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

Lead Research Organisation: University of Oxford
Department Name: Computer Science

Abstract

Chronic pain affects millions of people worldwide, but currently lacks effective treatments. A potential target for new therapies to relieve chronic pain are the millions of specialised sensory neurons in our bodies, called nociceptors, that detect harmful stimuli and transmit this information to the brain, where it can be perceived as pain. Nociceptors normally provide us with a protective sense of pain that helps us to avoid harm. Recently, patients with chronic neuropathic pain (unwanted pain due to damage to the nervous system) and with complete insensitivity to pain have had their pain states linked to genetic mutations in specific ion channels that are found most commonly in nociceptors. These ion channels are proteins found in the outer membranes of nerve cells, that open and close to control the flow of electrically charged ions, such as sodium and potassium, into and out of the cell. Each nociceptor contains many different ion channels, and the combined flow of electrical current through these channels determines how it responds when we encounter both harmless and harmful stimuli. These findings have shown that there are particular ion channels that play a vital role in determining which stimuli cause each of us to experience pain.

Studies into how these ion channel mutations affect pain signalling, and the search for new drugs that could treat chronic pain by targeting nociceptor ion channels are ongoing. Currently these studies use nociceptors from animals, particularly rats and mice. This is because human nociceptors, donated to research by organ donors, are scarce and we have limited data from them. Unfortunately there is not a perfect match between animal and human nociceptors and ion channels. Therefore findings made using animal models may not translate to human biology. Nociceptors themselves are also highly varied, as they are divided up into different sub-types, sensitive to different stimuli such as heat or cold, and show differences in behaviour between individuals.

To address these problems, the goal of this project is to develop computer models of human nociceptors that are representative of the wide range of variability we see between nociceptor sub-types, and between nociceptors from different individuals. Computational models of nociceptor electrical activity mathematically describe how the different ion channels in a human nociceptor open and close, and how this affects the signalling properties of the nociceptor. The equations in these models are too complex to solve by hand, and must be solved on computers. Usually these models only describe the average behaviour of nociceptors, but instead we have developed a method to construct population of nociceptor models, consisting of thousands of different models, where every model produces behaviours that are within the range of biological variability observed in experiments, but where every model has a slightly different makeup, such as having different densities of each type of ion channel in its membrane. Therefore, each model behaves differently, and responds differently to simulated application of drugs or the insertion of an ion channel mutation.

Mutations and other factors such as nerve injury and inflammation can make nociceptors hyper excitable. This means they can fire signals for longer, or at lower thresholds, leading to unwanted, non-protective pain. Drugs that block particular ion channels could return these nociceptors back to normal excitability, but effective therapies of this kind have yet to be developed. Current findings suggest individual drugs alone may not be sufficient to achieve this, and combinations of drugs might be more effective. We will use computer simulations to screen a wide range of different drug combinations to predict which combinations restore normal excitability to nociceptors exposed to inflammatory agents, and to nociceptors with ion channel mutations linked to the development of neuropathic pain.

Technical Summary

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.

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