Unveiling Cathepsin B inhibition with repurposed drugs for anticancer and anti-Alzheimer's drug discovery.
basic_science · Level V
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- Record sourced from PubMed, PMID 39700174.
- Also identified by DOI 10.1371/journal.pone.0316010 and PMC identifier 11658610.
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Abstract
Alzheimer's disease (AD) is characterized by the aggregation of amyloid β (Aβ) peptides and the formation of plaques in the brain, primarily derived from the proteolytic degradation of amyloid precursor protein (APP). Cathepsin B (CatB) is a cysteine protease that plays a pivotal role in this process, making it a potential target for the development of anti-Alzheimer's therapies. Apart from AD, CatB is implicated in various physiological and pathological processes, including cancer. Given the critical role of CatB in these diseases, identifying effective inhibitors is of significant therapeutic interest. In this study, we employed a systematic virtual screening approach using repurposed molecules from the DrugBank database to identify potential CatB inhibitors. Primarily, we focused on binding affinities and selectivity to pinpoint potential hits against CatB. Two repurposed molecules, Lurasidone and Paliperidone, emerged as promising candidates with significant affinity for CatB. These molecules demonstrated favorable drug profiles and exhibited preferential binding to the catalytic pocket of CatB via interacting with functionally significant residues. To further explore the binding mechanism and stability of the CatB-drug complexes, molecular dynamics (MD) simulations were conducted for 500 ns. The results revealed that CatB and Lurasidone, as well as Paliperidone, form stable complexes throughout the simulation. Taken together, the findings suggest that Lurasidone and Paliperidone can act as repurposed CatB inhibitors with potential applications in the development of therapeutics against AD and other CatB-associated diseases after further validation.
Medical subject headings
- Cathepsin B
- Alzheimer Disease
- Drug Repositioning
- Antineoplastic Agents
- Molecular Dynamics Simulation
- Drug Discovery