Novel Biochanin A Carbamate Derivatives as Multifunctional Anti‐Alzheimer's Disease Metal Chelators: Design, Synthesis, and Activity Evaluation
Considerable evidence suggests that metal ions play crucial roles in Alzheimer's disease (AD) progression, spurring the development of metal-involved therapeutic strategies. In this study, biochanin A, a bioactive ingredient derived fromTrifolium pratense L. (a traditional Chinese medicine [TCM]), was identified as a lead compound for the rational design of multifunctional metal chelators. A series of biochanin A derivatives (compounds a-e) was synthesized through a one-step carbamate introduction reaction. Their drug-likeness and pharmacokinetic profiles were assessed using an online prediction server. The bioactivities, including metal ion chelation, β-amyloid aggregation regulation, reactive oxygen species elimination, cholinesterase inhibition, and neurocytotoxicity were evaluated. The results indicated that compounds (a-e) possessed favorable pharmacokinetic profiles and potential as multifunctional metal chelators with minimal neurocytotoxicity, with compound d emerging as a particularly promising candidate. These findings may provide meaningful information for developing novel multifunctional metal chelators for AD treatment and innovative drugs derived from TCM.
Authors
- Xinjie Jiang
- Ai‐Hong Yang (ORCID: https://orcid.org/0000-0002-0827-655X)
- Xiaodi Kou (ORCID: https://orcid.org/0000-0001-6394-5819)
- Caiyun Wang (ORCID: https://orcid.org/0000-0003-2542-7425)
- Yizhen Sun (ORCID: https://orcid.org/0000-0002-2794-7195)
- Rui Shen (ORCID: https://orcid.org/0000-0001-8491-0471)
- Xiangyu Zhang (ORCID: https://orcid.org/0000-0002-9544-2500)
- Xiaoyue Yi
Institutions
- Tianjin University of Traditional Chinese Medicine (CN)
- IE University (ES)
- Guizhou University (CN)
- Mianyang Institute of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Chemistry & Biodiversity
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1002/cbdv.202501827
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00