Extraction and Quantification of Azelaic Acid from Different Wheat Samples (Triticum durum Desf.) and Evaluation of Their Antimicrobial and Antioxidant Activities

Abstract

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.

Keywords: analytical methods; azelaic acid; green techniques; plants extracts; wheat.

Figure 1

MRM of standard solution of AzA (500 ng/mL), Time is expressed in minutes.

Figure 2

Specificity of the selected method, MRM of standard solution of AzA (500 ng/mL) against blank.

Figure 3

Scree plot of the data set (variance % explained by each component: 54.4; 20.4; 12.2; 8.0; 2.52).

Figure 4

Score plot of PC1-PC2 (74.8% of total variance explained) of the data matrix. with the object coded according to the sample state (fluid or freeze-dried; respectively),and colored according to the starting material (whole grain or wheat flour, respectively).

Figure 5

PCA loading plot of the first two principal components of the dataset.

Figure 6

Biplot of the first two principal components, built using scores and loadings.