09)60492-X
  10. Bower JH, Maraganore DM, McDonnell SK, Rocca WA. Incidence and distribution of parkinsonism in Olmsted County, Minnesota, 1976–1990. Neurology 1999; 52(6): 1214. doi: 10.1212/wnl.52.6.1214
  11. Grandinetti A, Morens DM, Reed D, MacEachern D. Prospective study of cigarette smoking and the risk of developing idiopathic Parkinson’s disease. American Journal of Epidemiology 1994; 139(12): 1129–1138. doi: 10.1093/oxfordjournals.aje.a116960
  12. Dixit S, Bohre K, Singh Y, et al. A comprehensive review on AI-enabled models for Parkinson’s disease diagnosis. Electronics 2023; 12(4): 783. doi: 10.3390/electronics12040783
  13. Hernán MA, Zhang SM, Rueda-DeCastro AM, et al. Cigarette smoking and the incidence of Parkinson’s disease in two prospective studies. Annals of Neurology 2001; 50(6): 780–786. doi: 10.1002/ana.10028
  14. Hernán MA, Takkouche B, Caamaño-Isorna F, Gestal-Otero JJ. A meta-analysis of coffee drinking, cigarette smoking, and the risk of Parkinson’s disease. Annals of Neurology 2002; 52(3): 276–284. doi: 10.1002/ana.10277
  15. Ritz B, Ascherio A, Checkoway H, et al. Pooled analysis of tobacco use and risk of Parkinson disease. Archives of Neurology 2007; 64(7): 990–997. doi: 10.1001/archneur.64.7.990
  16. Ileșan RR, Cordoș CG, Mihăilă LI, et al. Proof of concept in artificial-intelligence-based wearable gait monitoring for Parkinson’s disease management optimization. Biosensors 2022; 12(4): 189. doi: 10.3390/bios12040189
  17. Breckenridge CB, Berry C, Chang ET, et al. Association between Parkinson’s disease and cigarette smoking, rural living, well-water consumption, farming and pesticide use: Systematic review and meta-analysis. PLoS One 2016; 11(4): e0151841. doi: 10.1371/journal.pone.0151841
  18. Ross GW, Abbott RD, Petrovitch H, et al. Association of coffee and caffeine intake with the risk of Parkinson disease. JAMA 2000; 283(20): 2674–2479. doi: 10.1001/jama.283.20.2674
  19. Chen JF, Xu K, Petzer JP, et al. Neuroprotection by caffeine and A (2A) adenosine receptor inactivation in a model of Parkinson’s disease. The Journal of Neuroscience 2001; 21(10): RC143. doi: 10.1523/JNEUROSCI.21-10-j0001.2001
  20. Noyce AJ, Bestwick JP, Silveira-Moriyama L, et al. Meta-analysis of early nonmotor features and risk factors for Parkinson disease. Annals of Neurology 2012; 72(6): 893–901. doi: 10.1002/ana.23687
  21. Noviandy TR, Maulana A, Idroes GM, et al. Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery. Malacca Pharmaceutics 2023; 1(2): 48–54.
  22. Fondell E, O’Reilly ÉJ, Fitzgerald KC, et al. Intakes of caffeine, coffee and tea and risk of amyotrophic lateral sclerosis: Results from five cohort studies. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 2015; 16(5–6): 366–371. doi: 10.3109/21678421.2015.1020813.
  23. Xu K, Xu Y, Brown-Jermyn D, et al. Estrogen prevents neuroprotection by caffeine in the mouse 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine model of Parkinson’s disease. Journal of Neuroscience 2006; 26(2): 535–541. doi: 10.1523/JNEUROSCI.3008-05.2006
  24. Deng H, Wang P, Jankovic J. The genetics of Parkinson disease. Ageing Research Reviews 2018; 42: 72–85. doi: 10.1016/j.arr.2017.12.007
  25. Schulte C, Gasser T. Genetic basis of Parkinson’s disease: Inheritance, penetrance, and expression. The Application of Clinical Genetics 2011; 4: 67–80. doi: 10.2147/tacg.s11639
  26. Polymeropoulos MH, Lavedan C, Leroy E, et al. Mutation in the α-synuclein gene identified in families with Parkinson’s disease. Science 1997; 276(5321): 2045–2047. doi: 10.1126/science.276.5321.2045
  27. Healy DG, Falchi M, O’Sullivan SS, et al. Phenotype, genotype, and worldwide genetic penetrance of LRRK2-associated Parkinson’s disease: A case-control study. The Lancet Neurology 2008; 7(7): 583–590. doi: 10.1016/S1474-4422(08)70117-0
  28. Pange SS, Patwekar M, Patwekar F, et al. A potential notion on alzheimer’s disease: nanotechnology as an alternative solution. Journal of Nanomaterials 2022; 2022: 6910811. doi: 10.1155/2022/6910811
  29. Chai C, Lim KL. Genetic insights into sporadic Parkinson’s disease pathogenesis. Current Genomics 2013; 14(8): 486–501. doi: 10.2174/1389202914666131210195808
  30. Chinta SJ, Lieu CA, DeMaria M, et al. Environmental stress, ageing and glial cell senescence: A novel mechanistic link to Parkinson’s disease? Journal of Internal Medicine 2013; 273(5): 429–436. doi: 10.1111/joim.12029
  31. Goldman JG, Bernard BA. Cognitive assessments and Parkinson’s disease. Encyclopedia of Movement Disorders 2010; 1: 232. doi: 10.1016/B978-0-12-374105-9.00168-4
  32. Reeve A, Simcox E, Turnbull D. Ageing and Parkinson’s disease: Why is advancing age the biggest risk factor? Ageing Research Reviews 2014; 14: 19–30.
  33. Dawson TM, Dawson VL. Rare genetic mutations shed light on the pathogenesis of Parkinson disease. The Journal of Clinical Investigation 2003; 111(2): 145–151. doi: 10.1172/JCI17575
  34. Sherer TB, Chowdhury S, Peabody K, Brooks DW. Overcoming obstacles in Parkinson’s disease. Movement Disorders 2012; 27(13): 1606–1611. doi: 10.1002/mds.25260
  35. Macphee GJ, Stewart DA. Parkinson’s disease. Reviews in Clinical Gerontology 2001; 11(1): 33–49. doi: 10.1017/s0959259801011145
  36. Savica R, Grossardt BR, Bower JH, et al. Risk factors for Parkinson’s disease may differ in men and women: An exploratory study. Hormones and Behavior 2013; 63(2): 308–314. doi: 10.1016/j.yhbeh.2012.05.013
  37. Jankovic J, Hurtig H, Dashe J. Etiology and pathogenesis of Parkinson Disease. Available online: https://www.uptodate.com/contents/epidemiology-pathogenesis-and-genetics-of-parkinson-disease (accessed on 26 October 2023).
  38. Gazewood JD, Richards DR, Clebak K. Parkinson disease: An update. American Family Physician 2013; 87(4): 267–273.
  39. Fahn S. Parkinson’s disease: 10 years of progress, 1997–2007. Movement Disorders 2010; 25(S1): S2–S14. doi: 10.1002/mds.22796
  40. Davie CA. A review of Parkinson’s disease. British Medical Bulletin 2008; 86(1): 109–127. doi: 10.1093/bmb/ldn013
  41. Mallucci GR. Prion neurodegeneration: starts and stops at the synapse. Prion 2009; 3(4): 195–201. doi: 10.4161/pri.3.4.9981
  42. Palop JJ, Mucke L. Amyloid-β-induced neuronal dysfunction in Alzheimer’s disease: From synapses toward neural networks. Nature Neuroscience 2010; 13(7): 812–818. doi: 10.1038/nn.2583
  43. Lavedan C, Leroy E, Dehejia A, et al. Identification, localization and characterization of the human γ-synuclein gene. Human Genetics 1998; 103: 106–112. doi: 10.1007/s004390050792
  44. Simon-Sanchez J, Schulte C, Bras JM, et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nature Genetics 2009; 41(12): 1308–1312. doi: 10.1038/ng.487
  45. Fortin DL, Nemani VM, Voglmaier SM, et al. Neural activity controls the synaptic accumulation of α-synuclein. Journal of Neuroscience 2005; 25(47): 10913–10921. doi: 10.1523/JNEUROSCI.2922-05.2005
  46. Burré J, Sharma M, Tsetsenis T, et al. α-Synuclein promotes SNARE-complex assembly in vivo and in vitro. Science 2010; 329(5999): 1663–1667. doi: 10.1126/science.1195227
  47. Pickrell AM, Youle RJ. The roles of PINK1, parkin, and mitochondrial fidelity in Parkinson’s disease. Neuron 2015; 85(2): 257–273. doi: 10.1016/j.neuron.2014.12.007
  48. Quilty MC, King AE, Gai WP, et al. Alpha-synuclein is upregulated in neurones in response to chronic oxidative stress and is associated with neuroprotection. Experimental Neurology 2006; 199(2): 249–256. doi: 10.1016/j.expneurol.2005.10.018
  49. Singleton AB, Farrer M, Johnson J, et al. α-Synuclein locus triplication causes Parkinson’s disease. Science 2003; 302(5646): 841. doi :10.1126/science.1090278
  50. Fischer EF, Victor B, Robinson D, et al. Coffee consumption and health impacts: A brief history of changing conceptions. In: Coffee: Consumption and Health Implications. The Royal Society of Chemistry; 2019.
  51. Schulz-Schaeffer WJ. The synaptic pathology of α-synuclein aggregation in dementia with Lewy bodies, Parkinson’s disease and Parkinson’s disease dementia. Acta Neuropathologica 2010; 120(2): 131–143. doi: 10.1007/s00401-010-0711-0
  52. Piccoli G, Condliffe SB, Bauer M, et al. LRRK2 controls synaptic vesicle storage and mobilization within the recycling pool. Journal of Neuroscience 2011; 31(6): 2225–2237. doi: 10.1523/jneurosci.3730-10.2011
  53. Shin N, Jeong H, Kwon J, et al. LRRK2 regulates synaptic vesicle endocytosis. Experimental Cell Research 2008; 314(10): 2055–2065. doi: 10.1016/j.yexcr.2008.02.015
  54. Asanuma M, Miyazaki I, Ogawa N. Dopamine-or L-DOPA-induced neurotoxicity: The role of dopamine quinone formation and tyrosinase in a model of Parkinson’s disease. Neurotoxicity Research 2003; 5(3): 165–176. doi: 10.1007/bf03033137
  55. Conway KA, Rochet JC, Bieganski RM, Lansbury PT. Kinetic stabilization of the α-synuclein protofibril by a dopamine-α-synuclein adduct. Science 2001; 294(5545): 1346–1349. doi: 10.1126/science.1063522
  56. Leong SL, Cappai R, Barnham KJ, Pham CL. Modulation of α-synuclein aggregation by dopamine: A review. Neurochemical Research 2009; 34(10): 1838–1846. doi: 10.1007/s11064-009-9986-8
  57. Perier C, Villa M. Park DS. Programed cell death in Parkinson’s disease. Cold Spring Harbor Perspectives in Medicine 2012; 2(2): a009332. doi: 10.1101/cshperspect.a009332
  58. Tai HC, Schuman EM. Ubiquitin, the proteasome and protein degradation in neuronal function and dysfunction. Nature Reviews Neuroscience 2008; 9(11): 826–838. doi: 10.1038/nrn2499
  59. Keane PC, Kurzawa M, Blain PG, Morris CM. Mitochondrial dysfunction in Parkinson’s disease. Parkinson’s Disease 2011; 2011: 1–18. doi: 10.4061/2011/716871
  60. Langston JW, Ballard P, Tetrud JW, Irwin I. Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 1983; 219(4587): 979–980. doi: 10.1126/science.6823561
  61. Betarbet R, Sherer TB, MacKenzie G, et al. Chronic systemic pesticide exposure reproduces features of Parkinson’s disease. Nature Neuroscience 2000; 3(12): 1301–1306. doi :10.1038/81834
  62. Cassarino DS, Fall CP, Swerdlow RH, et al. Elevated reactive oxygen species and antioxidant enzyme activities in animal and cellular models of Parkinson’s disease. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1997; 1362(1): 77–86. doi: 10.1016/S0925-4439(97)00070-7
  63. McGeer PL, Itagaki S, Boyes BE, McGeer EG. Reactive microglia are positive for HLA‐DR in the substantia nigra of Parkinson’s and Alzheimer’s disease brains. Neurology 1988; 38(8): 1285. doi: 10.1212/WNL.38.8.1285
  64. Gao HM, Liu B, Zhang W, Hong JS. Novel anti-inflammatory therapy for Parkinson’s disease. Trends in Pharmacological Sciences 2003; 24(8): 395–401. doi: 10.1016/S0165-6147(03)00176-7
  65. Hunot S, Hirsch EC. Neuroinflammatory processes in Parkinson’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society 2003; 53(S3): S49–S60. doi: 10.1002/ana.10481
  66. Hirsch EC, Hunot S, Hartmann A. Neuroinflammatory processes in Parkinson’s disease. Parkinsonism & Related Disorders 2005; 11: S9–S15. doi: 10.1016/j.parkreldis.2004.10.013
  67. Am OB, Amit T, Youdim MB. Contrasting neuroprotective and neurotoxic actions of respective metabolites of anti-Parkinson drugs rasagiline and selegiline. Neuroscience Letters 2004; 355(3): 169–172. doi: 10.1016/j.neulet.2003.10.067
  68. Arıca B, Kaş HS, Moghdam A, et al. Carbidopa/levodopa-loaded biodegradable microspheres: In vivo evaluation on experimental Parkinsonism in rats. Journal of Controlled Release 2005; 102(3) :689–697. doi: 10.1016/j.jconrel.2004.11.004
  69. Singh N, Pillay V, Choonara YE. Advances in the treatment of Parkinson’s disease. Progress in Neurobiology 2007; 81(1): 29–44. doi: 10.1016/j.pneurobio.2006.11.009
  70. Hindle JV. Ageing, neurodegeneration and Parkinson’s disease. Age and Ageing 2010; 39(2): 156–161. doi: 10.1093/ageing/afp223
  71. Jin W, Luo Q. When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation. Computers in Biology and Medicine 2022; 145: 105499. doi: 10.1016/j.compbiomed.2022.105499