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Astragalin identification in graviola pericarp indicates a possible participation in the anticancer activity of pericarp crude extracts: In vitro and in silico approaches
⁎Corresponding author. rafaelsilvatorres@hotmail.com (Rafael Silva-Torres)
-
Received: ,
Accepted: ,
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
Peer review under responsibility of King Saud University.
Abstract
Graviola, soursop, or guanabana (Annona muricata L.), is an ethnomedical fruit consumed to alleviate headache, diarrhea, diabetes, and cancer. Pericarp is the inedible part of graviola least studied in comparison to seeds and leaves, even thought, it contains the highest concentration of graviola total polyphenols. Anticancer effect of graviola pericarp has been demonstrated in crude extracts attributing the effect to acetogenins, however, crude extracts contain several active molecules. Thus, the present work aimed to fractionate and purify an ethanolic crude extract from graviola pericarp. Purified graviola pericarp fraction (PGPF) was evaluated on cancerous and non-cancerous cell lines, and then was identified by NMR, TOF-MS, and HPLC. Finally, an in silico analysis was performed to predict targets cancer-related of the molecule detected. Our results revealed IC50 values for cervix adenocarcinoma (HeLa), hepatocellular carcinoma (HepG2), triple-negative breast cancer (MDA-MB-231), and non-cancerous cell line (HaCaT) of 92.85 ± 1.23, 81.70 ± 1.09, 84.28 ± 1.08, and 170.2 ± 1.12 µg PGPF/mL, respectively. In vitro therapeutic indexes estimated as quantitative relationship between safety and efficacy of PGPF were 1.83, 2.08, and 2.02 for HeLa, HepG2, and MDA-MB-231, respectively. The NMR analysis revealed astragalin (kaempferol-3-O-glucoside) in PGPF, a flavonoid not reported in graviola pericarp until now. Astragalin identity was confirmed by TOF-MS and HPLC. In silico results support previous reports about astragalin modulating proteins such as Bcl-2, CDK2, CDK4, MAPK and RAF1. Also, results suggest that astragalin may interact with other cancer-related proteins not associated previously with astragalin. In conclusion, astragalin may be contributing to the anticancer effect observed in graviola pericarp extracts.
Keywords
Annona muricata
Pericarp
Cancerous cells
kaempferol-3-O-glucoside
Astragalin
Targets prediction
- ACID
-
auto in silico consensus inverse docking
- ANOVA
-
analysis of variance
- DMEM
-
Dulbecco’s modified Eaglés medium
- ΔEbind
-
energy bind
- FBS
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fetal bovine serum
- HPLC
-
High-Performance Liquid Chromatography
- IC50
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half maximal inhibitory concentration
- NMR
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nuclear magnetic resonance
- TOF-MS
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Time-of-Flight Mass Spectra
- PGPF
-
purified graviola pericarp fraction
- PPI
-
protein–protein interaction
- SD
-
standard deviation
Abbreviations
1 Introduction
Graviola, soursop, or guanabana (Annona muricata L.), is an ethnomedical fruit distributed in the tropical regions of Central and South America, Western Africa, and Southeast Asia. Traditionally, inhabitants of these regions consume the fruit, leaves, bark, root, and seeds of graviola to alleviate headache, diarrhea, coughs, diabetes, and cancer (Coria-Téllez et al., 2018, Rady et al., 2018). The anticancer activity of A. muricata is mainly attributed to annonaceous acetogenins (Qazi et al., 2018, Rady et al., 2018, Yajid et al., 2018, Jacobo-Herrera et al., 2019, Errayes et al., 2020), even though, graviola contains active molecules such as alkaloids, phenols, and flavonoids (Prasad et al., 2019). Recently, the pericarp (peel, skin or husk) of fruits such as Punica granatum L. (Wong et al., 2021), Garcinia mangostana L. (Meylina et al., 2021), Camellia japonica L. (Cho et al., 2021), and Capsicum annuum L. (Chilczuk et al., 2020) has been identified as a source of bioactive molecules with anticancer properties. Analyses of graviola pericarp demonstrate acetogenins (Aguilar-Hernández et al., 2020) and the highest concentration of phenols, flavonoids (Adefegha et al., 2015), and total polyphenols (Aguilar-Hernández et al., 2019) compared with pulp, columella, and seeds. However, graviola pericarp is the inedible part least studied in comparison to seeds and leaves. Although, crude extracts from graviola pericarp exhibit antioxidant (Adefegha et al., 2015, Lee et al., 2016, Audu et al., 2019, Iyanda-Joel et al., 2019a, Orak et al., 2019), antiparasitic (Jaramillo et al., 2000), antibacterial (Karthikeyan et al., 2016, Iyanda-Joel et al., 2019b), antidiabetic, antihypertensive (Adefegha et al., 2015), and anticancer effects (Deep et al., 2016, Kuete et al., 2016, Robles et al., 2017, González-Pedroza et al., 2021, Jabir et al., 2021), the reported studies focus on crude extracts without purification and identification of molecules. Thus, the present work aimed to purify a fraction obtained from a crude extract of graviola pericarp. Purified graviola pericarp fraction (PGPF) was evaluated on cancerous and non-cancerous cell lines viability and PGPF identity was elucidated by Nuclear Magnetic Resonance (NMR), Time-of-Flight Mass (TOF-MS), and High-Performance Liquid Chromatography (HPLC). Finally, an in silico analysis was performed to predict targets cancer-related of the molecule detected.
2 Material and methods
2.1 Chemicals and cell lines
All reagents and solvents were analytical grade purchased from Sigma-Aldrich (St. Louis, MO, USA) and Merck KGaA (Darmstadt, HE, DEU). Cell culture reagents such as Dulbecco’s modified Eaglés medium (DMEM), fetal bovine serum (FBS), and penicillin–streptomycin antibiotics were acquired from GIBCO (Grand Island, NY, USA), while MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) reduction reagent was purchased from Sigma-Aldrich (St. Louis, MO, USA). Cervix adenocarcinoma (HeLa), hepatocellular carcinoma (HepG2), and triple-negative breast cancer (MDA-MB-231) cell lines were obtained from American Type Culture Collection (ATCC; Manassas, VA, USA), while non-cancerous immortalized keratinocytes (HaCaT) cell line was kindly provided by Dr. Mario Eugenio Cancino Díaz (Laboratorio de Inmunidad Innata, Departamento de Inmunología, Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional, México).
2.2 Graviola pericarp crude extract and phytochemical identification
Graviola fruit collected from Colima Mexico in February 2017 was identified as Annona muricata by the Botany Department (Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional). First, the fresh fruit was washed and peeled, and then the pericarp was fragmented and weighed. Pericarp crude extract was prepared by mixing ethanol (96 %, 3 L) and graviola pericarp (2.314 kg) for five days at room temperature. Solvent was removed under reduced pressure at 40 °C and secondary metabolites were identified in the crude extract by qualitative phytochemical screening tests as Dominguez described (1973). Coumarins, tannins, and quinones were detected by Erlich, jelly reagent, and ammonium hydroxide tests, respectively. Flavonoids were identified by Shinoda reagent and sodium hydroxide, while alkaloids were recognized by silicotungstic acid, Dragendorff method, and Mayer test.
2.3 Purified graviola pericarp fraction (PGPF)
Pericarp crude extract (42 g) was separated in a silica gel column with methanol-acetone (4:1). Fraction 5–8 was purified by preparative thin layer chromatography in silica gel 60 F254 plates eluting with methanol-acetone (4:1) and visualizing with UV light. Then, PGPF was evaluated on cancerous and non-cancerous cell viability, and PGPF identity was elucidated by NMR, TOF-MS, and HPLC.
2.4 Cell viability
The PGPF effect on cell viability of cancerous (HeLa, HepG2, and MDA-MB-231) and non-cancerous (HaCaT) cell lines was determined by MTT assay. Cells were grown and maintained as monolayer culture in DMEM supplemented with FBS (10 %), penicillin (100 U/mL), and streptomycin (100 µg/mL) at standard conditions in a humidified incubator (37 °C and 5 % CO2). The PGPF was prepared in serum-free DMEM (5 mg PGPF/mL) and then sterilized by a syringe driven filter (0.22 µm pore size). Cells were seeded in 96-well culture plates at 6x103 cells/well and incubated 24 h in 0, 5, 50, 100, 150, 200, 250, and 500 µg PGPF/mL in triplicate. The PGPF was removed, and cells were incubated 3 h in MTT solution (0.1 mg/mL). Finally, MTT was discarded, and formazan crystals were dissolved with isopropanol (pH = 4). Wells absorbances (Abs) were measured at 595 nm in an ELISA ELx808 reader (BioTek, Winooski, VT, USA), and data were used to calculate cell viability (%) for each cell line by the following formula: Cell viability (%) = (Abs595 of PGPF treated cells/Abs595 control cells)100
2.5 Statistical analysis
Cell viabilities from three independent experiments were analyzed by two-way analysis of variance (ANOVA) followed by Bonferroni post hoc test. The mean ± standard deviation (SD) was plotted for each cell line, and p ≤ 0.05 was considered as statistically significant. Statistical analysis and graphs were performed in GraphPad Prism 5 (GraphPad Software Inc.).
2.6 Half maximal inhibitory concentration (IC50) and in vitro therapeutic index
The IC50 of PGPF for each cell line was determined in GraphPad Prism 5. Cell viabilities from triplicate measures were normalized, and PGPF concentrations were transformed to logarithmic scale. Then, non-linear regression was performed to estimate the IC50 for each cell line. The IC50 ± SD, as well as the R2 value were reported. On the other hand, the in vitro therapeutic indexes for cancer cell lines were estimated by the following formula as González-Pedroza (2021) described. In vitro therapeutic index = IC50 non-cancerous cell line/IC50 cancerous cell line
2.7 Nuclear magnetic resonance (NMR), time-of-flight mass spectra (TOF-MS) and high-performance liquid chromatography (HPLC)
The PGPF was identified by 1H and 13C NMR. Tetradeuteromethanol (CD3OD) was used as solvent, and trimethylsilane (TMS) was the internal standard. Spectra were recorded on Varian Mercury-300 NMR spectrometer (300 MHz and 75.4 MHz) (Varian Inc., Palo Alto, CA, USA), and data were compared with literature and an astragalin standard. Molecule identity was confirmed by TOF-MS and purity was evaluated by HPLC, the experimental conditions are described in the supplementary section. Molecule structure was drawn in ACD/ChemSketh (Freeware) version 2019.2.2.
2.8 In silico analyses
2.8.1 Targets prediction and protein–protein interaction (PPI) network
Astragalin 2D structure in SDF format was downloaded from PubChem database (https://pubchem.ncbi.nlm.nih.gov/) as Compound ID: 5282102. Astragalin targets prediction was performed by inverse docking in ACID web server (Auto in silico Consensus Inverse Docking, http://chemyang.ccnu.edu.cn/ccb/server/ACID/index.php/home/index), selecting Vina, PSO, LeDock, and PLANTS as docking software. The consensus inverse docking program contains 809 approved targets and it evaluates the binding affinity between astragalin and each target in database, outputting top potential targets and corresponding energy terms. ACID results were ordered from lowest to highest energy bind (ΔEbind = kcal/mol). Target names and their origin organism were searched in UniProt data base (https://www.uniprot.org). The first 150 targets from Homo sapiens were uploaded to STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, https://string-db.org/) and a protein–protein interaction (PPI) network was determined. The STRING settings were selected as default: full STRING network, edges indicate evidence, all active interaction sources, medium confidence (0.4) for interaction score, non-maximum number of interactions to show, and interactive svg display mode. The STRING server presents an enrichment analysis of gene ontologies, pathways and domains that shows proteins grouped by their functional description. In the enrichment analysis proteins related with “Cancer”, “Pathways in cancer”, “Breast cancer pathway”, and “Cervical carcinoma cell” were selected from the DISEASES (DOID:162), KEGG Pathways (hsa05200), WikiPathways (WP4262), and TISSUES (BTO:0000180), respectively.
2.8.2 Statistical data
The web server STRING estimated and provided statistical data of the inferred PPI network. Number of edges expected, and p-value of PPI enrichment were determined. Also, functional enrichments presented a false discovery rate by the p-values corrected from multiple testing within each category using the Benjamini-Hochberg procedure.
3 Results and discussion
3.1 Graviola pericarp crude extract, phytochemical identification, and fraction purification
In this study, we obtained 52.8 g of brown and viscous ethanolic crude extract from 2.314 kg of graviola pericarp. Crude extract evaluation by qualitative phytochemical screening tests revealed flavonoids, tannins, coumarins, alkaloids, and quinones in agreement with previous reports of ethanolic extracts from graviola pericarp analyzed by qualitative methods (Iyanda-Joel et al., 2019a, Iyanda-Joel et al., 2019b). Reports of crude extracts from graviola pericarp demonstrate anticancer properties attributed mainly to acetogenins (Deep et al., 2016), albeit crude extracts contain phytochemicals related to anticancer activity such as flavonoids (Kubczak et al., 2021), tannins (El Omari et al., 2021), coumarins, alkaloids (Huang et al., 2017), and quinones (Verrax et al., 2011). Moreover, these studies focus on evaluating crude extracts without purifying or identifying the molecules involved. Thus, we procured a PGPF to evaluate its effect on cancerous and non-cancerous cells. After fractionating and purifying 42 g of ethanolic crude extract we obtained 1.2 g of yellow PGPF (total yield of 28.57 mg/g extract).
3.2 Effect of PGPF on cancerous and non-cancerous cell viability
The Fig. 1 shows the viability of cancerous HeLa, HepG2, and MDA-MB-231 cell lines and non-cancerous HaCaT cells after 24 h incubation with PGPF. The PGPF reduced more than 90% the cell viability in cancerous and non-cancerous cell lines exposed to the highest concentrations (250 and 500 µg PGPF/mL). While at 50, 100, 150, and 200 µg PGPF/mL cell viability was decreased depending on cell lines; cancerous cells were significantly more affected than non-cancerous cells. The IC50 of PGPF was determined (Table 1) and cell lines sensitivity to PGPF was in the following order: HepG2 > MDA-MB-231 > HeLa > HaCaT. Subsequently, the in vitro therapeutic index was estimated for each cancerous cell line as a quantitative relationship between PGPF safety (IC50 on HaCaT) and efficacy (IC50 on HeLa, HepG2, and MDA-MB-231). Although, there is no universal therapeutic index value considered sufficient or required for a drug candidate a high therapeutic index is preferable, but a lower therapeutic index may be acceptable for treatment of life-threatening diseases that have limited treatment options (Muller and Milton 2012) such as cancer. The in vitro therapeutic indexes obtained were 1.83 (HeLa), 2.08 (HepG2), and 2.02 (MDA-MB-231), that are 10.8, 12.2, and 11.9-fold greater than the value (0.17) reported by Caba et al. (2011) for 5-fluorouracil estimated in the breast cancer MCF-7 cell line. IC50, half maximal inhibitory concentration; SD, standard deviation; R2, from non-linear regression.
Effect of purified graviola pericarp fraction (PGPF) on cancerous and non-cancerous cell viability. Columns represent the means ± standard deviation (SD) from three independent experiments performed in triplicate. Symbols *, **, and *** indicate p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001, respectively. Viability of cancerous cell lines HeLa, HepG2, and MDA-MB-231 was compared with non-cancerous HaCaT viability at the same PGPF concentration.
Cell line
IC50 ± SD (µg PGPF/mL)
R2
In vitro therapeutic index
HaCaT
170.2 ± 1.12
0.8748
HeLa
92.85 ± 1.23
0.8888
1.83
HepG2
81.70 ± 1.09
0.9753
2.08
MDA-MB-231
84.28 ± 1.08
0.9759
2.02
3.3 PGPF identification by NMR, TOF-MS, and HPLC
The identity of PGPF was elucidated by NMR. Table 2 indicates the chemical shift values obtained from 1H and 13C spectrum. The NMR results unambiguously coincided with the spectrum values of kaempferol 3-O-glucoside, known as astragalin, previously identifyied in Allium paradoxum by Ghavam-Haghi and Sadeghi Dinani (2017). The TOF-MS results confirmed astragalin identity (Fig. S1), and estimations from HPLC indicated a purity of 97.3 % (Fig. S2). Kaempferol 3-O-glucoside (Fig. 2) is a flavonol common in nature (Calderon-Montano et al., 2011) identified in leaves from Annona muricata (Taiwo et al., 2019) and other species of Annonaceae family such as A. mucosa (Bicalho et al., 2012), A. macroprophyllata (Brindis et al., 2013), A. cherimola (Haykal et al., 2021), and U. rufa (Deepralard et al., 2009). However, there are not previous reports of astragalin in graviola pericarp. Interestingly, anticancer effect of astragalin on HeLa (Krauze-Baranowska et al., 2013, Zilla et al., 2014), HepG2 (Ahmed et al., 2016, Li et al., 2017, Hong et al., 2021), and MDA-MB-231 (Ahn et al., 2019) cell lines have been reported in agreement with our results (3.2 section). Also, protective effect of astragalin on HaCaT UV-irradiated was observed by Park et al. (2012). Thus, astragalin could be contributing to the anticancer properties reported for crude extracts of graviola pericarp. The identification of bioactive molecules in pericarp is relevant because it represents 20 % of the inedible graviola parts (Aguilar-Hernández et al., 2019), which are discarded as waste during the preparation of drinks, juices, jellies, jams, ice-creams, candies, and nectars in the food industry (Qazi et al., 2018). Therefore, bioactive molecules, such as astragalin, could be exploited at reduced costs by sustainable utilization of agri-food wastes (Ben-Othman et al., 2020).
PGPF
Standard
Literature*
Carbon
δ1H
δ13 C
δ1H
δ13 C
δ 1H ppm
δ13C
2
157.3
157.5
162.8
3
134.6
135.2
135.7
4
177.6
177.5
179.5
5
160.6
161.0
161.5
6
6.20 (1H, s)
99.4
6.21 (1H, s)
99.7
6.22 (1H, s)
99.7
7
164.6
164.8
165.9
8
6.40 (1H, s)
94.4
6.42 (1H, s)
94.7
6.41 (1H, s)
94.9
9
157.4
157.2
159.2
10
103.8
104.1
105.7
1΄
122.2
122.6
122.6
2́, 6́
6.80 (2H, d, J = 8.6)
131.2
6.81 (2H, d, J = 8.5)
131.6
6.91 (2H, d, J = 8.5)
132.3
3́, 5́
8.00 (2H, d, J = 8.6)
115.6
8.05 (2H, d, J = 8.5)
116.0
8.07 (2H, d, J = 8.5)
116.1
4́
160.7
159.9
158.5
1́́
5.30 (1H, d, J = 7.1)
102.5
5.29 (1H, d, J = 7.2)
102.7
5.26 (1H, d, J = 7.2)
103.9
2́́
3.90 (1H, dd, J = 10.3, 3.9)
74.5
3.76 (1H, dd, J = 10.3, 3.8)
74.7
3.46 (1H, dd, J = 10.4, 3.6)
75.7
3́́
4.03 (1H, m)
76.4
4.07 (1H, m)
76.8
3.37 (1H, m)
77.9
4́́
3.10 (1H, m)
71.5
3.14 (1H, m)
71.4
3.24 (1H, m)
71.3
5́́
3.30 (1H, m)
77.9
3.51(1H, m)
77.4
3.56 (1H, m)
78.4
6́́
3.40 (2H, d, J = 11.2)
62.2
3.6 (2H, d, J = 11.3)
62.5
3.70 (2H, d, J = 11.5)
62.6
Astragalin identified in PGPF.
3.4 In silico analyses
3.4.1 Astragalin targets prediction and interaction network
Astragalin exhibits various pharmacological properties, including anti-inflammatory, antioxidant, neurological, cardioprotective, antidiabetic, and anticancer effects (Peng et al., 2020). However, investigations are still mandatory to fully understand the mechanisms of action by which astragalin acts (Riaz et al., 2018). The anticancer effect of astragalin has been related with apoptosis induction. Hong et al. (2021), Wang et al. (2021), Xu et al. (2021), Yang et al. (2021), and You et al. (2017) suggest that astragalin induces apoptosis by modulation of Bcl-2 and Bax in liver, gastric, lung, colon, and melanoma cancers, respectively. Interestingly, apoptosis regulator Bcl-2 protein (Uniprot ID P10415, PDB 4LXD) ranks fourteenth (ΔEbind = –32.99 kcal/mol) in the results of targets prediction from ACID web server (Table S1). Thus, our results confirm the possible interaction of astragalin with Bcl-2, that is supported by Pirvu et al. (2018) in a molecular docking study. On the other hand, Yang et al. (2021) observed that astragalin induces cell arrest by modulation of CDK2 and CDK4 in cancerous colon cells. Both, cyclin-dependent kinase 2 (CDK2, Uniprot ID P4941, PDB 4EK4) and cyclin-dependent kinase 4 (CDK4, Uniprot ID P11802, PDB code 2W96) were identified as astragalin targets in our results with ΔEbind values −30.76 and −28.02 kcal/mol, respectively. The 150 targets of astragalin obtained by inverse docking in ACID web server are described in Table S1. After uploading the astragalin targets to STRING web server, the PPI network showed in Fig. 3 was obtained. Nodes represent the proteins (150) and edges indicate the interactions between proteins (698), network stats estimated a p-value < 1.0e−16 since 337 edges were expected. Consequently, predicted targets present more interactions among themselves than a random set of 150 proteins from the genome. From all the targets evaluated in STRING, 32 % present functional enrichments related with cancer. The p-values of functional enrichments were <0.001 (Table S2). Proteins related with cancer, pathways in cancer, breast cancer pathway, and cervical carcinoma cell were colored yellow, purple, red, and green in the PPI network, respectively (Fig. 3). A Venn diagram in Fig. 4 shows the relation among proteins grouped by description enrichments, MAP2K1 and RAF1 coincide with the four enrichments selected in STRING. Particularly, these proteins are components of the pathway RAS-MAPK considered a potential therapeutic target for cancer treatment (Santarpia et al., 2012). Cho et al. (2014) reported that astragalin ameliorated oxidative stress by modulating MAPK signaling in an asthma model, while Asaad et al. (2021) demonstrated the inhibition of Raf/MAPK pathway by astragalin in mice treated with paracetamol. Therefore, interactions of astragalin with the targets predicted in ACID web server have been suggested in previous reports, however, targets such as ALDH2, CYP19A1, HCK, LCK, MME, MTAP, NTRK2, PRKAA1, MET, ALK, FGFR3, FGFR4, JAK2, NTRK1, RARA, RARB, PRKCA, PARP1, BRAF, KIT, APEX1, HSPA5, PKM, TOP2A, CHUK, CYCS, EDNRB, F2, F2R, NFKBIA, PPARD, RXRA, and SMO, remains unexplored in relation with astragalin. In conclusion, anticancer activity of astragalin has been widely studied but its interactions with cancer related proteins are poorly understood. Our in silico results suggest astragalin interactions with proteins previously not reported and results propose that astragalin contributes to the anticancer effect observed in crude extracts from graviola pericarp. Nevertheless, detailed experiments of the interactions are required.
Protein-protein interaction (PPI) network of astragalin predicted targets. Nodes represent proteins obtained from astragalin targets prediction, while edges indicate interactions between proteins. Nodes related with cancer, pathways in cancer, breast cancer pathway, and cervical carcinoma cell were colored yellow, purple, red, and green, respectively.
Venn diagram from PPI network. Gene names of proteins grouped by the enrichment functions selected in STRING. Proteins are possible targets of astragalin elucidated by inverse docking in ACID web server.
4 Conclusion
Astragalin (kaempferol 3-O-glucoside) previously unidentified in graviola pericarp, was recognized in a PGPF obtained from a crude extract of graviola pericarp. The PGPF presented effect on cell viabilities of cancerous cell lines (HepG2, MDA-MB-231, and HeLa) at in vitro therapeutic indexes greater than 1.5. In silico targets prediction and analysis of PPI network suggest that astragalin interacts with proteins involved in cancer disease. Thus, astragalin may contribute to the anticancer effect observed in crude extracts from graviola pericarp.
Fund ing
This work was supported by Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional (SIP-IPN, project number: 20171072), and by the Consejo Nacional de Ciencia y Tecnología (CONACyT, grant number: 419181).
Availability of data and material
Not applicable.
Code availability
Not applicable.
Authors’ contributions
APX, performed in vitro and in silico assays, data analysis, diagrams elaboration, and the manuscript writing. RGE, planned and directed the in vitro and in silico analyses, reviewed data, and revised manuscript. DCF, performed molecule identification by NMR, data analysis, and revised the manuscript. STR, performed the extraction and purification, phytochemical identification, data analysis, and revised the manuscript.
Acknowledgements
The authors are grateful to Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional de México and to the Consejo Nacional de Ciencia y Tecnología for the support and scholarships awarded. Also, authors thank Dr. Mario Eugenio Cancino Díaz for donating HaCaT cell line.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Appendix A
Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1016/j.arabjc.2022.103720.
Appendix A
Supplementary material
The following are the Supplementary data to this article:Supplementary data 1
Supplementary data 1
