INTRODUCTION:

Alpinia galanga rhizomes are currently in the spotlight for possible cancer treatment. Many reports have revealed that this spice with its active marker 1’-acetoxychavicol acetate (ACA) is a potent and promising candidate against several cancer cell lines as compared to other condiments or numerous medicinal plants.^1-3This plant can inhibit the colorectal adenocarcinoma cell line SW480.⁴ Its isolated compound 1'S-1'-acetoxychavicol acetate (ACA) has been reported to induce apoptosis some cancer cell.^5-8 In term of selectivity index, it has appreciable value in a preliminary report.⁹

Further, its combination with other agents has also been reported additively to induce apoptosis in numerous cancer cell lines.¹⁰ However, previous bioassay-guided fractionation works had shown that its ethanol extract had similar activity to its fractions against T47D and MCFcell lines. Moreover, the inhibitory activity of its isolated compounds consisting of phenylpropanoid derivatives showed a similar activity to their crude extracts. From a previous study, trans-p-coumaryl alcohol (1) and p-coumaryl diacetate (2) showed weak activity against T47D cell, while [1’S]-1’-acetoxy chavicol (3) and trans-p-coumaryl diacetate (4) displayed strong activities with IC₅₀ values of 17.3 and 25.4mg/mL, respectively.¹¹Thus, a synergistic or additive mechanism among the chemical constituents could play a role in its bioactivity. Therefore, from the formulation point of view, using the crude extract will be more economical than the purified material.

In a clinical study, A. galanga rhizome has also been shown to ameliorate Alzheimer’s,¹²antiinflammatory,¹³enhance mental alertness and prolong the attention mood. It also demonstrated its ability to attenuate hyperlipidaemia in the rat.¹⁴Thus, this rhizome is a promising material to be developed as an important pharmacological agent. However, consistent raw material is mandatory for continuous supply for industrial purposes. In contrast, a geographical origin might bea determinant factor,an herbal material found in various qualitative and quantitative chemical features.^15-17Furthermore, these diversities may deter bioactivity consistency. This factor then has made the reproducibility of the chemical constituents to be a main issue in maintaining the supply consistence of starting material. Thus, the reliable chemical standardization of their chemical profiles should be revealed. RP HPLC becomes the method of choice for the analysis of numerous micromolecules. Since its specificity, selectivity, and efficiency, RP HPLC with an ultraviolet detector has been applied to characterize numerous active pharmaceutical ingredients ranging from raw materials,^18,19 processing manufacturing control,^20,21 final products,^22-24 and post-market surveillance.²⁵ Its robustness has propelled further development for characterizations of converted metabolites in body fluids.^26-28 Since it has a wide range of reliability, RP-HPLC also provides good performance in herbal constituent profiling.^29-31

Some instrumental methods have been introduced to analyse Alpinia rhizomes with flavonoids as the targeted markers with HPLC diode.^32,33Previously, Luo et al.³⁴introduced an advanced method by UPHLC-MS-MS to detect diarylheptanoids. To the best of our knowledge, there has been no report on an analytical approach to target appropriate cytotoxic markers, i.e. phenylpropanoids in A. galanga. Moreover, a cheapand efficient method providing a holistic view is still led by the HPLC method. This research aims to develop the RP-HPLC-UV method for analysing four phenylpropanoids in A. galanga rhizome and to introduce a principle component analysis (PCA) mapping of their chemical features. Further, to secure the A. galanga supply in terms of bioactivity, the rhizomes from twelve different regions have been examined for their T47D breast cancer cell inhibitory activity.

MATERIALS AND METHODS:

Chemical and standards:

The standard compounds (1–4) were isolated from our laboratory, and their identities were confirmed by 1D- and 2D-NMR and compared to the published data.^35-36Their purities were 1=99%, 2=99%, 3=90%, and 4=99%, respectively, as determined on the basis of HPLC-UV and TLC analysis. Acetonitrile and methanol were HPLC grades obtained from Merck (Darmstadt, Germany) and purified water was purchased from Ikapharmindo Co. (Jakarta, Indonesia). The pH modifiers dimethylamines, triethylamine, formic acid, and acetic acid were purchased from Merck (Darmstadt, Germany).

Preparation of sample solutions:

The rhizomes were obtained from twelve different plantation centres in Central Java Province and Jogjakarta (Java Island) and other three were taken from Sumatra and Borneo Islands during the period of January-March 2018. The plant samples were authenticated Siti Sartika, MS (taxonomist). All samples were sliced and dried in an oven at 70°C for 48hours. The material was grounded into fine powder. A 20.0g of each sample was macerated in 200mL ethanol for 3 days at a room temperature. The obtained extract was concentrated in a vacuo. The fractionation was conducted by sampling of 1.0g extract. It was dissolved in 10.0mL aquadest then fractionated with 10mL ethylacetate, 3 times. The upper layer was taken and removed its ethylacetate with a rotatory evaporator. For the profiling, a 5.0mg fraction was weighed and dissolved in methanol to have 1.0mL solution. Prior to the injection, the sample solution was filtered through the 0.45mm filter.

Chromatographic conditions:

The following used instruments included: 4001 Efficient Heidolph rotatory evaporator (Heidolph, Germany), water bath (Memmert, USA), Advantec filter 0.45µm (Toyo Roshi Kaisha, Japan), Branson 2510 Sonicator (Connecticut, USA), High-performance liquid chromatography (HPLC) system was Waters Alliance 2965 (Massachusetts, USA) with PDA 2998 detector, fitted with a Cosmosil column (150mm x 34.6mm). The column temperature was 25°C The solvent system was chosen from the combination of water-acetonitrile, water-methanol, and water-acetonitrile-methanol. The pH modifiers were added in the mobile phases in order to optimize the separation.

Preparation of standard solutions:

The standard stock of 1, 2, and 4 was prepared at a concentration of 1.0mg/mL in methanol, while 3 was at 2.0mg/mL. The working standard solutions were prepared from the stock in five different concentration levels. All the solutions were kept in a refrigerator at 4 °C.

MTT cytotoxic assay:

The T47D breast cancer cell was obtained from Prof. Masashi Kawaichi of Nara Institute of Science and Technology (NAIST) Japan. For the MTT assay was conducted according to van Meerloo (2011)³⁷ with a slight modification. The cells were seeded in RPMI medium (Sigma, USA) containing HEPES (4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid, fetal bovine serum (10%), fungizone (1%), and penicillin-streptomycin (2%) in a dish within 48 hours in a CO₂ incubator (37°C, CO₂ 5%) and monitored under a microscope (CX21 Olympus) until 80% confluent. The cells were distributed onto 96 well plates (Iwaki Inc., Tokyo), each 1x10³ per well, accompanied by control wells. The plates were incubated for 48 hours. Their media were descanted before being replaced with fresh media containing the tested samples. The methanol extract was dissolved in DMSO to make a concentration of 1.0mg/mL. 100μL of it was dissolved in 900μL media (with a final concentration of 50μg/mL). 100μL of the solution was added to each well in triple replication. Doxorubicin (PT. Kalbe Farma Indonesia) was employed as a positive control. After the 24-hour incubation, their media were descanted and replaced with 100μL MTT (5mg/mL in media). The plate was placed in the CO₂ incubator for 6hours prior to the addition of 100μL SDS 10% stop solution. The treated plates were wrapped with aluminium foil and placed at room temperature for 24hours. The solution absorbance was measured by means of a Biotech microplate reader with 590nm of Lambda. The cytotoxic tests were conducted in at least three independent experiments.

Data analysis:

Principal component analysis (PCA) was applied to 12 samples on the chemical parameters to investigate and visualize the homogeneity of the samples. HCA and PCA were performed using the R i386 3.4.3 version (The R Foundation for Statistical Computing, Vienna, Austria).³⁸In this study, combined “untargeted” and “targeted” peaks to investigate different A. galanga plants species was approached by using the merging of high-performance liquid chromatography (HPLC)-image analysis and pattern recognition methods used for fingerprinting and classification of 12 different A. galanga samples collected from Indonesia.

RESULTS AND DISCUSSION:

Chromatographic profiling:

The sample plants for this study were obtained from Karangpandan (I-1), Karanganyar (I-2), Selogiri (I-3), Jogja (I-4), Solo (I-5), Wonogiri (I-6), Singkawang (I-7), Banjarmasin (I-8), Kudus (I-9), Lampung (I-10), Boyolali (I-11), and Klaten (I-12), since they have been well known for cultivation centers of A. galanga. Prior to conducting chemical profiling of their chemical constituents, HPLC systems were optimized based on chemical markers 1-4. Among the simulated chromatography systems, the solvent system of methanol-water with a ratio of 1:1 showed the best separation feature to standard compounds (Figure 1) with a flow rate of 1 mL/min. Prior to that finding, neither acetonitrile-water nor acetonitrile-water-methanol could not achieve satisfying separations even with weak acids or bases modification. On the solvent choice, the combination of acetonitrile-water and methanol–acetonitrile-water could not demonstrate any satisfying separating power. Considering methanol is much cheaper than acetonitrile, this solvent system was deemed more economical in terms of solvent price and convenience without any modifiers. Moreover, the isocratic system involved was convenient and economical to be applied.³⁹

Figure 1: Chromatogram of standards 1-4. All markers were nicely separated with a methanol-water (1:1) solvent system in 15 minutes. The flow rate was 1 mL/minute.

To the best of our knowledge, this is the first report regarding the combination of methanol and water solvent system for phenylpropanoid in A. galanga with an isocratic model. The number of peaks could be observed in the highest number of 250nm. Therefore, this wavelength was used for the detection. Under a chromatographic condition, all components were separated well within 15 minutes, which is considered notably efficient. Hence, these four markers in this HPLC system may be proposed for galangal rhizomes profiling for industrial purposes.

All calibration curves of four phenylpropanoids (1–4) provided a linear correlation between the concentration and the peak area. On the basis of calibration data (Table 1), the systems showed good linearity with the regression value of r²>0.99. Meanwhile, the limit of detection and limits of quantification for the working standards were found between 7-15µg/mL and 2,3-5,0 µg/mL, respectively. The accuracy of the method was found on the basis of the recovery test by spiking the standards in the A. galanga sample at the concentration of 80%, 100%, and 120%, respectively and compared against a sample without standard addition. Meanwhile, the intra- and inter-day variations of the assay were found to be lower than 4.0% with a maximum RSD of 5.0, indicating that the method had good reproducibility. The system was also capable of featuring peaks 1–4 in all samples. Thus, on the basis of all sample chromatograms, the method will be a very good prospect to be applied for sample authentication and discrimination as well as for quantitative analysis purposes of A. galanga.

The chromatogram system was applied to quantify the phenylpropanoids therein from twelve different geographical origins. trans-p-Coumaryl alcohol (1) was detectable in the range of 0.04 to 0.52%, in which the Karanganyar area (H-2) was found to have the highest concentration (0.52%) (Table 2). This area also showed the highest quantity for p-coumaryl diacetate (2), with notably very high, i.e., 25.45%. Meanwhile, the other areas showed concentrations ranging from 0.04 to 0.38% for 1 and 0.8 to 9.40% for 2. For [1’S]-1’-acetoxy chavicol (3)and trans-p-coumaryl diacetate (4),all samples were found to have concentrations between 0.20 and 1.23%; 0.04 and 0.63%, respectively.

The chromatography system demonstrated a good performance in separating all targeted markers in all samples. It should be noted here that the appearance of standards 1–4 in all samples represented reproducibility for the analytical markers for A. galanga. However, samples I-2 and I-4 gave another notable peak at the retention time (Rt) of 4.25 min without any enhancement of the inhibitory activities (Figure 3).

Figure 2: Variation of trans-p-coumaryl alcohol (1), p-coumaryl diacetate (2), [1’S]-1’-acetoxy chavicol (3), trans-p-coumaryl diacetate (4) colonies collected from a single population located in Central Java, Lampung, South Borneo, and West Borneo, Indonesia.

Figure 3: HPLC‐UV chromatograms of plant samples (I‐1, I‐2, I‐4, I‐6 and I‐12) using a PDA detector at 250 nm.

Two most active sample chromatograms, I-10 (Boyolali) and I-11 (Lampung),are shown in Figure 4. Considering that the chosen solvent, methanol is much cheaper than the other golden standard solvents, i.e. acetonitrile, as well as the isocratic system was capable of separating all chemical markers therein, the chromatographic system has shown a promising system for the routine analytical works of A. galanga. Thus, on the basis of all sample chromatograms, the method has a very good prospect to be applied for sample authentication and discrimination as well as for quantitative analysis purposes of A. galanga.

Figure 4: HPLC‐UV chromatograms of plant samples (I‐10 andI‐11) using a PDA detector at 250 nm. Both samples showed the most potent activities with IC₅₀ values of 27.22 ± 6.65 and 50.21 ± 1.64, respectively.

Cytotoxic test:

To gain insight into the capability of inhibiting a breast cancer cell, all samples were examined regarding their ability to inhibit T47D cell lines. Of the examined samples, samples from Lampung (H-10) and Boyolali (H-11) demonstrated the strongest activities with IC₅₀values of 28.41 and 27.22 mg/mL, respectively. Meanwhile, the samples H-1, H-2, H3, H4, H-5, and H-6had the IC₅₀ values of 41.47, 64.21, 54.73, 43.97, 51.74, 32.81, 54.37, 40.04, 41.34, and 50.21 mg/mL, respectively (Table 3).

Table 3: Samples taken from some locations and their IC50 values. The experiments were conducted in three independent experiments

S. No	Region	Code	IC₅₀ (µg/mL)
1	Karangpandan	I-1	41.47 ± 8.32
2	Karanganyar	I-2	64.21 ± 6.56
3	Selogiri	I-3	54.73 ± 5.69
4	Jogja	I-4	62.29 ± 8.22
5	Solo	I-5	51.74 ± 4.48
6	Wonogiri	I-6	65.76 ± 1.79
7	Singkawang	I-7	59.37 ± 5.78
8	Banjarmasin	I-8	40.04 ± 5.50
9	Kudus	I-9	52.34 ± 5.78
10	Lampung	I-10	33.25 ± 4.48
11	Boyolali	I-11	27.22 ± 6.65
12	Klaten	I-12	50.21± 1.64
13	Doxorubicin	Doxorubicin	6.25 ± 1.48

On the other hand, the positive control doxorubicin had an IC₅₀ value of 6.25mg/mL.Those significant values from relatively spreaded locations are reconfirmed that A. galangal provides valuable data on the potential medicinal merit within the Zingiberaceae family. As results, all treated cells by I-1 to I-12 showed necrotic cell death appearance, such as nuclear thickening, cytoplasmic blebbing as represented by samples I-6 and I-11, respectively (Figure 5). On the other hand, those phenomena were unobservable on the cell control. Apparently, there were no significant bioactivity differences in the majority of the tested samples.

Figure 5: T47D cells after 24 hours of incubation with sample I-6 (a), I-11 (b), and control positive doxorubicin (c) (magnification 40x), each concentration was 50 µg/mL. The vast majority of those cells showing necrotic conditions, such as nuclear thickening (arrow) or cytoplasmic blubbing with large un grown zones which are unobservable in that of cell control (d).

Overall, the samples H-1 to H-5 in which p-coumaryl diacetate (2) is contained more than 3 % showed inhibitory activity with IC₅₀ values between 41.47 and 64.21mg/mL. Moreover, H-2 containing 2 in the highest concentration of 25% showed one third less potent compared to the samples H-10 and H-11. It suggested that there was no significant contribution of trans-p-coumaryl alcohol (1) and p-coumaryl diacetate (2)to inhibitory activity. In the case of possible synergistic mode among 1–4 occurred, the samples H-1 to H-9 and H-12 containing 2 in a high concentration also did not demonstrate such features.

Figure 6: The principal component analysis (PCA) plot as applied to 12 samples on the chemical parameters. The contributions of the ﬁrst (PC1) and second (PC2) principal component obtained were together 60.1%, representing variation in the data. Sampel I-1, I-2, and I-3 are clearly separated from the other geographical origins.

On the basis of the PC analysis, the samples I-1, I-2, and I-3 showed less correlation among others (Figure 6), while the other samples are in one group. It could be the presence of two peaks within 4 minutes contributed to this separation. Most of the samples showed homogeneity, though with different islands Borneo and Sumatera. Overall, all samples maintained their ability to inhibit the cancer cell model with a relatively significant. The contributions of the ﬁrst and second principal components obtained, using the peak area data of all detected compounds, were 60.1%, representing variation in the data.

DISCUSSION:

During the preliminary experiment for establishing mobile phase, the combination of acetonitrile-water and methanol–acetonitrile-water could not demonstrate any satisfying separating power. As a result, the combination between methanol-water provided excellent separation features toward the chemical markers. Considering methanol is much cheaper than acetonitrile, this solvent system was deemed more economical in terms of solvent price and convenience with neither acid or basic modifiers. Moreover, the isocratic system involved is convenient and economical to be applied.³⁸Under the chromatographic condition, all components were separated well within 15 minutes, which is considered notably efficient. To the best of our knowledge, this is the first report regarding the combination of methanol and water solvent system for phenylpropanoid in A. galanga with an isocratic model. On the use of a standard curve confirmed the linearity of all markers, with different ranges, as some of the markers demonstrated sensitivity. As shown in Table 1, the linearity curves fall within the limits according to the guidance of FDA (2020),³⁹ hence a correlation coefficient more than 0.990 is necessary for a multi residue. Intraday as well as interday precision was conducted in triplicate, and established the accuracy of each marker molecule's respective concentrations. Based on the the ICH guidelines (2005)⁴¹all signal-to-noise ratio obtained are appropriate to determine the LOD and LOQ, respectively. The standard deviation of Y intercepts of the regression line method determined the LOD and LOQ; the LOD for the individual markers ranged from 0.0023µg/ml to 0.0049µg/ml and the LOQ varied from 0.0007µg/ml to 0.0016µg/ml. Hence, these four markers together with this HPLC system may be proposed for fast profiling for industrial purposes for sample authentication and discrimination as well as for quantitative analysis purposes of A. galanga. To our knowledge, this is the first report of the HPLC method with four phenylpropanoids as the corresponding active markers in A. galanga extract rather than relying on non-active marker flavonoids. By employing four markers, this method will be robust against adulteration and faking samples. Moreover, another method using the HPTLC technique with galangin as a single marker²⁴ will apparently have lower specificity and less resilience viewpoint.

On the geographical sample origin survey result, overall, the samples H-1 to H-5 in which p-coumaryl diacetate (2) is contained more than 3% showed inhibitory activity with IC₅₀ values between 41.47 and 64.21 mg/mL. Moreover, H-2 containing 2 in the highest concentration of 25% showed one third less potent compared to the samples H-10 and H-11. It suggested that there was no significant contribution of trans-p-coumaryl alcohol (1) and p-coumaryl diacetate (2)to inhibitory activity. In the case of possible synergistic mode among 1–4 occurred, the samples H-1 to H-9 and H-12 containing 2 in a high concentration also do not demonstrate such features. Hence, there was no such mechanism to be observable. Apparently, trans-p-coumaryl alcohol (1) did not have any contribution to the inhibitory effect at any quantity. 1-2 are present in the lower concentrations compared to the other samples with 0.04 and 0.8% for H-10 and 0.06 and 3.29% for H-11, respectively. Nevertheless, H-10 and H-11 have the concentration for [1’S]-1’-acetoxy chavicol (3)of 0.26 and 0.29% and trans-p-coumaryl diacetate (4)of 0.10 and 0.11%, showing the relatively lower concentration than other samples and the couple sample showed the most potent activity. Therefore, [1’S]-1’-acetoxy chavicol (3) became the main contributor to the bioactivity, as indicated in a previous report.¹¹ Furthermore, a certain ratio among the markers could have an additive effect rather than merely a quantity of 3 or 4. Although the former samples were from Central Java, they showed outsiders as they are mountainous places. Meanwhile, the other geographical areas showed homogeneity, though with different islands Borneo and Sumatera. It has been acknowledged that the soil compositions, climate, as well as water type really have an impact on the qualitative and quantitative metabolites of the Zingiberacease family by mean geographical impact⁴³ and recognized on the other families.^44-45 Hence, qualitatively, the highland produces a distinctive compound but does not contribute significantly to the inhibitory activity. Overall, all samples maintained their ability to inhibit the cancer cell model with a relatively significant effect by IC₅₀ lower than 65µg/mL. The contributions of the ﬁrst and second principal components obtained, using the peak area data of all detected compounds, were 60.1%, representing variation in the data. However, since the samples from Lampung and Boyolali exhibited the strongest activity so the two areas may be potential for future study agronomical studies for A. galanga cultivation. Intriguingly, during two decade, the vast majority reports has been acknowledged Curcuma longa rhizome in the spotlight as the most promising species for tumour treatment but no firm formulation from this material has been indicated in the clinical prescription until now. Moreover, from thereassessment reports from numerous in vitro to clinical data, either C. longa or its main constituents curcuminoids have no longer evident as a potent for future anticancer in particular for curative purpose. Those significant values reconfirmed that this A. galangaprovides insight on the potential medicinal merit than explored drugs turmeric and ginger within the Zingiberaceae family.⁴⁶

On the basis of the PC analysis, the samples I-1, I-2, and I-3 showed less correlation among others (Figure 6), while the other samples are in one group. It could be the presence of two peaks within 4 minutes contributed to this separation. Although the former samples were from Central Java, they showed outsiders as they are mountainous places. Meanwhile, the other geographical areas showed homogeneity, though with different islands Borneo and Sumatera. Hence, quantitatively, the highland produces a distinctive compound 2 does not contribute significantly to the inhibitory activity as can be observed for sample I-2. Overall, all samples maintained their ability to inhibit the cancer cell model with a relatively significant effect by IC₅₀ lower than 65 µg/mL. The contributions of the ﬁrst and second principal components obtained, using the peak area data of all detected compounds, were 60.1%, representing variation in the data. However, since the samples from Lampung and Boyolali exhibited the strongest activity so the two areas may be potential for future study agronomical studies for A. galanga cultivation.

CONCLUSION:

This HPLC method has been demonstrated to be capable of providing good resolution and a cheap solvent system. Most importantly, it is able to feature the main active markers [1’S]-1’-acetoxy chavicol (3) and trans-p-coumaryl diacetate (4). The result from this investigation showed no significant variation in inhibitory activities as well as the chemical feature among the samples. The novel HPLC technique possesses good resilience to be applied in the chemotaxonomy study and industrial purposes of A. galanga rhizomes. Especially those producents in developing countries. The use of chemometrics tools for the evaluation of fingerprints can reduce the expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different A. galanga extracts. Thus, the combination of quantitative data with PCA allows the different samples to be discriminated against each other.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGEMENT:

The authors express their gratitude to Universitas Muhammadiyah Surakarta for providing financial support for the study project through the Hibah Integrasi Tri Dharma grant. Reference Number: 236.2/FF/A.3-III/VI/2023.

REFERENCES:

1. Pradubyat N. Giannoudis A. Elmetwali T. Mahalapbutr P. Palmieri C. Mitrpant C. Ketchart W. 1′-Acetoxychavicol Acetate from Alpinia galanga represses proliferation and invasion, and induces apoptosis via HER2-signaling in endocrine-resistant breast cancer cells. Planta Medica. 2022; 88(2): 163-178.https://doi.org/10.1055/a-1307-3997

2. Ramanunny AK. Wadhwa S. Gulati M. Vishwas S. Khursheed R. Paudel KR. Gupta S. Porwal O. Alshahrani SM. Jha NK. Chellappan DK. Prasher P. Gupta G. Adams J. Dua K. Tewari D. Singh SK. Journey of Alpinia galanga from kitchen spice to nutraceutical to folk medicine to nanomedicine. J Ethnopharmacol. 2022; 12(291): 115144. https://doi.org/10.1016/j.jep.2022.115144

3. Subramaniam B. ArshadNM. Malagobadan S.MisranM. Nyamathulla S. Mun KS. NagoorNH. Development and evaluation of 1′-acetoxychavicol acetate (ACA)-Loaded nanostructured lipid carriers for prostate cancer therapy. Pharmaceutics. 2021. 13(4): 439. https://doi.org/10.3390/pharmaceutics13040439

4. Baradwaj RG. RaoMV.Kumar TS. Novel purification of 1’S-1’-Acetoxychavicol acetate from Alpinia galanga and its cytotoxic plus antiproliferative activity in colorectal adenocarcinoma cell line SW480. Biomedicine and Pharmacotherapy. 2017. 91: 485-493. https://doi.org/10.1016/j.biopha.2017.04.114

5. Itokawa H. Morita H. SumitomoT. TotsukaN.Takeya K. Antitumour principles from Alpinia galanga. Planta Medica1987; 53(01): 32-33.https://doi.org/10.1055/s-2006-962611

6. ItoK. Nakazato T. Murakami A. Yamato K.Miyakawa Y.Yamada T. KizakiM. Induction of apoptosis in human myeloid leukemic cells by 1′-acetoxychavicol acetate through a mitochondrial-and Fas-mediated dual mechanism. Clinical Cancer Research. 2004; 10(6): 2120-2130.https://doi.org/10.1158/1078-0432.CCR-1142-03

7. SokSP. Arshad NM. Azmi MN. Awang K. Ozpolat B. Hasima Nagoor N. The apoptotic effect of 1’S-1’-Acetoxychavicol Acetate (ACA) enhanced by inhibition of non-canonical autophagy in human non-small cell lung cancer cells. PloS one. 2017; 12(2): e0171329.https://doi.org/10.1371/journal.pone.0171329

8. Suhendi A. Wikantyasning ER. Setyadi G. Wahyuni AS. Da'i M. Acetoxy Chavicol Acetate (ACA) concentration and cytotoxic activity of Alpinia galanga extract on HeLa, MCF7 and T47D cancer cell lines. Indonesian Journal of Cancer Chemoprevention2017; 8(2): 81-84. http://dx.doi.org/10.14499/indonesianjcanchemoprev8iss2pp81-84

9. Da'i M. Meilinasary KA. Suhendi A. Haryanti S. Selectivity index of Alpinia galanga extract and 1’-acetoxychavicol acetate on cancer cell lines. Indonesian Journal of Cancer Chemoprevention. 2019; 10(2): 95-100.http://dx.doi.org/10.14499/indonesianjcanchemoprev10iss2pp95-100

10. Phuah NH. Azmi MN.Awang K.Nagoor NH. Suppression of microRNA-629 enhances sensitivity of cervical cancer cells to 1′ S-1′-acetoxychavicol acetate via regulating RSU1. OncoTargets and Therapy. 2017; 10:1695-1705. https://doi.org/10.2147%2FOTT.S117492

11. DaiM. Fadhilah A. RahmawatiJ. Forentin A. Usia T. Maryati M. SaifudinA. T47D cell-inhibiting Indonesian medicinal plants and active constituents of Alpinia galanga rhizome. Pharmacognosy Magazine. 2018; 14(56): 359-363. https://dx.doi.org/10.4103/pm.pm_259_17

12. Singh JH. Alagarsamy V. Diwan PV. Kumar S S. Nisha JC.Reddy YN. Neuroprotective effect of Alpinia galanga (L.) fractions on Aβ (25–35) induced amnesia in mice. Journal of Ethnopharmacology. 2011; 138(1): 85-91. https://doi.org/10.1016/j.jep.2011.08.048

13. Das RJ. Duragkar NJ. Umekar MJ. Katolkar P. Evaluation of Anti-inflammatory Activity of Different Extracts of Alpinia galanga. Research Journal of Pharmacy and Technology. 2017; 10(10): 3301-3304.http://dx.doi.org/10.5958/0974-360X.2017.00585.6

14. IyerD. Sharma BK.Patil UK. Isolation of bioactive phytoconstituent from Alpinia galanga L. with anti-hyperlipidemic activity. Journal of Dietary Supplements. (2013); 10(4): 309-317. https://doi.org/10.3109/19390211.2013.830674

15. Godelmann R. Fang F.Humpfer E. Schütz B. Bansbach M. Schäfer H.Spraul M. Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage. Journal of Agricultural and Food Chemistry. 2013; 61(23): 5610-5619. https://doi.org/10.1021/jf400800d

16. Lee J. Jung Y. Shin JH. Kim HK. MoonBC. Ryu DH.HwangGS. Secondary metabolite profiling of Curcuma species grown at different locations using GC/TOF and UPLC/Q-TOF MS. Molecules. 2014; 19(7): 9535-9551. https://doi.org/10.3390/molecules19079535

17. Karabagias IK. Vlasiou M. Kontakos S. Drouza C. Kontominas MG. Keramidas AD. Geographical discrimination of pine and fir honeys using multivariate analyses of major and minor honey components identified by 1H NMR and HPLC along with physicochemical data. European Food Research and Technology. 2018; 244: 1249-1259. https://doi.org/10.1007/s00217-018-3040-5

18. Sri KV.Ravinderreddy S. Suresh K. Rapid RP-HPLC method development and validation for analysis of raltegravir in bulk and pharmaceutical dosage form. Asian Journal of Research in Chemistry. 2015; 8(5): 335-339. http://dx.doi.org/10.5958/0974-4150.2015.00055.3

19. Sri KV. Anusha M. Reddy SR. A rapid RP-HPLC method development and validation for the analysis of linagliptinin bulk and pharmaceutical dosage form. Asian Journal of Pharmaceutical Analysis. 2015; 5(1): 16-20. https://doi.org/10.5958/2231-5675.2015.00003.4

20. Elrefay H. Ismaiel OA. Hassan WS. Shalaby A. Determination of glibenclamide and metformin hydrochloride in active pharmaceutical ingredients and combined dosage form using a stability-indicating HPLC-UV method. Asian Journal of Research in Chemistry. 2013; 6(8): 716-721.

21. Khan H. Ali M. Ahuja A. Ali J. Validated HPLC-UV Method for simultaneous determination of some anti-inflammatory and analgesic drugs. Asian Journal of Pharmaceutical Analysis. 2016; 6(3):183-187.

22. Pathi PJ. Raju, N. A. The Estimation of Cefquinome Sulphate in Suspension Form by RP-HPLC. Asian Journal of Pharmaceutical Analysis. 2012; 2(2): 33-35.

23. Kumaraswamy G. Ravindra N. Jyothsna B. Development and validation of a reversed-phase HPLC method for simultaneous determination of Aspirin, Atenolol and Amlodipine in capsules dosage forms. Asian Journal of Pharmaceutical Analysis 2014; 4(3), 116-120: 10.5958/2231–5675

24. Gupta SK. Sachan N. Chandra P. Sharma AK. A facile RP-HPLC strategy for sensitive detection and stability study of dacarbazine in API and in pharmaceutical sosage dorm. Research Journal of Pharmacy and Technology. 2023; 16(4): 1674-1678.https://doi.org/10.52711/0974-360X.2023.00274

25. Bytyqi M. Shabani D. Bozalija A. In vitro pharmaceutical quality evaluation of different ibuprofen tablet brands available on the Republic of Kosovo market. Research Journal of Pharmacy and Technology. 2022; 15(8): 3725-3730.http://dx.doi.org/10.52711/0974-360X.2022.00624

26. Ningrum VD. Wibowo A. Fuaida I. Ikawati Z. Sadewa AH. Ikhsan MR. Validation of an HPLC-UV method for the determination of metformin hydrochloride in spiked-human plasma for the application of therapeutic drug monitoring. Research Journal of Pharmacy and Technology. 2018; 11(6): 2197-2202. http://dx.doi.org/10.5958/0974-360X.2018.00406.7

27. Akasha R. Allaf AW. Al-Mardini MA. Characterization of the global metabolic profile of Canagliflozin in Rat plasma, urine and feces based on HPLCUV-MS Analysis. Research Journal of Pharmacy and Technology. 2020; 13(1): 399-403.http://dx.doi.org/10.5958/0974-360X.2020.00078.5

28. Singh A. Upmanyu N. Development and validation of analytical HPLC-UV method for simultaneous estimation of losartan and its active Metabolite EXP-31742021. Asian Journal of Research in Chemistry. 2021;14(4): 275-281http://dx.doi.org/10.52711/0974-4150.2021.00047

29. Govindarajan R. Tejas V. Pushpangadan P. High-performance liquid chromatography (HPLC) as a tool for standardization of complex herbal drugs. Journal of AOAC International. 2019; 102(4): 986-992.https://doi.org/10.5740/jaoacint.18-0378

30. Seo CS. Shin HK. Quality assessment of traditional herbal formula, Hyeonggaeyeongyo-tang through simultaneous determination of twenty marker components by HPLC–PDA and LC–MS/MS. Saudi Pharmaceutical Journal. 2020; 28(4): 427-https://doi.org/10.1016/j.jsps.2020.02.003

31. Abid A. Dekmouche M. Bechki L. Bireche K. Belkhalfa H. Messaoudi A. Belfar ML. Bioactive composition analysis using HPLC-UV profile and evaluation of antioxidant activities of different extracts from aerial parts of Atractylisaristata batt. Research Journal of Pharmacy and Technology. 2022; 15(8): 3370-3376. https://doi.org/10.1016/j.jsps.2020.02.003

32. Liu Z. Sang S. Hartman TG. Ho CT.Rosen RT. Determination of diarylheptanoids from Alpinia officinarum (lesser galangal) by HPLC with photodiode array and electrochemical detection. Phytochemical Analysis: An International Journal of Plant Chemical and Biochemical Techniques2005; 16(4): 252-256.https://doi.org/10.1002/pca.827

33. Fang L, Zhang H, Zhou J, Geng Y, Wang X. Rapid screening and preparative isolation of antioxidants from Alpinia officinarumhance using HSCCC coupled with DPPH-HPLC assay and evaluation of Their antioxidant activities. Journal of Analytical Methods Chemistry. 2018; 31 (2018): 3158293. doi: 10.1155/2018

34. Luo J, Rui W, Jiang M, Tian Q, Ji X, Feng Y. Separation and identification of diarylheptanoids in supercritical fluid extract of Alpinia officinarum by UPLC-MS-MS. Journal of Chromatographic Science2018; 48(10): 795–801. https://doi.org/10.1155/2018/3158293.

35. Noro T. Sekiya T. Katoh M. Oda Y. Miyase T.Kuroyanagi M. Inhibitors of xanthine oxidase from Alpinia galanga, Chemical Pharmarceutical Bulletin. 1998; 36: 244-248.https://doi.org/10.1248/cpb.36.244

36. Chourasiya SS, Sreedhar E, Babu KS, Shankaraiah N, Nayak VL, Ramakrishna S, Sravani S, Basaveswara Rao MV. Isolation, synthesis and biological evaluation of phenylpropanoids from the rhizomes of Alpania galanga. Natural Product Communications. 2013; 8(12):1741-6.https://doi.org/10.1177/1934578X1300801222

37. van Meerloo J, Kasper, GJL, Cloos J Cell Sensitivity Assays: The MTT Assay. In: Cree, I. (eds) Cancer Cell Culture. Methods in Molecular Biology. 2011; vol 731. Humana Press. https://doi.org/10.1007/978-1-61779-080-5_20

38. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2013. http://www.R-project.org/

39. Abdelgawad MA, Abdelaleem EA, Gamal M, Abourehab MAS, Abdelhamid NS. A new green approach for the reduction of consumed solvents and simultaneous quality control analysis of several pharmaceuticals using a fast and economic RP-HPLC method; a case study for a mixture of piracetam, ketoprofen and omeprazole drugs.The Royal Society of Chemistry RSC Advances. 2022; 12(25): 16301-16309. doi: 10.1039/d2ra02395d

40. FDA (Food and Drug Administration - Office of Regulatory Affairs). Methods, Method Verification and Validation. 2020. Accessed March 4, 2021. https://wwwfdagov/media/73920/download

41. ICH (International Conference of Harmonization). Validation of Analytical Procedures: Text and Methodology Q2 (R1). 2005. Accessed August 25, 2021. https://databaseichorg/sites/default/files/Q2%28R1%29%20Guideline.pd

42. Upadhye AS.Rajopadhye A. DiasL. Development and validation of HPTLC fingerprints of three species of Alpinia with biomarker Galangin. BMC Complementary and Alternative Medicine. 2018; 18(1): 1-5. https://doi.org/10.1186/s12906-017-2033-4

43. Lee JE, Lee BJ, Chung JO, Kim HN, Kim EH, Jung S, Lee H, Lee SJ, Hong YS. Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography. Food Chemistry 2014; 174: 452-459. doi: 10.1016/j.foodchem.2014.11.086.

44. Olivier D, van der Kooy F, Gerber M. Geographical and seasonal phytochemical variation of Artemisia afra Jacq. ex Willd. Phytochemical Analysis. 2023; 34(2):175-185. https://doi.org/10.1002/pca.3191

45. Yousefzadeh S. Abedi R.Mokhtassi-Bidgoli A. Which environmental factors are more important for geographic distributions of Thymus species and their physio-morphological and phytochemical variations?. Arabian Journal of Geosciences. 2021; 14(18), 1864. https://doi.org/10.1007/s12517-021-08253-2

46. Khattab AR. Rasheed DM. El-Haddad AE. Porzel A. Wessjohann LA. FaragMA. Assessing phytoequivalency of four Zingiberaceae spices (galangals, turmeric and ginger) using a biochemometric approach: A case study. Industrial Crops and Products. 2022; 188: 115722. https://doi.org/10.1016/j.indcrop.2022.115722

Received on 27.08.2023 Modified on 01.10.2023

Research J. Pharm. and Tech 2024; 17(5):2185-2192.

DOI: 10.52711/0974-360X.2024.00344

Analytes	Regression Equation	r2	LOD (µg/mL)	LOQ (µg/mL)
1	y= 8.8×10⁸x – 4.9×10⁵	0.9988	0.0015	0.0049
2	y= 4.8×10⁷x – 1.3×10⁵	1	0.0008	0.0027
3	y= 1.2×10⁸x – 6.0×10⁴	0.9979	0.0015	0.0050
4	y= 3.3×10⁸x – 2.0×10⁵	0.9996	0.0007	0.0023

Location	Code	1	2	3	4
Karangpandan	I-1	0.21	9.40	1.23	0.63
Karanganyar	I-2	0.52	25.45	0.58	0.26
Selogiri	I-3	0.12	8.15	0.52	0.14
Jogja	I-4	0.08	3.15	0.20	0.11
Solo	I-5	0.38	6.77	0.45	0.42
Wonogiri	I-6	0.14	1.96	0.40	0.26
Singkawang	I-7	0.06	2.78	0.22	0.09
Banjarmasin	I-8	0.04	0.76	0.33	0.12
Kudus	I-9	0.06	1.53	0.44	0.04
Lampung	I-10	0.04	0.80	0.26	0.10
Boyolali	I-11	0.06	3.29	0.29	0.11
Klaten	I-12	0.03	1.38	0.30	0.03