Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review
Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | artículo de revisión biblioteca |
| Language: | English |
| Published: |
Springer Nature
2021-07-14
|
| Subjects: | Health foods, Nutraceuticals, Cosmeceuticals, Machine learning, QSAR, Docking, Molecular dynamics, |
| Online Access: | http://hdl.handle.net/10261/248227 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003359 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|