Unveiling the Multitarget Anti-Alzheimer Drug Discovery Landscape: A Bibliometric Analysis.
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Sampietro, A., Pérez-Areales, F. J., Martínez, P., Arce, E. M., Galdeano, C., & Muñoz-Torrero, D. (2022). Unveiling the Multitarget Anti-Alzheimer Drug Discovery Landscape: A Bibliometric Analysis.. Pharmaceuticals (Basel), 15 (5) https://doi.org/10.3390/ph15050545
Multitarget anti-Alzheimer agents are the focus of very intensive research. Through a comprehensive bibliometric analysis of the publications in the period 1990-2020, we have identified trends and potential gaps that might guide future directions. We found that: (i) the number of publications boomed by 2011 and continued ascending in 2020; (ii) the linked-pharmacophore strategy was preferred over design approaches based on fusing or merging pharmacophores or privileged structures; (iii) a significant number of in vivo studies, mainly using the scopolamine-induced amnesia mouse model, have been performed, especially since 2017; (iv) China, Italy and Spain are the countries with the largest total number of publications on this topic, whereas Portugal, Spain and Italy are the countries in whose scientific communities this topic has generated greatest interest; (v) acetylcholinesterase, β-amyloid aggregation, oxidative stress, butyrylcholinesterase, and biometal chelation and the binary combinations thereof have been the most commonly pursued, while combinations based on other key targets, such as tau aggregation, glycogen synthase kinase-3β, NMDA receptors, and more than 70 other targets have been only marginally considered. These results might allow us to spot new design opportunities based on innovative target combinations to expand and diversify the repertoire of multitarget drug candidates and increase the likelihood of finding effective therapies for this devastating disease.
multifactorial diseases, Alzheimer’s disease, polypharmacology, multitarget drugs, hybrids, target combinations, multitarget drug design, animal models
Ministerio de Ciencia e Innovación (MCIN) / Agencia Estatal de Investigación (AEI) / ERDF (PID2020-118127RB-I00)
AGAUR (2017SGR106 / 2019LLAV00017)
External DOI: https://doi.org/10.3390/ph15050545
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336651