Spinal Cord Injury

What is SCI? Why does research in this area matter?

Spinal cord injury (SCI) is a rare but devastating condition that not only affects patients but also caregivers and society, diminishing quality of life and necessitating significant resources from the healthcare system. Countless clinical trials have failed to identify effective treatments to improve the underlying injury in patients suffering from SCI. Prior to chasing new treatments, we propose and work on reconsidering the design of traditional clinical trials through different avenues: improving the definition and prediction of outcomes of recovery, maximising the use of current and historical data available in the field and investigating drug repositioning as a new drug discovery strategy.

Biomarker Discovery

Accurately predicting the extent of functional recovery after a spinal cord injury has proven to be particularly challenging. To date, the only meaningful indication of the extent to which a patient will recover functionally is the sparing of sensory and motor fibers at the time of injury, as measured by standard neurological examination techniques. The overarching aim of this project is therefore to investigate the role of blood biomarkers in predicting the long-term recovery of neurological function after spinal cord injury.

In collaboration with John Kramer (ICORD, UBC, Vancouver, Canada). See funding.

Drug Repositioning

Identifying relevant targets to improve outcome post SCI remains a priority area of research. The typical approach is to seek out novel treatments, notably through randomized clinical trials. However, it overlooks the possibility that some drugs, already routinely prescribed after SCI for the management of secondary complications such as pain or infection, may have unintended detrimental or beneficial effects on the recovery of the SCI itself. Repositioning (or repurposing) has emerged as a successful strategy in other fields (e.g., multiple sclerosis) to improve outcomes in the absence of novel therapies. Our aim is to adapt this strategy to the field of SCI research to formulate hypotheses on existing molecules that could improve patients’ outcomes.

A shiny app illustrating the benchmarking of medications prescribed as standard of care is available, see external page shiny app. See funding. 

Benchmarking Acute Spinal Cord Injury

Recovery after spinal cord injury is very heterogeneous and can occur in different dimensions, including changes to the neurological status and improvements in function due to adaptation. Further, strong predictors of recovery remain relatively scarce. This project aims to better characterise recovery after spinal cord injury by constructing typical recovery profiles to understand the temporal and physiological patterns underlying improvements. With this additional information, we hope to be able to formulate new hypotheses about mechanisms driving recovery, which, in turn, can be used to advance the development of new treatments. Further, we expect that new insights into the recovery process and the integration of multiple data modalities, like surface electromyography, can help to improve recovery prediction. Overall, this project will provide essential building blocks to achieve our goal of optimising individual recovery.

In collaboration with Balgrist University Hospital and the Indian Spinal Injuries Center. See funding.

Wearable Inertial Sensors to Evaluate the Quality of Physical Activity in Individuals with Neurological Injury (BASCI, SNF)

Neurological disorders, such as spinal cord injury, can significantly impact an individual’s ability to control and coordinate their muscle movements. These deficits can vary considerably, depending on the location and severity of the lesion in the spinal cord. A multitude of clinical assessments let us monitor a patient’s recovery process. However, these assessments tend to rely on subjective measures emphasising the need for more objectively quantifiable metrics to ensure consistent and accurate evaluations. To address this, we use inertial measurement units, wearable movement sensors, as a tool for providing objective metrics in our evaluations. These sensors have proven to be a reliable tool in objectively assessing human motion and can complement clinical assessments. We aim to incorporate sensor data in clinical applications and prediction models to improve the overall quality of care for individuals with spinal cord injuries. This interdisciplinary approach, integrating technology and machine learning with traditional clinical assessments, holds the promise of not only enhancing the accuracy of recovery predictions, but also personalising rehabilitation plans to better address the unique needs and challenges of each patient.

In collaboration with Balgrist University Hospital and the Indian Spinal Injuries Center. See funding.

Neurosurveillance

The overarching aim of the scientific project is to develop a system that can monitor neurological recovery in clinical practice in real time. Clinicians will be alerted by the neurosurveillance as soon as patients are under- or overperforming clinically. This is a necessary step towards a future-oriented understanding of hindering and promoting influences on neurological recovery. A shiny-app allowing interactive investigation of historic data is available, see external page shinny app for Neurosurveillance.

In collaboration with John Kramer (ICORD, UBC, Vancouver, Canada). See funding.

In Silico Trials - a Digital Health Solution to Assess Recovery from Traumatic Spinal Cord Injury

Given the lack of effective therapies to mitigate the life-long effects of spinal cord injury, there is a need to accelerate the search for effective treatments. Despite several promising strategies being identified in preclinical studies, several clinical trials have previously failed to show patient benefit. Conducting clinical trials for spinal cord injuries is challenging since these injuries are rare and highly heterogeneous. In this project, we investigate different ways to predict how an individual will recover and use these predictions to simulate control groups in clinical trials. We also provide tools to test in a simulation how strong a new treatment would have to be in order to be observable within a given trial cohort - this is called an "in silico trial". We combine our prediction and simulation tools within a in silico trial platform, in a web-based tool to enable users to explore recovery trajectories and trials composition effects.

A preliminary version of the tool to explore historic controls for recovery prediction can be found in the app overview link. See funding.

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Technology-driven Combinatorial Therapy to Rewire the Spinal Cord after Injury - ReWire

ReWIRE aims to facilitate the repair and restoration of neurological functions in individuals with Spinal Cord Injury (SCI) by integrating cutting-edge translational neurotechnologies with innovative rehabilitation interventions. Recent technological advancements have revolutionised SCI treatment, incorporating innovative drug delivery, biomaterial bridges, and neuromodulation therapies. By leveraging these breakthroughs within the framework of multiple Ph.D. projects that will continuously interact, converging towards combinatorial therapies to repair SCI, we aim to address a challenge that, to date, has not seen success using individual therapies.

By emphasising the transdisciplinary and translational aspects often overlooked in conventional educational programs, ReWIRE seeks to enhance research and development (R&D) capabilities and translate combinatorial SCI therapies from bench to bedside promptly, thereby improving the quality of life and reducing societal burden.

See external page project page.

See funding.

No-Go Inhibition in Spinal Cord Injury

The No-Go Inhibition in Spinal Cord Injury (NISCI) trial is a recently completed clinical trial assessing the potential of a Nogo-A inhibiting antibody to improve recovery after a cervical spinal cord injury. It provides a valuable source of data to investigate a multitude of hypotheses, as hospital and rehabilitation stays were documented extensively and new technologies, such as wearable devices tracking activity, have been used.

In collaboration with Balgrist University Hospital. See funding.

• Catherine Jutzeler (PI)
• external page Melina Giaiozis (Ph.D. student)
• Louis P Lukas (Ph.D. student)
• Lucie Bourguignon (Ph.D. student)
• external page Miklovana Tuci (Ph.D. student)
• Samuel Håkanson
• Sarah Brüningk

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