journal article·2024-08Open access

Challenges and Future Perspectives in Modeling Neurodegenerative Diseases Using Organ‐on‐a‐Chip Technology

Francesca Michela Pramotton, Sarah Spitz, Roger D. Kamm

Advanced Science· Volume 11· Issue 32

Abstract

Abstract Neurodegenerative diseases (NDDs) affect more than 50 million people worldwide, posing a significant global health challenge as well as a high socioeconomic burden. With aging constituting one of the main risk factors for some NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD), this societal toll is expected to rise considering the predicted increase in the aging population as well as the limited progress in the development of effective therapeutics. To address the high failure rates in clinical trials, legislative changes permitting the use of alternatives to traditional pre‐clinical in vivo models are implemented. In this regard, microphysiological systems (MPS) such as organ‐on‐a‐chip (OoC) platforms constitute a promising tool, due to their ability to mimic complex and human‐specific tissue niches in vitro. This review summarizes the current progress in modeling NDDs using OoC technology and discusses five critical aspects still insufficiently addressed in OoC models to date. Taking these aspects into consideration in the future MPS will advance the modeling of NDDs in vitro and increase their translational value in the clinical setting.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Neuroscience and Neural Engineering · 3D Printing in Biomedical Research · Molecular Communication and Nanonetworks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering and Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA

Funding

National Institutes of Health · Cure Alzheimer's Fund · Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

References (100+)

Show source-provided references
1. 10.1101/cshperspect.a02803510.1101/cshperspect.a028035 ↗
2. 10.1002/alz.1301610.1002/alz.13016 ↗
3. 10.1016/j.cell.2022.12.03210.1016/j.cell.2022.12.032 ↗
4. 10.1159/00049581310.1159/000495813 ↗
5. The Lancet Public Health Will dementia hamper healthy ageing?2022 7 e93.10.1016/S2468-2667(22)00009-3 ↗
6. 10.1038/s41467-024-46554-810.1038/s41467-024-46554-8 ↗
7. 10.1038/s41598-022-21820-110.1038/s41598-022-21820-1 ↗
8. 10.1111/aor.1450310.1111/aor.14503 ↗
9. 10.1016/j.drudis.2023.10349610.1016/j.drudis.2023.103496 ↗
10. 10.1016/j.toxlet.2023.09.01110.1016/j.toxlet.2023.09.011 ↗
11. 10.1172/JCI17582410.1172/JCI175824 ↗
12. 10.1038/s41573-020-0079-310.1038/s41573-020-0079-3 ↗
13. 10.1146/annurev‐bioeng‐08212010.1146/annurev‐bioeng‐082120 ↗
14. 10.1038/s41467-021-24775-510.1038/s41467-021-24775-5 ↗
15. 10.1016/j.biomaterials.2018.07.01410.1016/j.biomaterials.2018.07.014 ↗
16. 10.1038/s41567-018-0046-710.1038/s41567-018-0046-7 ↗
17. S.Spitz S.Bolognin K.Brandauer J.Füßl P.Schuller S.Schobesberger bioRxiv 2022 2022.
18. 10.1038/s41467-021-26066-510.1038/s41467-021-26066-5 ↗
19. 10.1002/advs.20190096210.1002/advs.201900962 ↗
20. 10.1016/j.stem.2019.05.01110.1016/j.stem.2019.05.011 ↗
21. 10.1039/C6LC00638H10.1039/C6LC00638H ↗
22. 10.1039/C7LC00682A10.1039/C7LC00682A ↗
23. 10.1038/nbt.422610.1038/nbt.4226 ↗
24. 10.1039/C4LC00962B10.1039/C4LC00962B ↗
25. 10.3389/fbioe.2023.125119510.3389/fbioe.2023.1251195 ↗
26. 10.1038/s41593-018-0175-410.1038/s41593-018-0175-4 ↗
27. 10.1002/advs.20180092710.1002/advs.201800927 ↗
28. 10.1038/s41467-023-39038-810.1038/s41467-023-39038-8 ↗
29. 10.1126/sciadv.aat584710.1126/sciadv.aat5847 ↗
30. 10.1002/adbi.20180030710.1002/adbi.201800307 ↗
31. 10.1126/scitranslmed.300374810.1126/scitranslmed.3003748 ↗
32. 10.1038/nature1443210.1038/nature14432 ↗
33. 10.1152/physrev.00031.202010.1152/physrev.00031.2020 ↗
34. 10.1159/00049034910.1159/000490349 ↗
35. 10.1007/s11064-015-1581-610.1007/s11064-015-1581-6 ↗
36. 10.1146/annurev-pathol-051217-11101810.1146/annurev-pathol-051217-111018 ↗
37. 10.1016/j.tins.2020.04.00310.1016/j.tins.2020.04.003 ↗
38. 10.1007/s11095-022-03241-x10.1007/s11095-022-03241-x ↗
39. 10.1101/cshperspect.a02041210.1101/cshperspect.a020412 ↗
40. 10.1177/107385842095482610.1177/1073858420954826 ↗
41. 10.1038/s41590-021-00994-210.1038/s41590-021-00994-2 ↗
42. 10.1186/s40035-019-0147-y10.1186/s40035-019-0147-y ↗
43. 10.1007/s00381-013-2321-310.1007/s00381-013-2321-3 ↗
44. 10.1007/s00401-009-0627-810.1007/s00401-009-0627-8 ↗
45. 10.1038/s41582-020-0312-z10.1038/s41582-020-0312-z ↗
46. 10.1073/pnas.95.20.1198110.1073/pnas.95.20.11981 ↗
47. 10.1152/physrev.00027.200710.1152/physrev.00027.2007 ↗
48. 10.1152/ajprenal.1981.240.4.F31910.1152/ajprenal.1981.240.4.F319 ↗
49. 10.1007/s00018-020-03706-510.1007/s00018-020-03706-5 ↗
50. 10.1002/aja.100062010610.1002/aja.1000620106 ↗
51. 10.3171/jns.1965.22.5.048110.3171/jns.1965.22.5.0481 ↗
52. 10.1001/archneur.1972.0049017000300210.1001/archneur.1972.00490170003002 ↗
53. 10.1002/ar.109075030810.1002/ar.1090750308 ↗
54. 10.1113/jphysiol.1983.sp01473110.1113/jphysiol.1983.sp014731 ↗
55. 10.1038/s41586-019-1419-510.1038/s41586-019-1419-5 ↗
56. 10.1038/s41467-017-01484-610.1038/s41467-017-01484-6 ↗
57. 10.1084/jem.2014229010.1084/jem.20142290 ↗
58. 10.1148/104.2.32110.1148/104.2.321 ↗
59. 10.3389/fphar.2019.0165610.3389/fphar.2019.01656 ↗
60. 10.1186/s40035-022-00291-410.1186/s40035-022-00291-4 ↗
61. 10.1016/j.molmed.2019.11.00810.1016/j.molmed.2019.11.008 ↗
62. 10.1111/ene.1217610.1111/ene.12176 ↗
63. 10.1186/s13024-015-0056-110.1186/s13024-015-0056-1 ↗
64. 10.1186/s13195-020-00688-110.1186/s13195-020-00688-1 ↗
65. 10.1016/j.brainresbull.2018.10.00710.1016/j.brainresbull.2018.10.007 ↗
66. 10.2967/jnumed.116.18721110.2967/jnumed.116.187211 ↗
67. 10.1038/s41586-018-0368-810.1038/s41586-018-0368-8 ↗
68. 10.1111/nan.1242010.1111/nan.12420 ↗
69. 10.1111/j.1749-6632.1991.tb00182.x10.1111/j.1749-6632.1991.tb00182.x ↗
70. 10.1016/j.nbd.2016.05.01510.1016/j.nbd.2016.05.015 ↗
71. 10.1038/s41591-020-01198-110.1038/s41591-020-01198-1 ↗
72. Chen H. L. · Oxid. Med. Cell. Longev. · 2021 · 2021 · 1
73. 10.1111/bpa.1236910.1111/bpa.12369 ↗
74. 10.1016/j.neures.2020.10.00610.1016/j.neures.2020.10.006 ↗
75. 10.1016/j.neures.2017.11.00110.1016/j.neures.2017.11.001 ↗
76. 10.1523/JNEUROSCI.1865-20.202010.1523/JNEUROSCI.1865-20.2020 ↗
77. 10.1038/nrd.2015.2110.1038/nrd.2015.21 ↗
78. 10.1186/s12951-023-01798-210.1186/s12951-023-01798-2 ↗
79. 10.3390/mi1206068510.3390/mi12060685 ↗
80. 10.1016/j.mtbio.2023.10077310.1016/j.mtbio.2023.100773 ↗
81. 10.1002/adbi.20220002710.1002/adbi.202200027 ↗
82. 10.1002/advs.20230290310.1002/advs.202302903 ↗
83. 10.3390/ijms2403217110.3390/ijms24032171 ↗
84. Lenz K. M. · Front. Immunol. · 2018 · 9
85. 10.1038/nri370510.1038/nri3705 ↗
86. 10.1146/annurev-immunol-093019-11015910.1146/annurev-immunol-093019-110159 ↗
87. 10.1126/science.119463710.1126/science.1194637 ↗
88. 10.1038/nn.331810.1038/nn.3318 ↗
89. 10.1167/iovs.05-152210.1167/iovs.05-1522 ↗
90. 10.1002/glia.2114010.1002/glia.21140 ↗
91. 10.1016/j.stem.2010.08.01410.1016/j.stem.2010.08.014 ↗
92. 10.1523/JNEUROSCI.5925-11.201210.1523/JNEUROSCI.5925-11.2012 ↗
93. 10.1523/JNEUROSCI.4363-08.200910.1523/JNEUROSCI.4363-08.2009 ↗
94. 10.1371/journal.pbio.100052710.1371/journal.pbio.1000527 ↗
95. 10.1016/j.cell.2013.11.03010.1016/j.cell.2013.11.030 ↗
96. 10.1038/s41423-021-00751-310.1038/s41423-021-00751-3 ↗
97. 10.1038/nrn372210.1038/nrn3722 ↗
98. 10.1038/nature1075910.1038/nature10759 ↗
99. 10.1111/j.1471-4159.2007.05001.x10.1111/j.1471-4159.2007.05001.x ↗
100. 10.1002/glia.2243710.1002/glia.22437 ↗