Predicting relapse in UC patients

- A novel biomarker with the potential to improve the lives of millions of UC patients world-wide

About the technology

Ulcerative Colitis is a chronic autoimmune disorder of the colon characterized by alternating periods of remission and relapse. Disease flares occur in a random way and are currently unpredictable.

Our novel prognostic biomarker can identify patients with a high risk of relapsing, enabling clinicians to escalate treatment before the onset of symptoms and keep patients in remission (figure below). The test is based on mucosal samples from the colon, which might sounds challenging at first. However, endoscopy represent that mainstay evaluating the disease activity, meaning the test can be incorporated without need for additional endoscopies.

Market and competition

2.7 million people are carrying a UC diagnosis in Europe and US. According to the European Crohn’s and Colitis Foundation (ECCO), about 48% of the prevalence is in remission during a typical year, representing a substantial market potential for our test

Currently there is no biomarkers on the market which can predict relapse in UC patients hence our biomarker is targeting an unmet clinical need.

Value proposition

UC patients

  • Fewer relapses
  • More well managed disease and quality of life

Healthcare system/Society

  • Hospital bedtime reductions, examinations, tests, etc
  • Reduce need of proctocolectomies
  •  
Figure: Long term goal by implementing the test ~50% of the UC patient population are experiencing relapse at any given time (left). Our ultimate goal is to create a prevalence shift by increasing the population in remission offering great benefits for patients and the healthcare system (right)

More about the test

  • Molecular – measuring expression ratio between two cytokines (qRT-PCR)
  • Based on mucosal samples from the colon sampled during routine endoscopies
  • Patent pending (2021)
  • Invention originates from the University hospital of North Norway

Resources and partners

  • The Norwegian Nuclear Medicine Consortium 180°N
  • Tromsø Research Foundation ​
  • Centre for Research-based Innovation (SFI) Visual Intelligence
  • Proof of concept validated for small scale research in mice
Figure: Basic principle of relapse test on a per patient level. Test will enable the clinician to stay ahead of the curve, escalating treatment before the flare up and keep the patient in remission (blue line). Time of treatment not shown for simplicity.

Opportunities for Collaboration

We are currently looking for a licensing partner to develop the project further with test design and clinical IVDR- approval.

Universitetet i Tromsø
image (5)
unn

Contact information

Foto: Marius Fiskum

Ingrid Skjæveland

Forretningsutvikler | Tech Transfer

ingrid@norinnova.no
Tlf. 991 57 143

Les om andre prosjekter:

– A novel biomarker with the potential to improve the lives of millions of UC patients world-wide About the technology Ulcerative Colitis …

DLIF

– Deep Learning derived Input Function in dynamic PET About the technology With DLIF we present a non-invasive, automatically generated input-function for …

Mission – VTE

– MicroRNAs for risk assessment and treatment of venous thromboembolism The challenge Venous thromboembolism (VTE) is a highly prevalent disease, affecting around …

DLIF

- Deep Learning derived Input Function in dynamic PET

About the technology

With DLIF we present a non-invasive, automatically generated input-function for kinetic modelling in dynamic PET. Our deep learning models are trained end-to-end with dynamic PET images as input and ready-to-use input function as output. The model show very strong correlation and non-significant difference in influx (Ki) for myocardium and tumor derived with AIF and DLIF, respectively.

Value proposition

  • No surgery requirements
  • Compatible with any PET scanner and the most widely used tracers
  • Provides results that are highly accurate with less bias and variation than state of the art

Areas of application

  • Non-invasive input function for dynamic PET where blood sampling is not possible
  • Preclinical or clinical applications
  • Could be integrated into external software independent of PET scanner vendor
  • As a part of the PET scanner-software to deliver the DLIF together with the PET images

Resources and partners

  • The Norwegian Nuclear Medicine Consortium 180°N
  • Tromsø Research Foundation ​
  • Centre for Research-based Innovation (SFI) Visual Intelligence
  • Proof of concept validated for small scale research in mice

Comparison to state of the art technology

Image: Comparison model of DLIF and existing state of the art technology.

Opportunities for Collaboration

We are currently looking for industry partners who finds interest in our technology. We are open for working together on the development of DLIF to accommodate your needs.

Collaboration partners

Universitetet i Tromsø
Logo_180N
TFS_FARGE_NOR-HOR
unn

Contact information

Foto: Marius Fiskum

Ingrid Skjæveland

Forretningsutvikler | Tech Transfer

ingrid@norinnova.no
Tlf. 991 57 143

Les om andre prosjekter:

– A novel biomarker with the potential to improve the lives of millions of UC patients world-wide About the technology Ulcerative Colitis …

DLIF

– Deep Learning derived Input Function in dynamic PET About the technology With DLIF we present a non-invasive, automatically generated input-function for …

Mission – VTE

– MicroRNAs for risk assessment and treatment of venous thromboembolism The challenge Venous thromboembolism (VTE) is a highly prevalent disease, affecting around …