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Maximising the translational potential of neurophysiology in amyotrophic lateral sclerosis: a study on compound muscle action potentials

McKinnon, S.; Qiang, Z.; Keerie, A.; Wells, T.; Shaw, P.; Alix, J.; Mead, R.

2024-05-10 neuroscience
10.1101/2024.05.09.593349 bioRxiv
Show abstract

Transgenic mouse models of amyotrophic lateral sclerosis, such as the widely used SOD1G93A mouse, enable investigation of disease mechanisms and testing of novel therapeutic interventions. However, treatments that have been considered successful in mice have often failed to translate into human benefit in clinical trials, particularly when relying on the so-called survival read-out. Compound muscle action potentials (CMAPs), are a simple neurophysiological test that measures the summation of muscle fibre depolarisation in response to maximal stimulation of the innervating nerve. CMAPs can be measured in both mice and humans and decline with motor axon loss in ALS, making them a potential translational read-out of disease progression which could help bridge the preclinical and clinical divide. Herein we assess the translational potential of CMAPs and ascertain at what time points human and mouse data aligned most closely. We extracted data from 18 human studies and compared with results generated from SOD1G93A and control mice at different ages across different muscles. We found that the relative CMAP amplitude difference between SOD1G93A and control mice in tibialis anterior and gastrocnemius muscles at 70 days of age was most similar to the relative difference between baseline ALS patient CMAP measurements and healthy controls in the abductor pollicis brevis (APB) muscle. We also found that the relative decline in SOD1G93A tibialis anterior CMAP amplitude between 70-140 days was similar to that observed in 12 month human longitudinal studies in APB. Our findings suggest CMAP amplitudes can provide a translational window, from which to make comparisons between the SOD1G93A model and human ALS patients. CMAPs are easy to perform and can help determine the most clinically relevant starting/end points for preclinical studies and provide a basis for predicting potential clinical effect sizes.

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