Dried green tea (Camellia sinensis L.) Kuntze was harvested from Mitra Kerinci Farm in West Sumatra, Indonesia. HPMC K100, HPMC K4M, and PEG 400 (all are of pharmaceutical grade) were obtained as gifts from Colorcon Indonesia. Epigallocatechin gallate (EGCG), catechin, and caffeine (all are of analytical grade) were purchased from Sigma-Aldrich, Singapore.

Microbalance (Radwag 2.3Y), sonicator (Transonic 570), magnetic stirrer (Stuart cb162), pH meter (Hanna HI 8314), Franz cell diffusion (Logan VTC 300), HPLC (Shimadzu 2010C HT, Japan; equipped with an ultra-violet detector), and C18 column (150×4.6 mm, 5 µm, Luna, Phenomenex, USA) were used in this study.

The accuracy was presented by the recovery percentage by analyzing three different series of analyte concentrations for six replications. Precision was determined as the standard deviation or relative standard deviation (RSD) values. In the critical method, it is generally accepted that the RSD must be less than the RSD determined by the Association of Official Analytical Chemists (AOAC).

Formula optimization was performed by the simplex lattice design method (SLD) with the factors of HPMC K4M, HPMC K100, and PEG 400. This process was performed by using Design Expert ver. 7 software. Matrix weight, matrix thickness, dissolution efficiency values (%DE₃₀₀) of catechins, caffeine, and EGCG were used as the evaluated responses. The optimal formula was determined based on the criteria: 1) the smallest value of weight and thickness of the matrix; 2) the largest %DE₃₀₀ values of gallic acid, catechin, caffeine, and EGCG. Parameter of %DE₃₀₀ was calculated based on the area under the curve of the released compound from the matrix during 300 minutes. The determination of the released compound was performed by the RP-HPLC method.

A chemometrics model is used to determine the correlation between observational responses. PCA models were implemented to evaluate the changes of the variable on each run. This process was performed by using Minitab software. PCA’s output is a scree plot, score plot, loading plot, and a bi-plot graph describing the correlation and its effects on the principle components (PC).

Determination of the compound’s release kinetics was carried out by the curve fitting analyses of the observation release curve based on the mathematical approaches, i.e., the zero-order, the first-order, Higuchi, and Korsmeyer-Peppas models (Costa and Lobo 2003).

The chromatogram is presented in Figure 1. The system suitability test (SST) result showed good results, as indicated by the analytical parameters provided in Table 1. The coefficient of variation (CV) for all parameter was less than 2%, CV of Resolution (Rs) of < 2%, tailing factor (< 2), and theoretical plates (N > 2000) (Snyder et al. 1997; Indrati et al. 2018; Setyawan et al. 2020).

Figure 1. 

HPLC Chromatogram separation of EGCG, caffeine, and catechin (10 µg/mL) using mobile phase methanol-ortho phosphate-water (20:0.1:79.9 v/v/v) with flow rate 1 mL/min delivered isocratically and UV detected at λ 280 nm.

Linearity, the limit of detection (LoD), and limit of quantitation (LoQ).

The standard curves of dissolution studies were in the range of 0.05; 0.1; 0.2; 0.3; 0.4; 0.5 μg/mL with coefficient of correlation (r) was 0.9999 (catechin), 0.5; 1; 2; 3; 4; 5 μg/mL with (r) value was 0.9995 (caffeine), and 0.1; 0.2; 0.3; 0.4; 0.5; 1; 2; 3 µg/mL with (r) value was 0.9999 (EGCG). The results showed a good linearity in terms of the coefficient of correlation as illustrated in Figure 2. The limit of detection (LoD), and limit of quantitation (LoQ) values produced by catechin, caffeine, and EGCG were 0.007, 0.095, 0.026 µg/mL (LoD), and 0.024, 0.301, 0.086 µg/mL (LoQ), respectively.

Figure 2. 

Standard calibration curve of catechin, caffeine, and EGCG for dissolution study.

The accuracy and precision studies of drug release were performed by the standard addition method. The standard solutions were added into the matrix with three different concentrations with six replicates. A total of 0.1, 0.3, 0.4 µg/mL (catechin), 0.5, 1.0, 2.0 µg/mL (caffeine), 0.3, 0.5, 1.0 µg/mL (EGCG) were added into the matrix with the percentage of total recovery (intra-day) was 99.78% (CV 1.75%), 98.66% (CV 2.14%), 99.6% (CV 2.09%) for catechin, 105.86% (CV 1.44%), 100.13% (CV 3.14%), 103.52% (CV 0.64%) for caffeine, and 101.99% (CV 2.61%), 98.89% (CV 2.94%), 101.81% (CV 1.64%) for EGCG. The results of the percentage of total recovery (inter-day) was 104.37, 97.87, 99.91% (catechin), 103.83, 105.54, 105.20% (caffeine), and 98.20, 102.28, 99.29% (EGCG). The results have fulfilled the requirements of AOAC guidelines. These facts indicated that this method is accurate and precise in determining the levels of the compounds dissolution or release study.

The transdermal patch matrix was prepared using a solvent casting technique. The excipient composition was processed by the simplex lattice design (SLD) method with 13 runs (Table 2). The experimental results showed that matrix weight and thickness were 0.48–1.16 g and 0.10–0.33 mm, respectively. The %DE₃₀₀ of catechin, caffeine, and EGCG were 15.22–47.83%, 21.92–53.47%, and 1.88–8.71%, respectively. The results showed that the %DE₃₀₀ is all less than 90% over a period of 300 minutes. It can be caused by several things, such as the influence of dissolution media. The osmolarity of the dissolution medium contributed to auto-oxidation. Increasing the ionic strength at pH > 5 with the addition of sodium chloride will increase the rate of degradation of catechins and EGCG. EGCG is stable in acidic conditions (pH < 4). At a higher pH, it will trigger the EGCG epimerization process to become gallocatechin gallate (GCG). In addition, EGCG and catechin derivatives were able to interact with HPMC to form complex compounds that produce sediment or aggregates through hydrophobic interactions and hydrogen bonds. This complexation increased the stability of EGCG and catechins, but on the other hand, reduced the release of catechins and EGCG.

ANOVA analysis showed that all treatments generated a significant difference (p < 0.05). A mathematical equation describing the relationship between these components of the experimental response (matrix weight and matrix thickness) has followed a linear model (equations 1 and 2).

Weight = 0.63(A)+0.70(B)+1.17(C) (1)

Thickness = 0.18(A)+0.22(B)+0.32(C) (2)

Those equations demonstrate that all components play important roles in the increase in matrix weight and thickness. The PEG 400 (C) represents the most prominent role in increasing the weight of the matrix. PEG 400 might act as a plasticizer. Therefore its addition induced greater mobility of the polymer chains by replacing polymer-polymer interactions with polymer-plasticizer interactions. PEG 400 has non-volatile properties. The higher level of PEG 400 used in the matrix would make the matrix weight and thickness increase because PEG did not evaporate during the drying process.

ANOVA analysis showed that all treatments produced significant differences in the %DE₃₀₀ values (p < 0.05). The special-cubic equation models appropriately describe the relationship between these components to the experimental response of %DE₃₀₀ values of catechin, caffeine, and EGCG (equations 3, 4, and 5).

%DE300 catechin = 32.04A+47.60B+41.99C-0.91AB+35.77AC-59.19BC-741.92ABC (3)

%DE300 caffeine = 40.29A+49.54B+47.86C+0.23AB+15.12AC-54.51BC-630.05ABC (4)

%DE300 EGCG = 6.18A+8.49B+6.78C-1.53AB+6.82AC-11.11BC-147.90ABC (5)

The HPMC K4M (B) represents the most prominent role in increasing the level of %DE₃₀₀ of catechin, caffeine, and EGCG. The higher level of HPMC K4M used in the matrix would decrease the matrix viscosity and the diffusion layer to be quickly diffused out.

The optimum formula selection was determined by several criteria, namely the minimum weight and thickness of the matrix, the maximum of %DE₃₀₀ of catechin, caffeine, and EGCG. The numerical approach showed that the optimal formula was at a ratio of HPMC K100 (A), HPMC K4M (B), PEG 400 (C) at 4.0: 4.5: 0.5. The contour plot diagram of the optimum formula is presented in Figure 3. The optimal formula estimated matrix weight, matrix thickness, %DE₃₀₀ of catechin, %DE₃₀₀ of caffeine, and %DE₃₀₀ of EGCG at 0.67 g, 0.22 mm, 48.44, 49.68, 8.99%, respectively.

Figure 3. 

Contour plot diagram for optimum formula of transdermal matrix patch.

Principal component analysis (PCA) was carried out to determine the correlation of each response. PCA is an important part of chemometrics and provides the most compact representation of all variations in the data table. PCA is designed to reduce complexity with a big dataset into a series of optimized and interpretable sizes. PCA finds out factors or principle components (PC₁, PC₂,…..PC_n), which are in linear combinations of the original variables describing each object (X₁, X₂,……X_n). If there are five variables or responses, there will be five principal components (PC). There are five parts of PCA, namely data, score, loading, and residual. The score of PCs is also called as hidden or latent variable. Samples that have the same scores of PC can be understood as the same object. The PCA process generated five factors or PC. Based on the scree plot graph in Figure 4a, only four PC were able to produce the data variation of 99.20%. The score plot graph in Figure 4b shows that the formula is divided into several quadrants with different distances from one another. Some formulas show similar response characters based on the proximity value of the PC value. For example, formula 8, 9, and 10 had proximity values of PC₁ and PC₂. It is illustrated that formula 8, 9, 10 had proximity of response character. The loading plot shows the strength of each variable affecting the PC. The angle between vectors describes how these variables correlate with one another. If two vectors form a narrow-angle, it indicates a positive correlation between the two variables, and if the vectors form an angle ≥ 90^o, then they are not correlated or negatively correlated. The loading plot graph in Figure 4c showed that %DE of catechin, caffeine, and epigallocatechin gallate have a positive correlation. A similar correlation was also present between the weight and thickness of the matrix. In contrast, the matrix patch’s physical properties have a negative correlation to the % DE of the compounds. PCA bi-plot graph in Figure 4d is merged a usual score plot with a loading plot (Rohman and Putri 2019).

Figure 4. 

PCA scree plot illustrated the number of principal component to keep in PCA (a), PCA score plot illustrated the correlation between samples (formulas) (b), PCA loading plot illustrated the correlation between responses (c), PCA bi-plot (d).

This verification was performed to ensure that the results predicted by the model did not differ significantly from the results of the observations. The number of experiments was replicated three times, and the data results are presented in Table 3. Verification results showed that there was no significant difference between the model-based prediction and the observation of each response (p > 0.05).

The release profiles in Figure 5a were concluded that the kinetics of catechin, caffeine, and EGCG followed the Korsmeyer-Peppas (equation 6).

Figure 5. 

Dissolution profiles of caffeine, catechin, and EGCG from matrix patch transdermal for 300 minutes (a), Korsmeyer-Peppas fitting curve results (red lines) of catechin, caffeine, and EGCG drug release of the optimum formula (blue pattern) was performed by Solver (b).

M/Mt^~ = Ktⁿ (6)

Where M/Mt^~ is a fraction of drug released at time t, k is the release rate constant, and n is the release exponent. The assessment is based on the value of the correlation coefficient (r) between the profile of the observed curve against the predicted curve. The value of r ≤ 1 indicates a good correlation in both analyses. Based on the data results in Table 5, the release rate (k) of catechin, caffeine, and EGCG were 0.10–0.33 mg/hour, 0.23–0.51 mg/hour, and 0.01–0.08 mg/hour, respectively. The catechin, caffeine, and EGCG diffusion exponent value (n) were 0.29–1.11, 0.17–0.37, and 0.39–0.74, respectively. Based on the diffusion exponent value (n), the mechanism of catechin release (Fig. 5b) followed the Fickian diffusion (run 1, 2, 9, and 11), where the diffusion rate is less than relaxation. Some formulas follow non-Fickian diffusion (run 3, 4, 5, 6, 7, 8, 10, and 12) that the diffusion and the relaxation rate is balanced. Only run 13 followed the relaxation mechanism.

Caffeine release (Fig. 5b) followed a Fickian diffusion, in which the rate of diffusion is lower than the relaxation. Five formulas followed the Fickian diffusion (run 4, 8, 11, 12, and 13), while the remaining eight formulas followed non-Fickian diffusion. EGCG release (Fig. 5b) followed Fickian diffusion, where the rate of the diffusion is slower than the relaxation. Four formulas followed the Fickian diffusion (run 4, 8, 11, and 12), and the remaining nine formulas followed non-Fickian diffusion. The difference in this mechanism is related to the polymer composition. It has different physical and chemical properties, resulting in the polymer’s water penetration leading to erosion. The dissolution medium’s penetration caused the diffusion mechanism into the pores of the matrix produced by HPMC (hydrophilic polymer) and dissolving the compound. An increase in the volume of the medium caused the matrix expansion, followed by drug diffusion.