Inter-neuronal variability in human nociceptor electrophysiology: experimentally-driven computational study of response to drugs and channelopathies

Animal models of dorsal root ganglion (DRG) neurons are widely used in pain research as in vitro models of human nociception, due to a lack of human-specific alternatives.  However, these models do not capture human-specific electrophysiology, including differences in ion channel function, and do not address significant inter-neuronal variability, e.g. differences in ion channel expression and action potential morphology between DRG neuron sub-types. This heterogeneity is difficult to address through experiments alone but can result in variable responses to therapies and disease.

We have developed a method for integrating biological variability with in silico modelling, using experimentally-calibrated populations of models, and have used this approach extensively in cardiac electrophysiology. We propose integrating new recordings of human DRG neuron electrophysiology, provided by our collaboration with Anabios Corporation, with our methodology to construct and validate populations of in silico human DRG models that include inter-neuronal electrophysiological variability at two levels: variability between individual neurons, and variability between neuronal sub-types as determined by sensitivity to noxious stimuli (e.g. heat, irritating chemicals or cold). Mutations in specific sodium channel isoforms (e.g. Nav 1.7, 1.8 and 1.9) can lead to chronic pain and insensitivity to pain. This finding has spurred development of selective blockers of these channels. However, the effects of blocking these channels and the underlying mechanisms of these mutations are not fully understood. We propose using populations of human DRG models to improve on and replace existing animal DRG experiments that are used to predict the range of responses to ion channel blocking drugs, and to understand the mechanisms that link mutations in individual ion channels with their effects on DRG neuron excitability.

• Engagement Activities: Pint of Science 2017