Long-term, age-associated activity quantification in the DE50-MD dog model of Duchenne muscular dystrophy (DMD)

Abstract

Animal models with a clinically relevant phenotype remain important for robust evaluation of novel therapeutics for the fatal, X-linked genetic disorder, Duchenne Muscular Dystrophy (DMD). Demonstration of functional improvement is crucial for both patients and regulatory authorities. DMD is associated with a decline in musculoskeletal function with progressive paresis, muscle atrophy and fibrosis: phenotypic features that are also seen in the DE50-MD canine model of DMD. Here we investigate non-invasive methods to quantify changes in activity and behaviour in DE50-MD dogs, using collar-based, tri-axial accelerometers. We measured activity in affected DE50-MD male dogs (3-8 per age point) and littermate wild-type (WT) male controls (3-13 per age point) at monthly intervals from 3 to 18 months of age using Axivity-AX3 accelerometers attached ventrally on each dogs collar. Data were recorded for 48 hours while dogs remained in their kennels with outside runs following their normal routine. Acceleration vector magnitudes were used to derive various activity indicators over a 24-hour period. Mixed model analyses were used to examine differences between affected and WT groups at different ages. DE50-MD dogs activity indicators were significantly higher for % time spent at rest (p<0.001) and significantly lower for all other activity indicators (all p<0.05), when compared to age-matched WT dogs. Sample size calculations reveal that these non-invasive and objective biomarkers offer significant promise for preclinical testing of therapeutics in this model of DMD. Our approach reveals opportunities for cross-model standardisation of activity monitoring methods, applicable to both research and companion animal settings. Summary statementThe DE50-MD dog model of Duchenne muscular dystrophy shows significant age-associated reduction in activity quantified through non-invasive, wearable accelerometers. Activity metrics tested show promise for objective assessment of activity patterns for preclinical trials.