Maths & medicine go hand-in-hand

Developing treatments for SCI might be broken down into different phases. It begins with understanding what a SCI actually is – what happens after the immediate injury and changes that occur in the spinal cord during the following months – and from there identify which of the various changes or processes might be amenable to treatment interventions – in other words, identify a therapeutic target. This needs to be tested to establish the proof of principle that this target might be beneficial to outcome which might mean switching off a gene that makes a protein that you believe is one of the reasons neurons fail to regenerate after injury.

But there are so many things happening in the cord after injury and so many possible interacting processes, that identifying one single target is extremely difficult. This is why researchers appear to be working on lots of different concepts generating data. It is not that one group is right and all the others are barking up the wrong tree, it is that each concept may well have the potential to be helpful and the question really comes down to which one is most helpful or most central to repair.

In addition, spinal cord injury (SCI) really needs to be thought of as a syndrome with loss of mobility, sensation, loss of bladder/bowel and sexual function and pain amongst the most devastating. As a consequence we have had to develop many models to emulate these so that we can test the effectiveness of potential treatments.
Given the sheer complexity of SCI it is little wonder it has taken decades of basic research to get to the stage we are now. In the last 20 years in particular we have seen significant progress but few treatments have been successfully translated to the clinic for evaluation in humans. One major obstacle to translation is not being able to compare data from one lab or animal model or species because if we could we would be better placed to make judgements on the robustness of an experimental treatment and the “likelihood” it will work on the most important species we need to consider – humans.

Adam Ferguson is trying to address this problem. He is collecting data from many labs (with their different injury model, severities, species etc.) and looking for consistent patterns in that data – using sophisticated mathematical techniques – so that he can identify measurable outcomes that are most sensitive to the species used, most sensitive to the severity of the injury and those which are most sensitive to the changes over time after injury. It requires huge amounts of data and he has collected data from studies reaching back 9 years to achieve this. Experimental treatments that “shine” within these patterns are more likely to translate to humans.