| Literature DB >> 35431922 |
Zoufang Huang1, Vivek P Chavda2, Rajashri Bezbaruah3, Vladimir N Uversky4, Sucharitha P5, Aayushi B Patel6, Zhe-Sheng Chen7.
Abstract
Originating in ancient India, Ayurveda is an alternative medicinal approach that provides substantial evidence for a theoretical-level analysis of all aspects of life. Unlike modern medicine, Ayurveda is based upon tridoshas (Vata, pitta, and Kapha) and Prakriti. On the other hand, the research of all the genes involved at the proteomics, metabolomics, and transcriptome levels are referred to as genomics. Geoclimatic regions (deshanupatini), familial characteristics (kulanupatini), and ethnicity (jatiprasakta) have all been shown to affect phenotypic variability. The combination of genomics with Ayurveda known as ayurgenomics provided new insights into tridosha that may pave the way for precision medicine (personalized medicine). Through successful coordination of "omics," Prakriti-based treatments can help change the existing situation in health care. Prakriti refers to an individual's behavioral trait, which is established at the moment of birth and cannot be fully altered during one's existence. Ayurvedic methodologies are based on three Prakriti aspects: aushadhi (medication), vihara (lifestyle), and ahara (diet). A foundation of Prakriti-based medicine, preventative medicine, and improvement of life quality with longevity can be accomplished through these ayurvedic characteristics. In this perspective, we try to understand prakriti's use in personalized medicine, and how to integrate it with programs for drug development and discovery.Entities:
Keywords: ayurgenomics; ayurveda; diet; disease; genomics; lifestyle; pharmacogenomics
Year: 2022 PMID: 35431922 PMCID: PMC9011054 DOI: 10.3389/fphar.2022.866827
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.810
FIGURE 1DNA and cellular activity, as well as their relationship to Ayurveda [Adopted from (Sharma and Keith Wallace, 2020) under CC BY 4.0 Licence].
FIGURE 2Ayurgenomics approach for drug discovery towards personalized medicines.