Selecting the Best Animal Model of Parkinson’s Disease for Your Research
Purpose: Insight from in vivo PET Imaging Studies

Caroline   Cristiano   Real; Karina   Henrique   Binda; Majken   Borup   Thomsen; Thea   Pinholt   Lillethorup; David   James   Brooks; Anne   Marlene   Landau
doi:10.2174/1570159X21666230216101659
Abstract

Parkinson’s disease (PD) is a debilitating neurodegenerative multisystem disorder leading to motor and non-motor symptoms in millions of individuals. Despite intense research, there is still no cure, and early disease biomarkers are lacking. Animal models of PD have been inspired by basic elements of its pathogenesis, such as dopamine dysfunction, alpha-synuclein accumulation, neuroinflammation and disruption of protein degradation, and these have been crucial for a deeper understanding of the mechanisms of pathology, the identification of biomarkers, and evaluation of novel therapies. Imaging biomarkers are non-invasive tools to assess disease progression and response to therapies; their discovery and validation have been an active field of translational research. Here, we highlight different considerations of animal models of PD that can be applied to future research, in terms of their suitability to answer different research questions. We provide the reader with important considerations of the best choice of model to use based on the disease features of each model, including issues related to different species. In addition, positron emission tomography studies conducted in PD animal models in the last 5 years are presented. With a variety of different species, interventions and genetic information, the choice of the most appropriate model to answer research questions can be daunting, especially since no single model recapitulates all aspects of this complex disorder. Appropriate animal models in conjunction with in vivo molecular imaging tools, if selected properly, can be a powerful combination for the assessment of novel therapies and developing tools for early diagnosis.
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Graphical Abstract

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DOI https://dx.doi.org/10.2174/1570159X21666230216101659	Print ISSN 1570-159X
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