Integrative Biomedical Research

Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN  3068-6326
463
Citations
1.9m
Views
792
Articles
Your new experience awaits. Try the new design now and help us make it even better
Switch to the new experience
REVIEWS   (Open Access)

Md Abu Bakar Siddique1*, Moushumi Afroza Mou1, Asim Debnath2, Md Sefaut Ullah3, Md Sakil Amin4, Azizur Rahman5

+ Author Affiliations

Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210956

Submitted: 01 August 2026 Revised: 25 September 2026  Accepted: 03 October 2026  Published: 05 October 2026 


Abstract

Alzheimer’s disease has, for decades, been chased primarily down the amyloid road, yet the clinical picture keeps insisting otherwise: it is tau pathology, not plaque burden alone, that tracks most closely with synaptic failure and cognitive decline. This review takes stock of a field that has shifted rapidly in the last several years, as cryogenic electron microscopy and AI-driven structure prediction — AlphaFold, AlphaFold 3, and RoseTTAFold among them — have converted tau from an intractable, intrinsically disordered protein into something resembling a druggable target. We trace the pathological cascade from soluble hyperphosphorylated monomer through the recently described ‘first intermediate amyloid’ to the polymorphic, disease-specific fibril folds that distinguish Alzheimer’s disease from Pick’s disease, chronic traumatic encephalopathy, and related tauopathies. We then examine, modality by modality, how this structural knowledge is being translated into candidate therapeutics: small-molecule aggregation inhibitors and coumarin intercalators, multitarget-directed ligands, retro-inverso peptidomimetics capable of dual-capping the 275VQIINK280 and 306VQIVYK311 hotspots, conformation-specific nanobodies and antibodies, and proteolysis-targeting chimeras engineered for catalytic tau clearance. Persistent obstacles are examined candidly, including AlphaFold’s documented weaknesses for disordered ensembles and D-amino acid chirality, and the blood–brain barrier, which continues to exclude the great majority of otherwise promising candidates despite real advances in nanocarrier and nose-to-brain delivery engineering. Bringing these threads together, we propose an integrated, closed-loop framework — pairing generative AI with molecular dynamics, biophysical validation, and biomarker-stratified trial design — as a plausible route toward disease-modifying, structurally rational tau therapeutics within the coming decade.

Keywords: Tau protein; Alzheimer’s disease; structure-based drug design; AlphaFold; cryo-electron microscopy; PROTAC; blood–brain barrier

References

Abskharon, R., Pan, H., Sawaya, M. R., Seidler, P. M., Olivares, E. J., Chen, Y., Murray, K. A., Zhang, J., Lantz, C., Bentzel, M., & Eisenberg, D. S. (2023). Structure-based design of nanobodies that inhibit seeding of Alzheimer’s patient–extracted tau fibrils. Proceedings of the National Academy of Sciences, 120(18), e2300258120. https://doi.org/10.1073/pnas.2300258120

Al Khzem, A. H., & Gomaa, M. S. (2026). Peptide-based therapeutics for Alzheimer’s disease: Medicinal chemistry, AI-guided computational design, and blood–brain barrier delivery. Drug Design, Development and Therapy, 20, 597087.

Arar, S., Haque, M. A., & Kayed, R. (2023). Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics. Proteins: Structure, Function, and Bioinformatics, 91(10), 1314–1335. https://doi.org/10.1002/prot.26561

Arslan, S., Yilmaz, D., Altay, A., & Capan, S. (2026). Recent advances in pyrazole-based cholinesterase and kinase inhibitors as multi-target-directed ligands for Alzheimer’s disease. Pharmaceuticals, 19(7), 1079.

Cehlar, O., Njemoga, S., Horvath, M., Cizmazia, E., Bednarikova, Z., & Barrera, E. E. (2024). Structures of oligomeric states of tau protein, amyloid-β, α-synuclein and prion protein implicated in Alzheimer’s disease, Parkinson’s disease and prionopathies. International Journal of Molecular Sciences, 25(23), 13049. https://doi.org/10.3390/ijms252313049

Datta, D., Perone, I., Wijegunawardana, D., Liang, F., Morozov, Y., Arellano, J., Duque, A., Xie, Z., Van Dyck, C., Joyce, M. K., & Arnsten, A. (2023). Nanoscale imaging of pT217-tau in aged rhesus macaque entorhinal and dorsolateral prefrontal cortex: Evidence of interneuronal trafficking and early-stage neurodegeneration. Alzheimer’s & Dementia, 19(S17), e082021. https://doi.org/10.1101/2023.11.07.566046

Dominguez-Gortaire, J., Ruiz, A., Porto-Pazos, A. B., Rodriguez-Yanez, S., & Cedron, F. (2025). Alzheimer’s disease: Exploring pathophysiological hypotheses and the role of machine learning in drug discovery. International Journal of Molecular Sciences, 26(3), 1004. https://doi.org/10.3390/ijms26031004

Esmaeilpour, D., Hamblin, M. R., Cheng, J., Khosravi, A., Liu, J., Zarepour, A., Zarrabi, A., Sillanpää, M., Zare, E. N., Shen, J., & Karimi-Maleh, H. (2026). AI-assisted protein engineering and structural modeling in neurodegenerative therapeutics. Bioactive Materials, 60, 425–455. https://doi.org/10.1016/j.bioactmat.2026.01.012

Fang, Z., Ran, H., Zhang, Y., Chen, C., Lin, P., Zhang, X., et al. (2025). Integration of proteoformics and deep learning structure prediction in precision medicine. Journal of Proteomics, 321, 105524. https://doi.org/10.1016/j.jprot.2025.105524

Habtemariam, S. (2026). Natural products as a pipeline for next-generation neurodegenerative drugs: From single-target failure to multi-target opportunity in Alzheimer’s and Parkinson’s disease. Molecules, 31(7), 1489. https://doi.org/10.3390/molecules31071489

Huang, B., Li, J., Li, Z., Jiang, H., Yang, M., Li, X., & Niu, X. (2026). EvoPlay-MuZero hybrid framework incorporating a dual-peptide bridging strategy for adjunctive therapy in Alzheimer’s disease. Information, 17(7), 708. https://doi.org/10.3390/info17070708

Jeyaraj, G., Rajendran, A. K., Sathishkumar, K., Almutairi, B. O., Vadivelu, A., Chokkakula, S., Tu, Y., & Xie, W. (2025). High-resolution protein modeling through cryo-EM and AI: Current trends and future perspectives. Frontiers in Molecular Biosciences, 12, 1688455. https://doi.org/10.3389/fmolb.2025.1688455

Jiang, S., Tian, Z., Yang, Y., Han, H., Tong, Z., & Zhang, P. (2025). AI promotes clinical translational medicine in Alzheimer’s disease. Acta Pharmaceutica Sinica B, 15(8), 5100–5125. https://doi.org/10.1016/j.apsb.2025.08.015

Job, N., Thimmakondu, V. S., & Thirumoorthy, K. (2023). In silico drug design and analysis of dual amyloid-beta and tau protein-aggregation inhibitors for Alzheimer’s disease treatment. Molecules, 28(3), 1388. https://doi.org/10.3390/molecules28031388

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2

Kayed, R., Piccirella, S., & Aguzzi, A. (2024). Nasal tau immunotherapy clears intracellular tau pathology through TRIM21 and improves cognitive functions in aged tauopathy mice. Alzheimer’s & Dementia, 20(S8), LP146.

Lofgren, S. (2024). Brain shuttles to novel receptors to overcome liabilities of first-generation shuttled anti-amyloid therapeutics. Alzheimer’s & Dementia, 20(S8), LP078.

Lövestam, S., Li, D., Wagstaff, J. L., Kotecha, A., Kimanius, D., McLaughlin, S. H., Murzin, A. G., Freund, S. M. V., Goedert, M., & Scheres, S. H. W. (2024). Disease-specific tau filaments assemble via polymorphic intermediates. Nature, 625(7993), 119–125. https://doi.org/10.1038/s41586-023-06788-w

Masri, H. (2026). Natural coumarins as β-sheet intercalators and multi-target-directed ligands for neurodegenerative proteopathies. Chemistry, 8(1), 93.

Mella, J., Vega-Muñoz, A., Soto, M., Moraga, D., Campanini-Salinas, J., Sandoval-Obando, E., Contreras-Barraza, N., Salazar-Sepúlveda, G., Salas-Guzmán, N., Carabantes-Silva, R., & Mellado, M. (2026). Evolution of multitarget strategies for Alzheimer’s disease: From cholinergic inhibition to network-oriented therapeutic design (2006–2025). Pharmaceuticals, 19(5), 1024. https://doi.org/10.3390/ph19051024

Miotto, M., Di Paola, L., & Tosatto, S. C. E. (2025). The rise of AlphaFold and residue interaction networks in structural biology. Trends in Biochemical Sciences, 50(12), 1078–1089. https://doi.org/10.1016/j.tibs.2025.09.004

Peralta Reyes, F. S., Zielinski, M., Gremer, L., & Schröder, G. F. (2023). Cryo-EM structures of amyloid-β fibrils from Alzheimer’s disease mouse models. Alzheimer’s & Dementia, 19(S17), P225.

Pharmaceutics. (2026). Development and evaluation of galantamine nasal spray (GNT-NS P3) for enhanced nose-to-brain delivery in Alzheimer’s disease treatment. Pharmaceutics, 18(6), 885.

Rajkumar, M., Tian, F., Javed, B., Prajapati, B. G., Deepak, P., Girigoswami, K., & Karmegam, N. (2026). Smart biosensing nanomaterials for Alzheimer’s disease: Advances in design and drug delivery strategies to overcome the blood–brain barrier. Biosensors, 16(1), 66. https://doi.org/10.3390/bios16010066

Setiawan, A. W., Choi, J., Park, S., Choi, M., Tjandrawinata, R. R., Hadinata, E., Park, M. N., Ikrar, T., Nurkolis, F., & Kim, B. (2026). Multi-target pharmacological platforms against tauopathy: Neuro-metabolic crosstalk, drug-likeness, and translational challenges. Pharmaceuticals, 19(5), 792.

Taylor, A. I. P., & Radford, S. E. (2026). Amyloid fibril polymorphism: Structural mechanisms of assembly and the links to disease. Current Opinion in Structural Biology, 96, 103245. https://doi.org/10.1016/j.sbi.2026.103245

Tosatto, S. C. E., Barozi, V., & Miotto, M. (2025). Residue interaction networks for capturing conformational diversity and allosteric regulation in protein ensembles. Trends in Biochemical Sciences, 50(12), 1090–1102.

Wu, H., Qiu, Y., Sheng, F., & Wu, Y. (2024). De novo designed TfR1 binding peptide to shuttle antibody therapeutics across blood brain barrier. Alzheimer’s & Dementia, 20(S8), P149.

Zhou, J., & Chen, M. (2026). Orthogonal molecular feature signatures guide multi-target Alzheimer’s drug discovery through Graph Transformer representation learning. Journal of Data-Driven AD Discovery, 3(1), 19.

Zhou, Z., Liu, P., Li, Y., Liu, X., & Li, J. (2026). Application of PROTACs in protein-driven neurodegenerative and autoimmune diseases. Results in Chemistry, 24, 103234. https://doi.org/10.1016/j.rechem.2026.103234


Article metrics
View details
0
Downloads
0
Citations
80
Views

View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
80
View
0
Share