Pept-AGE · Elucidating the molecular mechanisms of age-related muscle loss to inform Artificial Intelligence-based discovery of novel peptide therapeutics for sarcopenia.

Elucidating the molecular mechanisms of age-related muscle loss to inform Artificial Intelligence-based discovery of novel peptide therapeutics for sarcopenia.

Ageing is often viewed as an inevitability – it has been with us for as long as life has existed. With age, there is a gradual decline in physical function. One of the most impactful aspects of this age-related physical decline is sarcopenia. Sarcopenia, or age-related muscle loss, is a primary cause of loss of physical independence in the elderly. Through increased rates of serious falls, loss of physical capability and by promoting a wide range of other age-associated diseases, sarcopenia has a huge impact on society, particularly given that it is considered an accepted side-effect of growing old. The global population is growing older, and with that comes a significant rise in age-associated disease, including sarcopenia – which is expected to explode in incidence in the coming decades. The need for effective treatment strategies that can slow, or reverse age-related muscle loss is essential if a future crisis is to be avoided. Despite the obvious problems posed by sarcopenia, there are no effective therapies currently on the market. One possible reason for this is that the scientific community has no reliable model of sarcopenia “in a dish”. Often the knowledge used to design drugs relies heavily on basic research, which usually provides information about diseases and illnesses at a molecular level to identify what particular cellular process is no longer working as intended in a particular disease. These discoveries are often based on research carried out using in vitro models – or cells in a dish. Without an easy way to test hypotheses, screen potential therapies before moving into animal models, or even to help identify the molecular pathways responsible for the disease, sarcopenia research is at a massive disadvantage. To try to address these problems, Pept-AGE had several aims. 1. The primary goal of Pept-AGE was to develop a new model of age-related muscle loss in a dish by pushing skin cells from young and old donors down a different developmental pathway to make young and old muscle cells 2. Add to the current knowledge of the processes that cause sarcopenia using the muscle cells developed in Aim 1 3. Using the model developed in Aim 1 and the newfound mechanisms identified in Aim 2, Nuritas’ AI to identify a peptide with the potential to slow age-related muscle loss down at the cellular level 4. A major goal of all MSCA Individual Fellowships is to expand the skills, training and career prospects of the researcher and so one of the main aims of Pept-AGE in addition to the aims above is development of the fellow as an independent scientist Importantly, Pept-AGE has succeeded in developing a novel in vitro model of age-related muscle loss and used that model to identify a potentially important pathway that may play a role in age related muscle loss. As such, Pept-AGE has provided an important new tool that will hopefully allow speed up progress in the field of sarcopenia research and is currently in use at Nuritas to test muscle health peptides prior to clinical trials.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз