A multimodal approach to identify clinically relevant parameters to monitor disease progression in a preclinical model of neuropediatric disease

1 A multimodal approach to identify clinically relevant parameters to monitor 2 disease progression in a preclinical model of neuropediatric disease 3 4 Authors: Tyler B. Johnson 1† , Jon J. Brudvig 1† , Kimmo K. Lehtimäki 2 , Jacob T. Cain 1 , Katherine A. White 1 , Timo 5 Bragge 2 , Jussi Rytkönen 2 , Tuulia Huhtala 2 , Derek Timm 1 , Maria Vihma 2 , Jukka T. Puoliväli 2 , Antti Nurmi 2 *, Jill M. 6 Weimer 1,3 * 7 8 Affiliations: 9 1 Pediatrics and Rare Diseases Group, Sanford Research, Sioux Falls, SD 57104, USA. 10 2 Discovery Research Services, Charles River, Kuopio, Finland. 11 3 Department of Pediatrics, Sanford School of Medicine at the University of South Dakota, Sioux Falls, SD 57104, 12 USA. 13 *To whom correspondence should be addressed: [email protected] and [email protected] 14 †Authors contributed equally 15 16 One Sentence Summary: Principal component analysis identifies a set of clinically relevant parameters able to 17 measure progression of Batten disease in a mouse model. 18 19 Abstract: While research has accelerated the development of new treatments for pediatric neurodegenerative 20 disorders, the ability to demonstrate the long-term efficacy of these therapies has been hindered by the lack of 21 convincing, noninvasive methods for tracking disease progression both in animal models and in human clinical 22 trials. Here, we unveil a new translational platform for tracking disease progression in an animal model of a 23 pediatric neurodegenerative disorder, CLN6-Batten disease. Instead of looking at a handful of parameters or a single 24 “needle in a haystack”, we embrace the idea that disease progression, in mice and patients alike, is a diverse 25 phenomenon best characterized by a combination of relevant biomarkers. Thus, we employed a multi-modal 26 quantitative approach where 144 parameters were longitudinally monitored to allow for individual variability. We 27 use a range of noninvasive neuroimaging modalities and kinematic gait analysis, all methods that parallel those 28 commonly used in the clinic, followed by a powerful statistical platform to identify key progressive anatomical and 29 metabolic changes that correlate strongly with the progression of pathological and behavioral deficits. This 30 innovative, highly sensitive platform can be used as a powerful tool for preclinical studies on neurodegenerative 31 diseases, and provides proof-of-principle for use as a potentially translatable tool for clinicians in the future. 32 33 certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted January 17, 2019. ; https://doi.org/10.1101/522011 doi: bioRxiv preprint

A multimodal approach to identify clinically relevant parameters to monitor disease progression in a preclinical model of neuropediatric disease

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