Extraction and inhibition of α-glucosidase activity
The extraction of A. paniculata was done by maceration at room temperature. From the result obtained, the yield of extraction with different concentrations of ethanol and water solvents was different slightly, as shown in Table 1. The highest yield when we extracted A. paniculata using 50% ethanol, while the lowest yield with water indicating that different concentrations of solvent extraction will affect the level of metabolite extracted.
The α-glucosidase inhibitory activity was determined in order to evaluate the effect of different solvent extraction to its biological activity. The assay principle is, α-glucosidase will hydrolyze glucose in the substrate p-nitrophenyl-α-D-glucopyranoside to α-D-glucose and p-nitrophenol and inhibitory activity was measured based on p-nitrophenol produced. Table 2 showed the α-glucosidase inhibitory activity in each extract with inhibition activity was high in 50% ethanol extract follow by 70%, 30% ethanol, water and ethanol. The result showed that combination of water and ethanol could extract more polar and semipolar compounds that are predicted to have α-glucosidase inhibition activity.
HPLC fingerprint and andrographolide content
Each extract of A. paniculata was analyzed using HPLC to know their differences in the composition of metabolite extracted using different solvent extraction. Figure 1 showed the fingerprint chromatogram of A. Paniculata extracts. About 23 peaks were detected in all extract with the percentage of the area more than 5%. Peak 15 (andrographolide) is the major peak in A. paniculata because the intensity and peak area are highest in all extracts. The fingerprint chromatograms obtained in all samples have a similar pattern with peak number 2, 7, 8, 10, 11, 13, 15, 21 are appear in all sample extracts. The differences mostly come from the peak height and area because each solvent used for extraction have different polarity and ability for extracting the chemical compounds.
Another differences also showed in typical peaks that appear in each extract, such as peak 12 and 22 only appear in ethanol extract. So, this indicates the peak is typical for the fingerprint pattern of the extract. Also, peak 1 appears in 30%, 50% ethanol, and water extracts. It is also can be seen that the more polar extraction solvents will give more detected peaks. We found in Figure 1 water extract gives more detected peaks compared to other extracts. This result is following the previous study that more addition of water in ethanol, which means more polar solvent extraction, the yield is increased [14].
Andrographolide is one of the primary bioactive compounds present in A. paniculata. We have determined this compound in five extracts used in this study to see which extract has a higher andrographolide level. The andrographolide levels in each sample extract are shown in Table 1. Based on the result obtained, the highest andrographolide levels were found in the 50% ethanol extract, while the lowest level is in the water extract. The andrographolide content was shown in the following order, 50% ethanol > ethanol > 70% ethanol > 30% ethanol > water. These results indicating the amount of andrographolide extracted depends on the polarity of the solvent extraction. As we know from earlier study, andrographolide has a lactone ring and this ring is very vulnerable, reactive and easily rearranged. The opening of the lactone ring in andrographolide is the initial stage to begin the decomposition process. In water, ring opening will happen through hydrolysis, whereas in alcohol will happen through trans-esterification mechanism. The hydrolysis is estimated to be faster than trans-esterification. Therefore, the rate of andrographolide decomposition depends on the type of solvent. According to research conducted by Kumoro et al. [14], the addition of water will lead to the conversion of andrographolide into deoxyandrographolide through the hydrolysis process, so will reduce the andrographolide levels in the sample.
Classification of A. paniculata extract
The HPLC fingerprint chromatogram for A. paniculata extracts used in this work has a similar chromatogram pattern, only differ in the peak height and area correspond to the level of chemical extracted by different solvent extraction. To differentiate based on HPLC fingerprint chromatogram only will not easy, so we need an aid from chemometrics analysis. We used principal component analysis (PCA) for classification or grouping the extract according to its solvent extraction. The peak area of 8 major peaks (Peak 2, 7, 8, 10, 11, 13, 15, 21) were used as a variable.
Before subjected to the PCA, the variable was pretreated using autoscaling method. Pretreatment of the data is an important step before the chemometric analysis to get a good result because the quality of input data greatly affects the quality of the output of the analysis. The common autoscaling method is applied by using standard deviation as a scaling factor and producing good analytical output using PCA chemometric analysis techniques [15].
Samples grouping to its solvent extraction was based on the chemical composition using PCA. This multivariate analysis is work to simplify the observed variables by reducing their dimensions and giving an overview of grouping sample through the principal component (PC) [16]. Figure 2A showed the PCA score plot of the A. paniculata extracts. As we can see in the PCA plot, the extracts were grouped according to the solvent extraction. Samples that show the similarity of the profile of the metabolite will be grouped together and the sample that shows the difference will form another group. The two principal components that are most often used in the analysis are the PC1 and PC2. Cumulative percentage of the two PCs used in this study is 89%. According to Varmuza (2001) [17], if the diversity amount of the main components one (PC1) and two (PC2) greater than 70%, the score plot shows good two-dimensional visualization.
PCA biplot is a combination of score plot and loading plot. Loading plot will give us information about how strong each variable affected on the principal component. Figure 2B showed the PCA biplot of A. paniculata extract with the most contribution variables affecting its grouping. We found, peak 5 and 7 gives strong contribution for grouping 50% and 70% ethanol extracts of A. paniculata.