Extraction and Quantification of Azelaic Acid from Different Wheat Samples ( Triticum durum Desf. ) and Evaluation of Their Antimicrobial and Antioxidant Activities.
Chiara SpaggiariGiannamaria AnnunziatoCostanza SpadiniSabrina Lucia MontanaroMattia IannarelliClotilde Silvia CabassiGabriele CostantinoPublished in: Molecules (Basel, Switzerland) (2023)
Azelaic Acid (AzA) is a 9-carbon atom dicarboxylic acid, with numerous pharmacological uses in dermatology. Its effectiveness in papulopustular rosacea and acne vulgaris, among other dermatological disorders such as keratinization and hyper-pigmentation, is thought to be related to its anti-inflammatory and antimicrobial properties. It is a by-product of Pityrosporum fungal mycelia metabolism but also it is found in different cereals such as barley, wheat, and rye. Diverse topical formulations of AzA exist in commerce, and it is mainly produced via chemical synthesis. In this study we describe the extraction of AzA from whole grains and whole-grain flour ( Triticum durum Desf. ) through green methods. Seventeen different extracts were prepared and analyzed for their AzA content by HPLC-MS methods and then screened for their antioxidant activity using spectrophotometric assays (ABTS, DPPH, and Folin-Ciocalteu). Minimum-inhibitory-concentration (MIC) assays against several bacterial and fungal pathogens were performed, to validate their antimicrobial activity. The obtained results indicate that whole grain extracts provide a wider spectrum of activity than the flour matrix; in particular, the Naviglio ® extract showed higher AzA content, while the hydroalcoholic ultrasound-assisted extract provided better antimicrobial and antioxidant activity. The data analysis was performed using principal component analysis (PCA), as an unsupervised-pattern-recognition technique, to extract useful analytical and biological information.
Keyphrases
- anti inflammatory
- data analysis
- oxidative stress
- staphylococcus aureus
- ms ms
- mass spectrometry
- simultaneous determination
- high throughput
- systematic review
- machine learning
- multiple sclerosis
- molecular dynamics
- high performance liquid chromatography
- social media
- tandem mass spectrometry
- genome wide identification
- single cell
- wound healing
- antimicrobial resistance