Selection and conservation of high centelloside containing germplasm of Centella asiatica L Urb

Abstract

Centella asiatica (Linnaeus) Urban is a highly valuable medicinal plant used in both the Indian and Chinese systems of medicine. It is a perennial creeper predominantly found in India, Sri Lanka, China, Madagascar, and Australia. India exports C. asiatica, to the United States (US) and Europe, and it has been reported to be a best-seller among herbal medicinal products. The value of the herb C. asiatica in the international market is increasing by 10% to 20% every year. Leaves of C. asiatica are edible and used for curing wounds, enhancing memory, treating bronchitis, asthma, dysentery, leucorrhea, and toxic fever. It is commonly known as the Brain Food as it revitalizes the brain and the nerve cells. The major components which contribute to its medicinal properties are collectively known as centellosides which include triterpene saponins like asiaticoside, asiatic acid, madecassoside, and madecassic acid. Apart from triterpenes, C. asiatica contains high total phenolic contents comprising flavonoids such as apigenin, quercetin, kaempferol, catechin, rutin, naringin and also volatile oils such as farnesol, caryophyllene, and elemene. newlineThe asiaticoside present in the plant helps in wound healing. Additionally, the plant is known to have antitubercular, anticancer, antileprotic, antiulcer, antidepressant, and antioxidant activities. But despite all its values, the plant is not cultivated on a large scale. The raw material for preparing herbal medicines and cosmetic products is mostly collected from wild areas. Therefore, the quality of the herb cannot be trusted. Moreover, heavy adulteration of Centella with Bacopa monneri and other plants having orbicular or fan-shaped leaves is also rampant. Adulteration with other herbs could reduce the therapeutic activity of the main components. Considering these factors, the current work attempted to conduct chemotyping of C. asiatica to find out an elite genotype, design an artificial neural network model to optimize and predict the drug yield in C. asiatica from various re