Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Hassan,Mubashir; Abbas,Qamar; Seo,Sung‑Yum; Shahzadi,Saba; Al Ashwal,Hany; Zaki,Nazar; Iqbal,Zeeshan; Moustafa,Ahmed  A.

doi:10.3892/mmr.2018.9044

Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Authors:
- Mubashir Hassan
- Qamar Abbas
- Sung‑Yum Seo
- Saba Shahzadi
- Hany Al Ashwal
- Nazar Zaki
- Zeeshan Iqbal
- Ahmed A. Moustafa
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Affiliations: Department of Biology, College of Natural Sciences, Kongju National University, Gongju, Chungcheongnam 32588, Republic of Korea, Department of Physiology, University of Sindh, Jamshoro 76080, Pakistan, Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan, College of Information Technology, United Arab Emirates University, Al‑Ain 15551, United Arab Emirates, School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW 2751, Australia
Published online on: May 22, 2018 https://doi.org/10.3892/mmr.2018.9044
Pages: 639-655

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Abstract

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

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