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

Mohsina Patwekar, Faheem Patwekar, Syed Sanaullah, Daniyal Shaikh, Ustad Almas, Rohit Sharma

Article ID: 2395
Vol 7, Issue 2, 2023

VIEWS - 576 (Abstract) 178 (PDF)


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.


Parkinson’s disease; artificial intelligence; neurodegenerative disorder; pathways; treatment optimization

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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

DOI: https://doi.org/10.24294/ti.v7.i2.2395


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