Summary
Osteoarthritis (OA) is the most common musculoskeletal disease. OA has significant socio-economic impact as it severely affects the quality of life, reduces productivity and carries higher risk of mortality. However, the development of new treatments for OA is painstakingly long and very challenging. There are many clinical OA drug trials that have ended in a failure. In my ERC-StG project, we have developed 3D histopathological evaluation method of OA from micro-computed tomography (μCT). It allows detecting narrow cracks and surface irregularities that are associated with very early OA. Importantly, with our novel method deterioration and degradation of joint surfaces can be analysed much earlier than with any other non-invasive imaging solution. The key is in pinpointing the most important histological changes with the highest reliability while retaining the anatomical information, lost in the conventional histology. This is expected to decrease the number of histological evaluations by 20-fold, saving substantial amount of time, and consequently, money. We will significantly add value to pharmaceutical industry developing new therapies for OA. Eventually, our solution speeds up the time-to-market for new and more effective OA drugs, while saving in R&D costs. Later on, we can offer inexpensive, effective and accurate diagnosis and prognosis tool for OA patients. Gradually, we will gather a big data repository of various compounds and their effects on OA. From this data, and with the help of Artificial Intelligence engine, we can visually show the predicted compound efficacy, or suggest a drug combination never imagined earlier. In this PoC, the commercialization planning is carried out with a focal point on Software as a Service (SaaS) business model. We will also improve the adoptability of our innovation (e.g. visualisation of the OA severity, positioning in the value chain) and plan the big data business strategy.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/875658 |
| Start date: | 01-01-2020 |
| End date: | 31-12-2021 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Osteoarthritis (OA) is the most common musculoskeletal disease. OA has significant socio-economic impact as it severely affects the quality of life, reduces productivity and carries higher risk of mortality. However, the development of new treatments for OA is painstakingly long and very challenging. There are many clinical OA drug trials that have ended in a failure. In my ERC-StG project, we have developed 3D histopathological evaluation method of OA from micro-computed tomography (μCT). It allows detecting narrow cracks and surface irregularities that are associated with very early OA. Importantly, with our novel method deterioration and degradation of joint surfaces can be analysed much earlier than with any other non-invasive imaging solution. The key is in pinpointing the most important histological changes with the highest reliability while retaining the anatomical information, lost in the conventional histology. This is expected to decrease the number of histological evaluations by 20-fold, saving substantial amount of time, and consequently, money. We will significantly add value to pharmaceutical industry developing new therapies for OA. Eventually, our solution speeds up the time-to-market for new and more effective OA drugs, while saving in R&D costs. Later on, we can offer inexpensive, effective and accurate diagnosis and prognosis tool for OA patients. Gradually, we will gather a big data repository of various compounds and their effects on OA. From this data, and with the help of Artificial Intelligence engine, we can visually show the predicted compound efficacy, or suggest a drug combination never imagined earlier. In this PoC, the commercialization planning is carried out with a focal point on Software as a Service (SaaS) business model. We will also improve the adoptability of our innovation (e.g. visualisation of the OA severity, positioning in the value chain) and plan the big data business strategy.Status
CLOSEDCall topic
ERC-2019-POCUpdate Date
27-04-2024
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