Advances in Developing Small Molecule Drugs for Alzheimer's Disease


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Abstract


Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development.

About the authors

Wei Zhang

School of Basic Medical Science, Xinxiang Medical University

Email: info@benthamscience.net

Liujie Zhang

School of Basic Medical Science, Xinxiang Medical University

Email: info@benthamscience.net

Mingti Lv

School of Basic Medical Science, Xinxiang Medical University

Email: info@benthamscience.net

Yun Fu

School of Basic Medical Science, Xinxiang Medical University

Email: info@benthamscience.net

Xiaowen Meng

School of Basic Medical Science, Xinxiang Medical University

Email: info@benthamscience.net

Mingyong Wang

School of Medical Technology, Xinxiang Medical University

Author for correspondence.
Email: info@benthamscience.net

Hecheng Wang

School of Basic Medical Science, Xinxiang Medical University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Tatulian SA. Challenges and hopes for Alzheimer’s disease. Drug Discov Today 2022; 27(4): 1027-43. doi: 10.1016/j.drudis.2022.01.016 PMID: 35121174
  2. Adav SS, Sze SK. Insight of brain degenerative protein modifications in the pathology of neurodegeneration and dementia by proteomic profiling. Mol Brain 2016; 9(1): 92. doi: 10.1186/s13041-016-0272-9 PMID: 27809929
  3. Peng C, Trojanowski JQ, Lee VMY. Protein transmission in neurodegenerative disease. Nat Rev Neurol 2020; 16(4): 199-212. doi: 10.1038/s41582-020-0333-7 PMID: 32203399
  4. Alzheimer’s disease facts and figures. Alzheimers Dement 2023; 19(4): 1598-695. doi: 10.1002/alz.13016 PMID: 36918389
  5. Alzheimer’s disease facts and figures. Alzheimers Dement 2022; 18(4): 700-89. doi: 10.1002/alz.12638 PMID: 35289055
  6. Yiannopoulou KG, Papageorgiou SG. Current and future treatments in Alzheimer disease: An update. J Cent Nerv Syst Dis 2020; 12 doi: 10.1177/1179573520907397 PMID: 32165850
  7. Livingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 2020; 396(10248): 413-46. doi: 10.1016/S0140-6736(20)30367-6 PMID: 32738937
  8. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2019. Alzheimers Dement (N Y) 2019; 5(1): 272-93. doi: 10.1016/j.trci.2019.05.008 PMID: 31334330
  9. Shukla R, Singh TR. Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer’s disease. J Biomol Struct Dyn 2020; 38(1): 248-62. doi: 10.1080/07391102.2019.1571947 PMID: 30688165
  10. Mouchlis VD, Melagraki G, Zacharia LC, Afantitis A. Computer-aided drug design of β-secretase, γ-secretase and anti-tau inhibitors for the discovery of novel Alzheimer’s therapeutics. Int J Mol Sci 2020; 21(3): 703. doi: 10.3390/ijms21030703 PMID: 31973122
  11. Jabir NR, Rehman MT, Alsolami K, et al. Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: In pursuit of Alzheimer’s treatment. Ann Med 2021; 53(1): 2332-44. doi: 10.1080/07853890.2021.2009124 PMID: 34889159
  12. Griffiths J, Grant SGN. Synapse pathology in Alzheimer’s disease. Semin Cell Dev Biol 2023; 139: 13-23. doi: 10.1016/j.semcdb.2022.05.028 PMID: 35690535
  13. Ashrafian H, Zadeh EH, Khan RH. Review on Alzheimer’s disease: Inhibition of amyloid beta and tau tangle formation. Int J Biol Macromol 2021; 167: 382-94. doi: 10.1016/j.ijbiomac.2020.11.192 PMID: 33278431
  14. Trejo-Lopez JA, Yachnis AT, Prokop S. Neuropathology of Alzheimer’s disease. Neurotherapeutics 2022; 19(1): 173-85. doi: 10.1007/s13311-021-01146-y PMID: 34729690
  15. Thal DR, Tomé SO. The central role of tau in Alzheimer’s disease: From neurofibrillary tangle maturation to the induction of cell death. Brain Res Bull 2022; 190: 204-17. doi: 10.1016/j.brainresbull.2022.10.006 PMID: 36244581
  16. Lee WJ, Brown JA, Kim HR, et al. Regional Aβ- tau interactions promote onset and acceleration of Alzheimer's disease tau spreading. Neuron 2022; 110(12): 1932-1943.e5. doi: 10.1016/j.neuron.2022.03.034
  17. Karran E, De Strooper B. The amyloid hypothesis in Alzheimer disease: New insights from new therapeutics. Nat Rev Drug Discov 2022; 21(4): 306-18. doi: 10.1038/s41573-022-00391-w PMID: 35177833
  18. Song C, Shi J, Zhang P, et al. Immunotherapy for Alzheimer’s disease: Targeting β-amyloid and beyond. Transl Neurodegener 2022; 11(1): 18. doi: 10.1186/s40035-022-00292-3 PMID: 35300725
  19. Walker LC. Aβ plaques. Free Neuropathol 2020; 11(31): 3025. doi: 10.17879/freeneuropathology-2020-3025
  20. Zhang Y, Chen H, Li R, Sterling K, Song W. Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future. Signal Transduct Target Ther 2023; 8(1): 248. doi: 10.1038/s41392-023-01484-7 PMID: 37386015
  21. Bellenguez C, Grenier-Boley B, Lambert JC. Genetics of Alzheimer’s disease: Where we are, and where we are going. Curr Opin Neurobiol 2020; 61: 40-8. doi: 10.1016/j.conb.2019.11.024 PMID: 31863938
  22. Bordone MP, Salman MM, Titus HE, et al. The energetic brain – A review from students to students. J Neurochem 2019; 151(2): 139-65. doi: 10.1111/jnc.14829 PMID: 31318452
  23. Sinsky J, Pichlerova K, Hanes J. Tau protein interaction partners and their roles in Alzheimer’s disease and other tauopathies. Int J Mol Sci 2021; 22(17): 9207. doi: 10.3390/ijms22179207 PMID: 34502116
  24. Venkatramani A, Panda D. Regulation of neuronal microtubule dynamics by tau: Implications for tauopathies. Int J Biol Macromol 2019; 133: 473-83. doi: 10.1016/j.ijbiomac.2019.04.120 PMID: 31004638
  25. Aisen PS, Jimenez-Maggiora GA, Rafii MS, Walter S, Raman R. Early-stage Alzheimer disease: Getting trial-ready. Nat Rev Neurol 2022; 18(7): 389-99. doi: 10.1038/s41582-022-00645-6 PMID: 35379951
  26. Ovejero-Benito MC, Ochoa D, Enrique-Benedito T, et al. Pharmacogenetics of donepezil and memantine in healthy subjects. J Pers Med 2022; 12(5): 788. doi: 10.3390/jpm12050788 PMID: 35629210
  27. Li X, Jia Y, Li J, et al. Novel and potent acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease from natural (±)-7,8-dihydroxy-3-methyl-isochroman-4-one. Molecules 2022; 27(10): 3090. doi: 10.3390/molecules27103090 PMID: 35630563
  28. Calhoun A, King C, Khoury R, Grossberg GT. An evaluation of memantine ER + donepezil for the treatment of Alzheimer’s disease. Expert Opin Pharmacother 2018; 19(15): 1711-7. doi: 10.1080/14656566.2018.1519022 PMID: 30244611
  29. Shi M, Chu F, Zhu F, Zhu J. Impact of anti-amyloid-β monoclonal antibodies on the pathology and clinical profile of Alzheimer’s disease: A focus on aducanumab and lecanemab. Front Aging Neurosci 2022; 14: 870517. doi: 10.3389/fnagi.2022.870517 PMID: 35493943
  30. Sims JR, Zimmer JA, Evans CD, et al. Donanemab in Early Symptomatic Alzheimer Disease. JAMA 2023; 330(6): 512-27. doi: 10.1001/jama.2023.13239 PMID: 37459141
  31. Pardo-Moreno T, González-Acedo A, Rivas-Domínguez A, et al. Therapeutic approach to Alzheimer’s disease: Current treatments and new perspectives. Pharmaceutics 2022; 14(6): 1117. doi: 10.3390/pharmaceutics14061117 PMID: 35745693
  32. Bateman RJ, Cummings J, Schobel S, et al. Gantenerumab: An anti-amyloid monoclonal antibody with potential disease-modifying effects in early Alzheimer’s disease. Alzheimers Res Ther 2022; 14(1): 178. doi: 10.1186/s13195-022-01110-8 PMID: 36447240
  33. Vukicevic M, Fiorini E, Siegert S, et al. An amyloid beta vaccine that safely drives immunity to a key pathological species in Alzheimer’s disease: Pyroglutamate amyloid beta. Brain Commun 2022; 4(1): fcac022. doi: 10.1093/braincomms/fcac022 PMID: 35479516
  34. Dubois B, López-Arrieta J, Lipschitz S, et al. Masitinib for mild-to-moderate Alzheimer’s disease: Results from a randomized, placebo-controlled, phase 3, clinical trial. Alzheimers Res Ther 2023; 15(1): 39. doi: 10.1186/s13195-023-01169-x PMID: 36849969
  35. Huang LK, Kuan YC, Lin HW, Hu CJ. Clinical trials of new drugs for Alzheimer disease: A 2020–2023 update. J Biomed Sci 2023; 30(1): 83. doi: 10.1186/s12929-023-00976-6 PMID: 37784171
  36. Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci 2023; 181: 106324. doi: 10.1016/j.ejps.2022.106324 PMID: 36347444
  37. Baig MH, Ahmad K, Rabbani G, Danishuddin M, Choi I. Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders. Curr Neuropharmacol 2018; 16(6): 740-8. doi: 10.2174/1570159X15666171016163510 PMID: 29046156
  38. Wang B, Dai P, Ding D, et al. Affinity-based capture and identification of protein effectors of the growth regulator ppGpp. Nat Chem Biol 2019; 15(2): 141-50. doi: 10.1038/s41589-018-0183-4 PMID: 30559427
  39. Chan HCS, Li Y, Dahoun T, Vogel H, Yuan S. New binding sites, new opportunities for GPCR drug discovery. Trends Biochem Sci 2019; 44(4): 312-30. doi: 10.1016/j.tibs.2018.11.011 PMID: 30612897
  40. Kim EY, Im JH, Han J, Cho WJ. Structure-based design and synthesis of sulfonylureas as novel NLRP3 inhibitors for Alzheimer’s disease. Bioorg Med Chem Lett 2024; 99: 129622. doi: 10.1016/j.bmcl.2024.129622 PMID: 38244940
  41. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  42. Ragonis-Bachar P, Axel G, Blau S, Ben-Tal N, Kolodny R, Landau M. What can ALPHAFOLD do for antimicrobial amyloids? Proteins 2024; 92(2): 265-81. doi: 10.1002/prot.26618 PMID: 37855235
  43. Llanes LC, Kuehlewein I, França IV, da Silva LV, da Cruz Junior JW. Anticholinesterase agents for Alzheimer’s disease treatment: An updated overview. Curr Med Chem 2023; 30(6): 701-24. doi: 10.2174/0929867329666220803113411 PMID: 35927804
  44. Lv MT, Wang HC, Meng XW, et al. In silico and in vitro analyses of a novel FOXO1 agonist reducing Aβ levels via downregulation of BACE1. CNS Neurosci Ther 2024; 30(3): e14140. doi: 10.1111/cns.14140 PMID: 36892036
  45. Makarasen A, Kuno M, Patnin S, et al. Molecular docking studies and synthesis of amino-oxy-diarylquinoline derivatives as potent non-nucleoside HIV-1 reverse transcriptase inhibitors. Drug Res (Stuttg) 2019; 69(12): 671-82. doi: 10.1055/a-0968-1150 PMID: 31698495
  46. Vilar S, Sobarzo-Sánchez E, Uriarte E. In silico prediction of P-glycoprotein binding: Insights from molecular docking studies. Curr Med Chem 2019; 26(10): 1746-60. doi: 10.2174/0929867325666171129121924 PMID: 29189117
  47. Ye W, Wang W, Jiang C, Yu Q, Chen H. Molecular dynamics simulations of amyloid fibrils: An in silico approach. Acta Biochim Biophys Sin (Shanghai) 2013; 45(6): 503-8. doi: 10.1093/abbs/gmt026 PMID: 23532062
  48. Nunes RR, Fonseca AL, Pinto ACS, et al. Brazilian malaria molecular targets (BraMMT): Selected receptors for virtual high-throughput screening experiments. Mem Inst Oswaldo Cruz 2019; 114: e180465. doi: 10.1590/0074-02760180465 PMID: 30810604
  49. Muratov EN, Bajorath J, Sheridan RP, et al. QSAR without borders. Chem Soc Rev 2020; 49(11): 3525-64. doi: 10.1039/D0CS00098A PMID: 32356548
  50. Kumar A, Nandi S, Saxena AK. Antidepressant drug design on TCAs and phenoxyphenylpropylamines utilizing QSAR and pharmacophore modeling. Comb Chem High Throughput Screen 2022; 25(3): 451-61. doi: 10.2174/1386207323666200901104222 PMID: 32875980
  51. Giordano D, Biancaniello C, Argenio MA, Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals (Basel) 2022; 15(5): 646. doi: 10.3390/ph15050646 PMID: 35631472
  52. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol Divers 2021; 25(3): 1315-60. doi: 10.1007/s11030-021-10217-3 PMID: 33844136
  53. Fang J, Zhang P, Wang Q, et al. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease. Alzheimers Res Ther 2022; 14(1): 7. doi: 10.1186/s13195-021-00951-z PMID: 35012639
  54. Winchester LM, Harshfield EL, Shi L, et al. Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia. Alzheimers Dement 2023; 19(12): 5860-71. doi: 10.1002/alz.13390 PMID: 37654029
  55. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26(1): 80-93. doi: 10.1016/j.drudis.2020.10.010 PMID: 33099022
  56. Subramanian M, Wojtusciszyn A, Favre L, et al. Precision medicine in the era of artificial intelligence: Implications in chronic disease management. J Transl Med 2020; 18(1): 472. doi: 10.1186/s12967-020-02658-5 PMID: 33298113
  57. Vatansever S, Schlessinger A, Wacker D, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41(3): 1427-73. doi: 10.1002/med.21764 PMID: 33295676
  58. Ren F, Aliper A, Chen J, et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat Biotechnol 2024; 2024: 02143-0. doi: 10.1038/s41587-024-02143-0 PMID: 38459338
  59. Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020; 25(22): 5277. doi: 10.3390/molecules25225277 PMID: 33198233
  60. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019; 37(9): 1038-40. doi: 10.1038/s41587-019-0224-x PMID: 31477924
  61. Amarreh I, Meyerand ME, Stafstrom C, Hermann BP, Birn RM. Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging. Neuroimage Clin 2014; 4: 757-64. doi: 10.1016/j.nicl.2014.02.006 PMID: 24936426
  62. Shi C, Dong F, Zhao G, Zhu N, Lao X, Zheng H. Applications of machine-learning methods for the discovery of NDM-1 inhibitors. Chem Biol Drug Des 2020; 96(5): 1232-43. doi: 10.1111/cbdd.13708 PMID: 32418370
  63. Zoffmann S, Vercruysse M, Benmansour F, et al. Machine learning-powered antibiotics phenotypic drug discovery. Sci Rep 2019; 9(1): 5013. doi: 10.1038/s41598-019-39387-9 PMID: 30899034
  64. Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25(3): 1517-39. doi: 10.1007/s11030-021-10274-8 PMID: 34282519
  65. Hu Y, Zhou G, Zhang C, et al. Identify compounds’ target against Alzheimer’s disease based on in silico approach. Curr Alzheimer Res 2019; 16(3): 193-208. doi: 10.2174/1567205016666190103154855 PMID: 30605059
  66. Jamal S, Grover A, Grover S. Machine learning from molecular dynamics trajectories to predict caspase-8 inhibitors against Alzheimer’s disease. Front Pharmacol 2019; 10: 780. doi: 10.3389/fphar.2019.00780 PMID: 31354494
  67. Xie C, Zhuang XX, Niu Z, et al. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow. Nat Biomed Eng 2022; 6(1): 76-93. doi: 10.1038/s41551-021-00819-5 PMID: 34992270
  68. Alokam R, Singhal S, Srivathsav GS, et al. Design of dual inhibitors of ROCK-I and NOX2 as potential leads for the treatment of neuroinflammation associated with various neurological diseases including autism spectrum disorder. Mol Biosyst 2015; 11(2): 607-17. doi: 10.1039/C4MB00570H PMID: 25465055
  69. Simpson DSA, Oliver PL. ROS generation in microglia: Understanding oxidative stress and inflammation in neurodegenerative disease. Antioxidants 2020; 9(8): 743. doi: 10.3390/antiox9080743 PMID: 32823544
  70. Khezri MR, Ghasemnejad-Berenji M. The role of caspases in Alzheimer’s disease: Pathophysiology implications and pharmacologic modulation. J Alzheimers Dis 2023; 91(1): 71-90. doi: 10.3233/JAD-220873 PMID: 36442198
  71. Alkanli SS, Alkanli N, Ay A, Albeniz I. CRISPR/Cas9 mediated therapeutic approach in Huntington’s disease. Mol Neurobiol 2023; 60(3): 1486-98. doi: 10.1007/s12035-022-03150-5 PMID: 36482283
  72. Imbriani P, Tassone A, Meringolo M, et al. Loss of non-apoptotic role of caspase-3 in the PINK1 mouse model of Parkinson’s disease. Int J Mol Sci 2019; 20(14): 3407. doi: 10.3390/ijms20143407 PMID: 31336695
  73. Brahadeeswaran S, Sivagurunathan N, Calivarathan L. Inflammasome signaling in the aging brain and age-related neurodegenerative diseases. Mol Neurobiol 2022; 59(4): 2288-304. doi: 10.1007/s12035-021-02683-5 PMID: 35066762
  74. Cancela S, Canclini L, Mourglia-Ettlin G, Hernández P, Merlino A. Neuroprotective effects of novel nitrones: In vitro and in silico studies. Eur J Pharmacol 2020; 871: 172926. doi: 10.1016/j.ejphar.2020.172926 PMID: 31958456
  75. Xing Y, Li A, Yang Y, Li X, Zhang L, Guo H. The regulation of FOXO1 and its role in disease progression. Life Sci 2018; 193: 124-31. doi: 10.1016/j.lfs.2017.11.030 PMID: 29158051
  76. Zhang W, Bai S, Yang J, et al. FoxO1 overexpression reduces Aβ production and tau phosphorylation in vitro. Neurosci Lett 2020; 738: 135322. doi: 10.1016/j.neulet.2020.135322 PMID: 32860886
  77. Zhang W, Bai SS, Zhang Q, et al. Physalin B reduces Aβ secretion through down-regulation of BACE1 expression by activating FoxO1 and inhibiting STAT3 phosphorylation. Chin J Nat Med 2021; 19(10): 732-40. doi: 10.1016/S1875-5364(21)60090-0 PMID: 34688463
  78. Cooper N, Ghanima W, Hill QA, Nicolson PLR, Markovtsov V, Kessler C. Recent advances in understanding spleen tyrosine kinase (SYK) in human biology and disease, with a focus on fostamatinib. Platelets 2023; 34(1): 2131751. doi: 10.1080/09537104.2022.2131751 PMID: 36331249
  79. Ennerfelt H, Frost EL, Shapiro DA, et al. SYK coordinates neuroprotective microglial responses in neurodegenerative disease. Cell 2022; 185(22): 4135-4152.e22. doi: 10.1016/j.cell.2022.09.030 PMID: 36257314
  80. Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin Drug Discov 2021; 16(9): 949-59. doi: 10.1080/17460441.2021.1909567 PMID: 33779453
  81. Dorahy G, Chen JZ, Balle T. Computer-aided drug design towards new psychotropic and neurological drugs. Molecules 2023; 28(3): 1324. doi: 10.3390/molecules28031324 PMID: 36770990
  82. Song N, Sun S, Chen K, et al. Emerging nanotechnology for Alzheimer’s disease: From detection to treatment. J Control Release 2023; 360: 392-417. doi: 10.1016/j.jconrel.2023.07.004 PMID: 37414222

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