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Optimal control for anti-abeta treatment in Alzheimer's disease using a reaction-diffusion model
Lee S, Kao CY, Li Z, Dan T, Wu G, Hao W.. Journal of the Royal Society, Interface, 2026-07-01
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs survival and quality of life. While anti-amyloid beta (Aβ) therapies can slow disease progression, their efficacy depends on personalized dosing that maximizes benefits and minimizes risks, such as amyloid-related imaging abnormalities (ARIA). Mathematical modelling offers a powerful tool for understanding AD dynamics and optimizing treatment, yet most models focus solely on temporal behaviour, overlooking spatial heterogeneity within the brain. In this study, we propose a spatially explicit reaction-diffusion model to describe Aβ plaque dynamics. We formulate an optimal control problem to minimize plaque concentration while balancing therapeutic efficacy and treatment risk. Under reasonable assumptions, we establish well-posedness and uniqueness of the optimal solution. A finite element method (FEM)-based numerical framework is developed to compute personalized treatment strategies. Our model is calibrated using longitudinal Aβ positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling estimation of patient-specific parameters, such as growth rate and effective diffusivity. Results show that optimized treatment strategies consistently outperform constant dosing regimens across patient groups, achieving substantial reductions in cumulative amyloid burden while minimizing side effects. This integrated, data-driven framework advances personalized, spatially informed therapeutic optimization for AD.
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APA 7th
Lee, S., Kao, C. Y., Li, Z., Dan, T., Wu, G., & Hao, W. (2026). Optimal control for anti-abeta treatment in Alzheimer's disease using a reaction-diffusion model. Journal of the Royal Society, Interface. https://doi.org/10.1098/rsif.2025.1158
Vancouver
Lee S, Kao CY, Li Z, Dan T, Wu G, Hao W. Optimal control for anti-abeta treatment in Alzheimer's disease using a reaction-diffusion model. Journal of the Royal Society, Interface. 2026. doi:10.1098/rsif.2025.1158. PMID: 42413935.
BibTeX
@article{lee2026-42413935,
author = {Lee, S. and Kao, C. Y. and Li, Z. and Dan, T. and Wu, G. and Hao, W.},
title = {{Optimal control for anti-abeta treatment in Alzheimer's disease using a reaction-diffusion model}},
journal = {Journal of the Royal Society, Interface},
year = {2026},
doi = {10.1098/rsif.2025.1158},
pmid = {42413935},
url = {https://doi.org/10.1098/rsif.2025.1158}
}
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Key findings
- Results show that optimized treatment strategies consistently outperform constant dosing regimens across patient groups, achieving substantial reductions in cumulative amyloid burden while minimizing side effects.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 59/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomized / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 34 of the 58 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of the Royal Society, Interface) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 42413935) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 34 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 34 of the 58 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
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