Posted on 2 March 2020
Using approaches such as calorie restriction and genetic manipulation, researchers have achieved remarkable increases in life expectancy in animal models. Comparatively, efforts to extend lifespan in humans have been at a standstill.
As the experiments needed to develop a deeper understanding of the ageing process would impractical or unethical in humans, scientists’ approach has been to perform these experiments in animal models and attempt to translate findings to humans. However, ageing in a short-lived species is fundamentally different from the human ageing process, with differing regulation, complexity, and associated diseases. Mice, for example, do not develop atherosclerosis or Alzheimer’s, simply because they do not live long enough.
Consequently, generalising effects seen in animals to humans often leads to disappointment. An intervention that extends mouse lifespan by 50% could have little to no effect in humans.
One approach to solving this problem is to change the way we translate findings across species. Rather than attempting to translate results directly from animal models, it may be more effective to create computational models based on animal data, which could then be ‘humanised’ using machine learning.
Translating preclinical models to humans: DOI: 10.1126/science.aay8086