Exploring the endogenous potential of Hemidesmus indicus against breast cancer using in silico studies and quantification of 2-hydroxy-4-methoxy benzaldehyde through RP-HPLC

Abstract

Being a woman and getting older are the main risk factors for breast cancer. While admitting the increasing prevalence of breast cancer among females globally, there is an increasing urge for widening the range of chemical compounds that can act as potential inhibitors for certain cancer target receptors. Current investigation involves virtually screening of 19 protein receptors having major role in signal transduction pathway of breast cancer development against 47 compounds present in Hemidesmus indicus. Virtual screening and supplementary analysis were performed using freely available softwares, tools and online servers. To obtain meaningful results, a comparative scenario was created by screening FDA-approved drugs/drug analogues against the same 19 receptors by keeping all the parameters same as to that of ligands. Two ligands namely Taraxasteryl acetate and Rutin were found to be the best ligands with high binding affinity towards six protein receptors establishing strong receptor ligand interactions. Furthermore, the major volatile compound, a high demand flavouring agent and an isomer of vanillin, namely 2-hydroxy-4-methoxy benzaldehyde (MBALD) specifically found in the roots of Hemidesmus, was quantified by RP-HPLC using a reverse phase C-18 column. The methanolic extract of fresh roots was found to contain 0.221 mg of MBALD/gram of tissue. From the current investigation, it could be surmised that Hemidesmus indicus had demonstrated its potential in both pharmaceuticals and the food industry.

Introduction

Breast cancer is the second-leading cause of cancer death in women, aside from skin cancer, and is the highest cause of mortality among female cancer patients. Breast cancer (BC) accelerates an uncontrolled growth of particular breast cells and part of them can metastasize to other body parts eventually leading to death. Out of all, breast cancer accounts 25% of all types of newly diagnosed cancers in women (Hwang et al. 2019). Due to the heavy occurrence of this type of cancer, around 40,000 women die annually in the US alone, therefore, imposes serious threats among women globally. Among the various reasons involved in causing breast cancer, the most associated reason is the genetics besides chances of acquiring it with an increase in age in women, by virtue of this, it is considered as the disease of aging. This disease is characterized by uncontrolled cell proliferation, aberrant cell apoptosis, and tumor formation in breast tissues (Gam 2012). The most common yardstick for classifying breast cancer into four subtypes (Table 1) is based on the status of cell surface receptors or biomarkers viz., the estrogen receptor (ER), the progesterone receptor (PR), and the human epidermal growth factor 2 (HER2) receptor (Eroles et al. 2012). Among all these subtypes of breast cancer, triple-negative breast cancer (TNBC), an aggressive form of breast cancer exhibits heterogeneity at multiple levels hence constituting significant diagnostic and therapeutic challenges. It is one of the most serious threats as it has to be cured only by conventional chemotherapy and radiation therapy and no FDA-approved drugs are available for this condition. (Hwang et al. 2019). Though Trodelvy (drug) was granted accelerated approval by FDA, yet due to the risk of severe neutropenia, severe diarrhoea, hypersensitivity reactions including severe anaphylactic (allergic) reactions, it demands continuous patient monitoring and further clinical trials are required (FDA.gov 2020).

Table 1.

Subtypes of Breast Cancer based on the status of biomarkers

Subtype of cancer	Biomarker status			Prevalence %
	ER*	PR*	HER2*
Luminal A	Positive	Positive	Negative	most common of all, 50–60% of all breast cancers
Luminal B	Positive and/or	Positive	Positive	10 and 20% of all breast cancers
HER2 over-expressing	Negative	Negative	Positive	15 to 20% percent of all breast cancers
Triple negative breast cancer (TNBC)	Negative	Negative	Negative	15–20% percent of all breast cancers

*ER Estrogen Receptor; PR Progesterone Receptor; HER2 Human Epidermal Growth Factor 2

In developing countries, a silent crisis persists in cancer treatment and at least 50–60% of cancer victims use radiotherapy for the destruction of cancerous tumors, but the search for an inexpensive alternative therapy with minimal side effects persists. Though different conventional and non-conventional medicines have been prescribed, the adverse effects and dissatisfaction among users could not give enough relief to patients (Baskar et al. 2012).

The ethnobotanical knowledge has helped us to cure many fatal diseases with ease and the process continues. In this context, a rich diversity of chemical compounds found in plants, based on their intrinsic complexity, could represent a novel and promising approach as they can interact with different molecular receptors (Ferruzzi et al. 2013). From ancient times, mankind is exploiting mother nature particularly herbs to obtain natural products with varied chemical compositions which provides wide spectra of applications in various fields for finding novel lead compounds against a disease (Cragg and Newman 2013).

Hemidesmus indicus (HI) is one of the important medicinal plants used from ancient times for healing many ailments including cancer and holds significance in Ayurveda (Kawlni et al. 2017). Simple aqueous extract of HI roots consists of a variety of phytochemicals including phenols (1.1%), flavonoids (1.12%), saponins (12.55%), terpenoids (0.79%), coumarins (0.91%), alkaloids (1.23%), and tannins (3.06%) (Ananthi et al. 2010) and a series of novel compounds like coumarino-lignans (hemisdesmins) and steroidal glycosides (hemidesmosides A–C) (Manjulatha et al. 2014). Amidst all these compounds, an expensive and high-value flavor product, 2-hydroxy-4-methoxy benzaldehyde (MBALD) (Fig. 1) C₈H₈O₃ is the major compound of interest which displays a typical aroma to the roots and constitutes 91% of the total steam distillation product from the root (Darekar et al. 2009). The molecular weight is 152.147 g/mol, same as vanillin; the difference being in the positions of the hydroxyl and methoxy groups and thus often used as a substitute for vanilla in ice creams.

Fig. 1.

2-Hydroxy-4-methoxy benzaldehyde (MBALD)

Many reports are available on the anti-cancer activity of H. indicus. Thabrew et al. (2005) documented the cytotoxic activity of roots decoction on HepG2 cells. Pal et al. (2014) observed H. indicus decoction to be cytotoxic to MCF7 breast cancer cell line. Similar results could be seen in other publications on cancer prevention by H. indicus (Banerji et al. 2017; More and Mali 2018; Swathi et al. 2019).

In the present work, an attempt has been made to investigate and reveal the anti-breast cancerous property of H. indicus through in silico studies. The structure-based virtual screening (SBVS) was utilized to screen the compounds present in H. indicus against the potential drug targets for breast cancer and a comparison was also made with already available FDA-approved drugs in respect of binding interactions with receptor proteins using freely available softwares, tools, and online servers. Proteins that were found to be potential drug targets were chosen and used for the construction of a protein library. Based on earlier reports on GC/MS studies of H. indicus, the compounds discovered during the research were thoroughly analysed and compounds that were common from each study were chosen as ligands. A freely available software, Pyrex 0.8 was used for performing SBVS that utilized the Autodock Vina tool for virtual screening. Absorption, distribution, metabolism, and excretion (ADME) analysis was carried out for determining the drug likeness nature of the ligands using SwissADME (online free server). Potential hits obtained after in silico studies presumably be further used for drug development against breast cancer after carrying out in vitro experiments, in vivo studies and clinical trials as well. Further efforts were made using aqueous methanolic extract to quantify 2-hydroxy-4-methoxy benzaldehyde, an astounding food flavouring metabolite and a major compound from fresh roots of Hemidesmus indicus.

Materials and methods

In silico virtual screening

Protein library preparation: Current investigation includes 19 different proteins that behave as target receptors having a role in different cell regulation pathways and are involved in causing breast cancer.

These 19 protein receptors were found to be actively participating in inducing breast cancer which were identified from breast cancer pathway map05224 of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further analysed for their role using available literature. The list of 19 proteins selected as target for ligands for virtual screening is given in Table 2. X-ray crystallographic structures of these proteins already complexed with their respective ligands were retrieved from the RCSB Protein Data Bank (Table 3).

Table 2.

Proteins selected as target receptors and their role in cancer development

Sr. no	Receptor	Role in cancer	References
1	Akt, also known as Protein kinase B	PI3K/AKT or Kinase signalling pathway plays major role in cellular processes The abnormal overexpression or activation of AKT has been found to be associated with risk of many cancers, increased cancer cell proliferation and their survival	Song et al. (2019)
2	CDK2	Cyclin-dependent kinases (CDKs) are mainly involved in regulation of cell-cycle The characteristics of “S” and ‘G2/M-phase” regulated by CDK1/CDK2 are significant events that may cause abruption in the process of cell division resulting in cancer	Chohan et al. (2018)
3	CDK6	Tumor growth can be seen as a result of aggravated levels of CDK4/CDK6 during G1-phase of cell cycle	Chohan et al. (2018)
4	EGFR: epidermal growth factor receptor	In cancer condition, EGFR is often constantly stimulated for the continuous production of EGFR ligands in the tumor microenvironment or due to mutation in EGFR which locks this protein receptor in a state of continual activation	Akhtar and Benter (2013)
5	ERK2	Certain processes in mammalian cells including cell cycle progression, differentiation, protein synthesis, metabolism, survival, migration and senescence are carried out by the RAS/RAF/MEK/ERK pathway. Mutations in gene components of these pathway like RAS or b-RAF leads to tumorigenesis	Ward et al. (2015)
6	Estrogen receptor (ER1)	Estrogen receptor viz. it’s receptor-mediated hormonal activity causing cellular proliferation, increased mutation rates causing direct genotoxic effects through a cytochrome P450-mediated metabolic activation, and induction of aneuploidy	Russo and Russo (2006)
7	FGFR: fibroblast growth factor receptors	FGF/FGFR signalling network under certain conditions plays a critical role in cancer cell proliferation, survival, differentiation, migration, and apoptosis	Tucker et al. (2014)
8	Grb2	Grb2 upregulation in association with EGFR/HER2 signalling results in breast cancer development	Ijaz et al. (2017)
9	HER2: human epidermal growth factor receptor 2	HER2-positive type patients show amplification or overexpression of the gene, i.e., upto 25–50 copies of gene, and up to 40–100-fold increase in HER2 protein resulting in 2 million receptors expressed at the tumor cell surface	Iqbal and Iqbal (2014)
10	IGFR1: insulin like growth factor receptor 1	In some breast cancer subtypes, activation and over-expression of IGF-1R has been found, additionally disease progression, increased resistance to radiotherapy and poor prognosis are linked with the molecules involved in downstream signalling of IGF-1R	Jackson and Lee (1997)
11	MAP2K1 also known as MEK1	Mitogen-activated protein kinase (MAPK) pathway is complex pathway which involves Ras/Raf/MEK/ERK receptor signaling cascade. As this pathway majorly deals with cell cycle progression and maintenance, any aberrant change may lead to tumorigenesis	Isshiki et al. (2011)
12	PARP1: poly ADP-ribose Polymerase1	One of the hallmarks of cancer is genomic instability caused by defect in DNA repair mechanism of a cells which is normally carried out by PARP proteins and subsequent mutations in that cells that promote the development of genotypes are found to be favourable for tumorigenesis	Dulaney et al. (2017)
13	PARP2: poly ADP-ribose Polymerase 2	Same as PARP1	Dulaney et al. (2017)
14	PI3K: phosphoinositide 3-kinase	In triple-negative breast cancer, aberrant activation of the PI3K pathway is mostly observed as the main cause	Papadimitriou et al. (2018)
15	PIM1: proto-oncogene 1	Overexpression of PIM1 has been observed in many cancer subtypes and acts as potential biomarker which contributes to the growth of cancer cells under abnormal conditions	Gao et al. (2019)
16	PTK6: protein tyrosine kinase 6	PTK6 overexpression contributes to anchorage-independent survival, proliferation, and migration of breast cancer cells	Park et al. (2015)
17	SOS1: son of sevenless1	Overexpression of SOS1, a component of EGF-dependent pathways facilitates cell growth and survival of cancer cells	De et al. (2014)
18	VEGFR: vascular endothelial growth factor A (VEGF-A) VEGFR1	VEGF-A primarily mediates tumor angiogenesis via two receptors VEGFR-1 and VEGFR-2. To date, the role of VEGFR-1 in angiogenic signal delivery for VEGF in tumor angiogenesis is poorly examined and still not entirely clear. Targeting the VEGF receptors VEGFR-1 & VEGFR-2 will have a potential impact on the motility of tumor cells	Srabovic et al. (2013)
19	VEGFR2	Same as VEGFR1	Srabovic et al. (2013)

Table 3.

Various protein receptors with PDB ID with Amino acid involved in active site complexed with respective drugs/inhibitors

Sr	Protein	PDB ID	Amino acid residues of active sites	Inhibitor/drug (from Pubchem)	References
1	Akt	4GV1	Glu228, Ala230, Glu234, Glu278, Asp292	Capivasertib	Addie et al. (2013)
2	CDK2	1PYE	Leu83, Phe82, Lys33, Gln131, Glu81, Ile10, Asp145, Leu134	Aminoimidazo[1,2-a] pyridines	Hamdouchi et al. (2004)
3	CDK6	5L2I	His100, Thr107, Phe98, Lys43, Tyr24, Asp104	Palbociclib	Chen et al. (2016)
4	EGFR	1M17	Gly696, Gly1022	Afatinib	Stamos et al. (2002)
5	ERK2	4ZZN	Asp106, Lys54, Leu107, Gln105, Cys166, Glu71, Asp167, Met108	CQ8	Ward et al. (2015)
6	ER1	2OUZ	Asp 351, Leu540, His524, Leu525	Lasofoxifene	Vajdos et al. (2007)
7	FGR1	4V01	Tyr563, Val561, Glu531, Ala564, Phe642, Asp641, Ile620	Ponatinib	Tucker et al. (2014)
8	Grb2	1X0N	Arg67, Arg86, Lys109, His107, Trp121, Leu11	BDBM50102025	Ogura et al. (2008)
9	HER2	3RCD	Met801, Lys753, Asp863, Glu770, Gly865, Phe864, Thr862	TAK-285	Ishikawa et al. (2011)
10	IGFR1	3D94	Gly1122, Gly1125, Met1052, Met1049, Met1112, Lys1003, Asp1056, Asp1123, Phe1124, Phe980, Leu975	PQIP	Wu et al. (2008)
11	MEK1	3OS3	Lys97, Val127, Val211, Ser212,	CH4858061	Isshiki et al. (2011)
12	PARP1	4UND	Glu988, Tyr896, Tyr907, Asp766, Gly863, Arg878, Ser904, Glu763	Talazoparib	Thorsell et al. (2017)
13	PARP2	4TVJ	Arg444, Tyr462, Tyr473, Ser470, Gly429, Glu335, Asp339	Olaparib	Thorsell et al. (2017)
14	PI3K	3L08	Val882, Lys833, Tyr867	Omipalisib	Knight et al. (2010)
15	PIM1	2O64	Arg122, Ser54, Leu120, Glu89, Glu121, Glu124, Asp186, Lys67, Val126,Pro123	Quercetagetin	Holder et al. (2007)
16	PTK6	5H2U	Arg195, Ala217, Glu235, Glu274, Ile262, Phe331, Asp330, Gly329, Leu248, Leu319, Ser271, Met267, Thr264	Dasatinib	Thakur et al. (2017)
17	SOS1	5OVE	Asn879, His905, Leu901, Met878	AXE	Hillig et al. (2019)
18	VEGFR1	3HNG	Glu878, Asp1040, Cys912, Ile1038, Val907, Glu910, Tyr 911, Ala 859	CHEMBL101683	Tresaugues et al. (2013)
19	VEGFR2	3WZD	Ala866,Phe918,Val848,Val898,Val899, Cys919, Gly841,922, Asn923, Leu840, Leu1035,Leu1049, Ile888, Asp146	Levatinib	Okamoto et al. (2015)

Binding-site analysis: The receptors were chosen from the available literature and appropriate data were collected regarding the binding sites for each receptor which allowed site-specific docking of ligands for more precise binding.

Preparation of chemical library: For identifying the compounds present in Hemidesmus indicus many researchers have contributed to the literature. During GC/MS analysis of Hemidesmus indicus, Nagarajan et al. (2001), Murugan et al. (2018), and Sharma et al. (2017) have extracted many compounds with organic solvents from the root powder. A total of 47 such compounds that were found to be prominent and common from these three articles were pooled and selected to serve as ligands (Table 4). All the compounds were retrieved from the PubChem database in SDF format. Compounds were converted to mol2 format using Open babel 3.0.0. (Boyle et al. 2011). For examining the significance of binding affinity of ligands with the protein receptors, currently available FDA-approved drugs for each protein receptor were also included in the study to get a meaningful comparison between the binding affinity of ligands and drugs towards the receptors. FDA-approved drugs for TNBC subtype were not available mainly since all the three particular protein receptors were not getting expressed simultaneously and this put together drive the disease difficult to cure. Under the above-mentioned circumstances, during this investigation, docking against any particular subtype of breast cancer was not focused. A separate drug library was maintained after downloading their structures from Pubchem.

Table 4.

Various H. indicus compounds selected for virtual screening with receptors

Sr. no	Compound	Sr. No	Compound
1	1,8-Cineole	25	Ferulic acid
2	2-Hydroxy-4- methoxy benzoic acid	26	Guaiacol
3	2-Hydroxy-4-methoxy-benzaldehyde	27	Hemidescine
4	3-Hydroxy-4-methoxy-benzaldehyde	28	Hemidine
5	4-Hydroxy-3-methoxy-benzaldehyde	29	Heminine
6	4-Isobutylaniline	30	Hemisine
7	16-Dehydropregnenolone	31	Indicine
8	Alpha-Amyrin	32	Isocaryophyllene
9	Alpha-Amyrin acetate	33	Isoquercitin
10	Alpha-Terpinyl acetate	34	Ledol
11	Anisaldehyde	35	Linalyl acetate
12	Beta-Amyrin	36	Lupanone
13	Beta-Amyrin acetate	37	Lupeol acetate
14	Beta-Amyrin palmitate	38	Lupeol
15	Beta-Sitosterol	39	Medidesmine
16	Borneol	40	Nerolidol
17	Campesterol	41	Nonadienal
18	Camphor	42	Octanoic acid
19	Cholesterol	43	Palmitic acid
20	Decanoic acid	44	Rutin
21	Desmisine	45	Salicylaldehyde
22	Dihydrocarvyl acetate	46	Taraxasteryl acetate
23	Dodecanoic acid	47	Thymol
24	Emidine	–	–

Protein preparation: Nineteen protein receptors maintained in the protein library were prepared for further analysis and were subjected to water removal and the removal of inhibitors/drugs complexed with protein receptor using UCSF Chimera 1.14 and were saved in PDB format. For preparation of proteins for virtual screening, Autodock 1.5.6 was utilized where polar hydrogens and Gasteiger charges were added (Morris et al. 2014). Proteins were finally saved in PDBQT format for further processing.

Virtual screening: in silico analysis of screening the 47 ligands against 19 receptor proteins were done by Pyrex 0.8 which has a built-in Autodock Vina tool for doing virtual screening (Dallakyan and Olson 2015). Energy minimization of ligands was done before doing screening to generate alternate conformations with rotatable bonds. After selecting ligands and macromolecules, the grid box (Table 5) was set to the required dimensions to achieve site-specific binding. The exhaustiveness of the run was kept at 8 (1–8) by default which sets the number of runs in parallel producing several results and captures the promising intermediate results and merges them in final results. The docking strategy followed involved screening of 47 ligands against one protein receptor at a time. All 19 receptors were screened in the same manner. The comparative scenario was created by performing virtual screening of FDA-approved drugs/drug analogues (Table 6) against receptors while keeping all the parameters same as that for ligands. Flowchart of virtual screening strategy is shown in Fig. 2.

Table 5.

Grid box dimensions used for target receptors for site-specific binding

Sr. no	Protein receptor	PDB ID	Center grid box	Grid dimensions
1	Akt	4GV1	center_x = − 26.4433962881 center_y = 5.87999413858 center_z = 11.481468964	size_x = 17.5491814942 size_y = 15.1645658997 size_z = 21.8659356895
2	CDK2	1PYE	center_x = 12.1595741137 center_y = − 7.70462760237 center_z = 23.0591549663	size_x = 17.7521482273 size_y = 19.2645153251 size_z = 19.3582063978
3	CDK6	5L2I	center_x = 13.6859667843 center_y = 27.3511289596 center_z = 6.22084657539	size_x = 16.7923335687 size_y = 20.0245340571 size_z = 17.5056366158
4	EGFR	1M17	center_x = 23.5645 center_y = 1.03071634208 center_z = 59.3942	size_x = 25.0 size_y = 16.0328635225 size_z = 25.0
5	ERK2	4ZZN	center_x = − 12.2250832319 center_y = 8.05511487232 center_z = 40.667428966	size_x = 29.0012970612 size_y = 21.8798196449 size_z = 25.4618379814
6	ER1	2OUZ	center_x = 35.710540759 center_y = − 1.11024654188 center_z = 19.8339786257	size_x = 21.971205293 size_y = 11.4629069162 size_z = 21.0837572515
7	FGR1	4V01	center_x = 91.6988892157 center_y = 1.02538503952 center_z = 14.3191711067	size_x = 14.6200017781 size_y = 14.214699205 size_z = 28.5126577866
8	Grb2	1X0N	center_x = 10.4649827631 center_y = − 5.06671193968 center_z = − 11.4349550251	size_x = 19.3516503493 size_y = 16.0383761206 size_z = 16.9432965669
9	HER2	3RCD	center_x = 9.57195981331 center_y = 0.245084953123 center_z = 30.4012441869	size_x = 22.2285879054 size_y = 14.2196042874 size_z = 29.4330189124
10	IGFR1	3D94	center_x = 24.701693415 center_y = 20.141 center_z = − 5.69169550793	size_x = 20.4824446255 size_y = 25.0 size_z = 21.3766089841
11	MEK1	3OS3	center_x = 6.50648876111 center_y = 40.8855898132 center_z = − 7.97359571258	size_x = 12.4307617464 size_y = 18.6773087119 size_z = 11.4541015458
12	PARP1	4UND	center_x = 2.52013090416 center_y = 64.7952621889 center_z = 189.002653312	size_x = 22.1182356719 size_y = 33.4007987591 size_z = 26.1022239335
13	PARP2	4TVJ	center_x = 17.1830806036 center_y = − 1.36290358543 center_z = 15.2093271218	size_x = 18.5955056509 size_y = 23.4819401532 size_z = 19.9685457564
14	PI3K	3L08	center_x = 22.642825206 center_y = 9.16699071362 center_z = 25.655091069	size_x = 20.0860669009 size_y = 22.7169166893 size_z = 29.8341638301
15	PIM1	2O64	center_x = 75.3772686654 center_y = 36.5030541821 center_z = − 2.32919493626	size_x = 18.6933550319 size_y = 29.1592228792 size_z = 18.0199945636
16	PTK6	5H2U	center_x = 30.939769054 center_y = − 0.972784774999 center_z = 40.7047208664	size_x = 26.4239844981 size_y = 33.6100567727 size_z = 34.7953371444
17	SOS1	5OVE	center_x = − 1.13570940655 center_y = − 32.3718926619 center_z = 42.5591887498	size_x = 18.1005232395 size_y = 19.2871979035 size_z = 13.389975125
18	VEGFR1	3HNG	center_x = 5.3838 center_y = 18.4599 center_z = 25.3055	size_x = 25 size_y = 25 size_z = 25
19	VEGFR2	3WZD	center_x = 3.2326708109 center_y = − 4.76562803382 center_z =  z = 14.3045293041	size_x = 23.9182816046 size_y = 22.0987087958 size_z = 30.4377413918

Table 6.

Target protein receptors and their corresponding FDA-approved drugs/drug analogues selected for virtual screening

Sr. no	Target protein	FDA-approved drugs/drug analogues
1	CDK2	Aminoimidazo [1,2a] pyridines
		TG-02
		AT7519
		AC1NCSZQ
		Dinaciclib
		Milciclib
2	EGFR	Erlotinib
		Neratinib
		Gefitinib
		Lapatinib
		Osimertinib
3	ERK2	CQ8
		AC1M8GZK
		Ravoxertinib
		DEL-22379
		LY3214996
4	HER2	TAK-285
		Afatinib
		Lapatinib
		Neratinib
		Aplaviroc
5	PARP1	Talazoparib
		Olaparib
		Niraparib
		Rucaparib
6	PARP2	Olaparib
		Niraparib
		Rucaparib
7	PIM1	Quercetagetin
		Lapatinib
		Dabrafenib
		Idelalisib
		Vemurafenib
		Nilotinib
8	PTK6	Dasatinib
		Pazopanib
		Dasatinib
		Vandetanib
		Sunitinib

Fig. 2.

Strategy followed for virtual screening

In silico ADME assessment: Usually, ADME assessment is the first step after selecting ligands but in this investigation, all 47 ligands were first screened against target receptors followed by their selection based on the performance of ADME parameters. In the first round of selection, ligands coupled with receptors were checked for ADME properties, the protein receptors with complexed ligand (that gave satisfactory results) were forwarded to the next round of screening against FDA-approved drugs. The rationale behind delaying ADME profiling was to avoid rejection of potent and possible drug-like ligands in the initial steps, moreover, in case, if a well-performing ligand fails to stand ADME parameters, it may be modified based on the need. For ADME analysis, ligands originally in SDF format were converted into smiles format using Open babel 3.0.0. and smiles were submitted onto the Swiss ADME online server for analysis that tested compounds for 46 various parameters. The obtained results were checked for few main parameters such as molecular weight, gastrointestinal (GI) absorption, blood brain barrier (BBB), P-glycoprotein substrates and inhibitors, cytochrome P (CYP) inhibitory promiscuity, Lipinski's rule of 5 (LRo5), druglikeness violations, and synthetic accessibility, otherwise the selection would have been difficult with all 46 parameters. Scoring was done for all 47 compounds using the above-mentioned parameters.

High-performance liquid chromatography

Plant material: Hemidesmus indicus plants were collected from the Forestry College and Research Institute, Mettupalayam (11.19′ N, 77.56′ E), Coimbatore, Tamil Nadu, India, and were maintained at the greenhouse facility available at the Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore. Fresh roots were used for the study.

Reagents and chemicals: Solvents including methanol and TFA used were of HPLC grade; mobile phase, and other reagents were prepared using Milli Q water.

HPLC condition: HPLC analysis was performed on the Shimadzu HPLC system equipped UV–Vis detector. Separation of compounds was achieved by the C18 reversed-phase column (INNO column, 5 µm, 120 Å, 4.6 × 250 mm). Shimadzu CLASS-VP^TMsoftware was used for data acquisition, processing, and reporting on the Windows XP platform. Solvent preparation was carried out by following the protocol given by Sircar et al. (2007). Isocratic solvent mixture (mobile phase) was prepared by adding 1 mM aqueous TFA and methanol in a 70:30 ratio with a flow rate of 1 ml min⁻¹. The wavelength was set to 280 nm for monitoring chromatograms. The sample was identified based on the comparison of retention time with those of the standard with keeping the same conditions.

Standard solution and sample preparation: HPLC grade (98%) 2-hydroxy-4-methoxy benzaldehyde standard was purchased from Sigma–Aldrich (Catalogue No. 160695, molecular weight 152.15 g/mol and PubChem Substance ID 24849887). A 1000 ppm standard stock solution was prepared by adding 1 mg of MBALD in 1 mL of aqueous methanol (50:50, v/v). The working standard solution was prepared with a concentration of 10 ppm by dilution of standard stock solution with aqueous methanol (50:50, v/v). All stock solutions were stored at 4 °C.

Sample plants maintained at the greenhouse were uprooted and roots were washed thoroughly under tap water to remove excess particles. 1 gm of fresh roots were macerated into a fine powder using liquid nitrogen and extracted with 5 mL of aqueous methanol (50:50, v/v). The extract was incubated for 2 days with continuous shaking and was subjected to centrifugation at 10,000 rpm for 10 min. The supernatant taken was first filtered with Whatmann filter paper no. 1 and further filtered through a 0.22 µm filter. 20 µl of the sample was injected into the HPLC system.

For the quantification purpose of 2-hydroxy-4-methoxy benzaldehyde, the retention time and peak area were obtained from the chromatogram. The formula for calculating the percentage of 2-hydroxy-4-methoxy benzaldehyde in the methanolic extract used is as follows:

$$\frac{\text{Peak area of sample}}{\text{Peak area of standard}}\times \frac{\text{Concentration of standard}}{\text{Concentration of sample}}\times \text{Purity of standard}$$

Results

In silico analysis

The current study aimed to reveal the anti-breast cancerous property of H. indicus phytochemicals by using in silico tools. Forty-seven compounds from H. indicus were chosen as ligands and in silico virtual screening (VS) was performed against the target proteins (Table 7). Complete SBVS analysis suggested that out of 19 target receptors, 6 protein receptors showed high binding affinity towards the ligands as compared to that of FDA-approved drugs (Table 8). Two ligands namely Taraxasteryl acetate and Rutin were majorly involved in building these strong interactions with minimum binding energy with the target receptors. Whereas, ERK2 and PIM1 were not considered for final results as they showed more binding affinity towards FDA-approved drugs as compared to that of their respective ligands.

Table 7.

Virtual screening of ligands with protein receptors and their binding energies

Sr. no.	Receptor	Ligand	Ligand ID and energy	Binding energy (kCal/mole)	rmsd/ub	rmsd/lb
1	Akt	2,Hydroxy-4-methoxy benzoic acid	21292822_uff_E = 167.22	− 7.5	0	0
2	*CDK2	Taraxasteryl acetate	13970053_uff_E = 798.31	− 11	0	0
3	CDK6	Heminine	102014181_uff_E = 909.83	− 9.6	0	0
4	*EGFR	Taraxasteryl acetate	13970053_uff_E = 1772.22	− 11	0	0
5	*ERK2	Rutin	5280805_uff_E = 751.59	− 9	0	0
6	Estrogen/ ER1	16-Dehydro-pregnenolone	92871_uff_E = 438.85	− 9.3	0	0
7	FGFR	Hemidine	101594607_uff_E = 718.42	− 8.6	0	0
8	Grb2	Emidine	101664026_uff_E = 1502.28	− 7.5	0	0
9	*HER2	Rutin	5280805_uff_E = 751.59	− 10.8	0	0
10	IGFRK1	Emidine	101664026_uff_E = 1502.28	− 8.6	0	0
11	MEK1	2,Hydroxy-4-methoxy benzoic acid	21292822_uff_E = 167.22	− 7.3	0	0
12	*PARP1	Rutin	5280805_uff_E = 751.59	− 12.4	0	0
13	*PARP2	Taraxasteryl acetate	13970053_uff_E = 798.31	− 12.4	0	0
14	PI3K	Desmisine	102446079_uff_E = 1499.09	− 10.2	0	0
15	*PIM1	Hemidescine	101664025_uff_E = 922.51	− 9.7	0	0
16	*PTK6	Rutin	5280805_uff_E = 751.59	− 10.4	0	0
17	SOS1	Cholesterol	5997_uff_E = 549.32	− 7.6	0	0
18	VEGFR1	Lupanone	129730785_uff_E = 857.53	− 9.9	0	0
19	VEGFR2	Campesterol	173183_uff_E = 573.30	− 9.8	0	0

*Indicates the receptor with respective ligand selected on the basis of ADME properties of ligands to be forwarded for next round of screening against FDA-approved drugs

Table 8.

Final comparison for minimum binding energy between FDA-approved drugs and H. indicus compounds selected for virtual screening with receptors

Sr. no.	Target protein	FDA-approved drugs/drug analogues	ID & energy	Binding energy (kCal/mole)	Ligand	ID & energy	Binding energy of ligands (kCal/mole)
1	CDK2	Aminoimidazo [1,2a] pyridines	11275025_uff_E = 290.74	− 5.3	Taraxasteryl acetate	13970053_uff_E = 798.31	− 11
		TG-02	16739650_uff_E = 341.49	− 8
		AT7519	11338033_uff_E = 609.37	− 6.9
		AC1NCSZQ	45380979_uff_E = 1851.79	− 6.1
		Dinaciclib	46926350_uff_E = 514.91	− 7.1
		Milciclib	16718576_uff_E = 766.77	− 6.8
2	EGFR	Erlotinib	176870_uff_E = 599.78	− 7.8	Taraxasteryl acetate	13970053_uff_E = 1772.22	− 11
		Neratinib	9915743_uff_E = 818.89	− 9.4
		Gefitinib	123631_uff_E = 518.86	− 7.9
		Lapatinib	208908_uff_E = 1028.85	− 9.1
		Osimertinib	71496458_uff_E = 865.16	− 8.1
3	*ERK2	CQ8	91758407_uff_E = 399.97	− 7.3	Rutin	5280805_uff_E = 751.59	− 9
		AC1M8GZK	2486631_uff_E = 961.91	− 9.6
		Ravoxertinib	71727581_uff_E = 578.46	− 8.2
		DEL− 22379	11224574_uff_E = 891.85	− 8.2
		LY3214996	121408882_uff_E = 938.70	− 7.6
4	HER2	TAK-285	11620908_uff_E = 669.77	− 6.2	Rutin	5280805_uff_E = 751.59	− 10.8
		Afatinib	10184653_uff_E = 543.13	− 6.7
		Lapatinib	208908_uff_E = 1028.85	− 8
		Neratinib	9915743_uff_E = 818.89	− 6.6
		Aplaviroc	3001322_uff_E = 576.31	− 7.9
5	PARP1	Talazoparib	135565082_uff_E = 560.9	− 9.5	Rutin	5280805_uff_E = 751.59	− 12.4
		Olaparib	23725625_uff_E = 1707.03	− 9.8
		Niraparib	24958200_uff_E = 461.77	− 8.1
		Rucaparib	9931954_uff_E = 653.31	− 8.1
6	PARP2	Olaparib	23725625_uff_E = 1707.03	− 9.7	Taraxasteryl acetate	13970053_uff_E = 798.31	− 12.4
		Niraparib	24958200_uff_E = 461.77	− 9.2
		Rucaparib	9931954_uff_E = 653.31	− 8.6
7	*PIM1	Quercetagetin	5281680_uff_E = 392.35	− 9.0	Hemidescine	101664025_uff_E = 922.51	9.7−
		Lapatinib	208908_uff_E = 1028.85	− 9.8
		Dabrafenib	44462760_uff_E = 1053.48	− 10.2
		Idelalisib	11625818_uff_E = 669.97	− 8.7
		Vemurafenib	42611257_uff_E = 1132.51	− 9.8
		Nilotinib	644241_uff_E = 626.59	− 10.8
8	PTK6	Dasatinib	3062316_uff_E = 744.65	− 6.4	Rutin	5280805_uff_E = 751.59	− 10.4
		Pazopanib	10113978_uff_E = 1068.53	− 6.9
		Dasatinib	3062316_uff_E = 744.65	− 6.4
		Vandetanib	3081361_uff_E = 464.09	− 6.1
		Sunitinib	5329102_uff_E = 836.64	− 6.2

*Shows the receptors which were excluded from final results as they failed to perform better than FDA-approved drugs/drug analogues

To describe briefly, six crystallized protein receptors were strongly bonded with two ligands in their active sites inhibiting them by affecting their interactions with other possible ligands. The maximum number of conventional hydrogen bonds (5) were formed between PTK6 residues namely ASP330, LYS219, ASN317, ARG316, GLU274, and Rutin that provided the stability to the complex. Whereas four conventional hydrogen bonds each were formed in the case of ERK2, HER2, and PARP1 when complexed with Rutin. EGFR formed three hydrogen bonds while CDK2 failed to form any hydrogen bonds when interacted with Taraxasteryl acetate. In case of virtual screening of FDA-approved drugs, two drug compounds viz., (1) AC1M8GZK (docked with ERK2 with the binding energy − 9.6) and (2) Dabrafenib (complexed with PIM1 with the binding energy − 10.2) bonded strongly with the receptors than the corresponding ligands. A detailed description of receptor–ligand interactions is given in the Table 9. Interactions of best receptor-FDA-approved drug/drug analogue complexes were also studied and results are summarized in Table 10.

Table 9.

Residues involved in the interactions between target receptors and ligand

Interactions	CDK2- Ligand	EGFR- Ligand	ERK2- Ligand	HER2- Ligand	PARP1- Ligand	PARP2- Ligand	PIM1- Ligand	PTK6- Ligand
van der WAALS	GLU8, ILE10, GLY11, ALA31, LYS33, PHE80, PHE82, LEU83, HIS84, GLN85, ASP86, LYS89, ASN132, LEU134	ALA719, MET769, LEU768, PRO770, GLY772, LEU820, CYS773, VAL702, ASP831, LYS721, MET742, LEU764	ASP165, ALA33, TYR34, GLU31, GLY32, LYS112, ILE82, ASP104, SER151, CYS164, THR108, MET106, LEU105, LYS52	VAL782, ILE752, THR798, ALA751, CYS805, LEU852, VAL734, GLY729, PHE731, ILE767, GLU770, GLY865, ASP863, PHE864, ILE861, ARG784	ASN868, ARG865, HIS909, GLN759, GLY888, LEU769, ILE872, LEU877, GLY876, ILE895, TRP861, ASN987, TYR9989, GLU988, SER904, GLY863, LYS903	ASN434, ILE438, LEU443, HIS428, TYR473, GLN324, ILE461, ASP339, ARG444, GLU335, ILE445, ALA446, TYR455, GLY454	PRO42, GLU124, PRO125, GLU171, VAL126, LEU44, VAL52, ILE185, LYS67, ASP186, GLU89, LEU120, ILE104, LEU174, ASP128, GLN127	LEU266, MET267, ALA268, GLY270, SER271, LEU273, GLY200, PHE202, GLY329, LEU248, THR264, ILE262, GLY198
CONVEN-TIONAL HB (no.)	–	(3) THR766, CYS751, THR830	(4) ASP109, GLN103, GLU107, ASN152	(4) ARG849, THR862, LEU785, SER783	(4) TYR896, PHE897, GLU763, SER864	(1) MET456	(2) SER54, ARG122	(5)ASP330, LYS219, ASN317, ARG316, GLU274
CARBON HB	–	–	–	–	ARG878	–	–	SER199
PI CATION	–	–	–	–	–	–	–	LYS219
PI DONOR HB	–	–	–	–	TYR889	–	–	–
PI SIGMA	–	–	ILE29	LEU785	–	TYR462	PHE49	LEU197 LEU319
PI SULFUR	–	–	–	MET774	–	–	–	–
PI–PI STACKED	–	–	–	–	TYR907	–	–	–
PI–PI T SHAPED	–	–	–	–	TYR896	–	-	–
ALKYL	VAL18, ALA144	LEU694	–	–	ALA880	–	–	–
PI AKYL	–	–	ALA50, LEU154, VAL37	LEU796, LYS753, LEU755	ALA898	–	–	VAL205, ALA217
UNFAV ACCEPTOR- ACCEPTOR	–	–	–	ASN850	–	–	–	–
UNFAV DONOR-DONOR	––	-	–	–	ARG878	–	–	ASN317

HB stands for hydrogen bond

UNFAV stands for unfavourable

Table 10.

Residues involved in the interactions between target receptors and FDA-approved drugs

Interactions	CDK2-FDA	EGFR- FDA	ERK2- FDA	HER2- FDA	PARP1- FDA	PARP2- FDA	PIM1- FDA	PTK6- FDA
van der Waals	GLU12, LYS129, GLN131, GLY11, ASP86, LEU134, VAL18	ASP813, PHE699, LEU834, GLY833, CYS773, GLU738, GLY772	TRP190, TYR191, SER151, GLU31, ILE82, ARG189, THR188, LYS112, GLY32, GLY30, TYR34, ALA33, ASP165	SER728, GLY727, PHE1004, ASP808, ALA730, LYS753	GLU688, LYS684, GLY913	ASP339, GLU335, ALA446, ILE445, TYR279, GLY338, ILE342, ILE275	SER46, GLY45, PHE49, ASP186, LEU120, ILE104, ALA65, ARG122, LEU174, PRO123, VAL126, ASP131	ARG431, PRO432, LEU437, CYS423, TRP371, LEU376, GLU298, GLN295, CYS326, HIS244, PHE331, LEU248, LEU303, TYR302
CONVEN-TIONAL HB (no.)	–	(1) THR766	(1) GLN103	(3) ARG849, LEU726, CYS805	(2) SER911, ASP914	(1) ARG444	(1) GLU121	(2) ASP369 MET300
CARBON HB	–	THR830, ASN818	ASP109	GLY729			ASP128, LEU44	ALA297
HALOGEN	–	–		ASP863, ASP845, ASN850	–	–	GLU171	–
UNFAV BUMP	–	–	–	–	–	–	–	VAL370, GLU304, THR366, PHE434, MET300, CYS301, LEU313, VAL296, GLY299, HIS246, ILE247
UNFAV DONOR-DONOR	LYS33	–	–	–	–	–	–	–
UNFAV +  +	–	–	–	–	–	–	–	–
PI CATION	–	–	LYS52, L–YS149	–	–	–	–	–
PI ANION	–	ASP831	–	–	–	–	ASP128	–
PI DONOR HB	–	–	–	–	–	–	–	–
PI SIGMA	–	–	–	–	–	VAL272	ILE185 VAL52	–
AMIDE PI STACKED	–	–	–	–	–	–	–	–
PI SULFUR	–	–	CYS164	–	–	–	–	MET300
PI-PI STACKED	–	–	–	–	–	–	–	–
PI-PI T SHAPED	–	–	TYR111	PHE731	–	–	–	PHE373
ALKYL	–	ARG817, LEU820, VAL702, MET742	ILE29, VAL37, ALA50	–	–	LYS276	–	LYS327, LYS367
PI AKYL	–	ALA719, LEU694, LEU764, LYS721	LEU154	VAL734	–	LYS276	LYS67	LYS327, LYS367
SALT BRIDGE	ASP145	–	–	–	–	–	–	–

HB stands for hydrogen bond

UNFAV stands for unfavourable

 +  + stands for positive–positive

Docked structures of ligands and FDA-approved drugs/drug analogues with target receptors are shown in Figs. 3, 4, 5, 6, 7, 8, 9, 10, whereas, comparative figures of interactions between ligands/receptor FDA-approved drugs with target receptors are shown in Figs. 11, 12, 13, 14, 15, 16, 17, 18. The virtual hits identified in this study could be used as an alternative targeting agent for breast cancer after being tested through in vitro experiments, animal lab studies, and clinical trials.

Fig. 3.

CDK2 in complex with Taraxasteryl acetate (red) and TG-02 (blue)

Fig. 4.

EGFR in complex with Taraxasteryl acetate (red) and Neratinib (blue)

Fig. 5.

ERK2 in complex with Rutin (red) and AC1M8GZK (blue)

Fig. 6.

HER2 in complex with Rutin (red) and Lapatinib (blue)

Fig. 7.

PARP1 in complex with Rutin (red) and Olaparib (blue)

Fig. 8.

PARP2 in complex with Taraxasteryl acetate (red) and Olaparib (blue)

Fig. 9.

PIM1 in complex with Hemidescine (red) and Nilotinib (blue)

Fig. 10.

PTK6 in complex with Rutin (red) and Pazopanib (blue)

Fig. 11.

a Receptor-ligand interaction between CDK2 and Taraxasteryl acetate. b Receptor-ligand interaction between CDK2 and TG-02

Fig. 12.

a Receptor-ligand interaction between EGFR and Taraxasteryl acetate. b Receptor-ligand interaction between EGFR and Neratinib

Fig. 13.

a Receptor-ligand interaction between ERK2 and Rutin. b Receptor-ligand interaction between ERK2 and AC1M8GZK

Fig. 14.

a Receptor-ligand interaction between HER2 and Rutin. b Receptor-ligand interaction between HER2 and Lapatinib

Fig. 15.

a Receptor-ligand interaction between PARP1 and Rutin. b Receptor-ligand interaction between PARP1 and Olaparib

Fig. 16.

a: Receptor-ligand interaction between PARP2 and Taraxasteryl acetate. b Receptor-ligand interaction between PARP2 and Olaparib

Fig. 17.

a Receptor-ligand interaction between PIM1 and Hemidescine. b Receptor-ligand interaction between PIM1 and Nilotinib

Fig. 18.

a Receptor-ligand interaction between PTK6 and Rutin. b Receptor-ligand interaction between PTK6 and Pazopanib

HPLC analysis

Quantification of 2-hydroxy-4-methoxy benzaldehyde was carried out using fresh roots extracted with aqueous methanolic extract and using an isocratic mixture of 1 mM TFA and methanol in 70:30 ratio as mobile phase with the flow rate of 1 ml min⁻¹ and was monitored at 280 nm with 25–30 °C temperature. The retention time for the standard 2-hydroxy-4-methoxy benzaldehyde at 10 ppm concentration was recorded in HPLC and was observed as 54.400 min at 280 nm (Fig. 19). Besides this, the peak for the sample was achieved at 55.499 min. (Fig. 20). The identity of the sample was confirmed by comparing the chromatograms of both standard and sample. Using the formula for calculating the percentage of compounds present in the sample, it was found that the methanolic root extract of fresh Hemidesmus indicus roots contains 0.221 mg of 2-hydroxy-4-methoxy benzaldehyde/gram of tissue.

Fig. 19.

HPLC-chromatogram of Standard sample (MBALD) at concentration of 10 ppm

Fig. 20.

HPLC-chromatogram of fresh root sample of Hemidesmus indicus

Discussion

Breast cancer derails societal stability as the mortality rate of different breast cancer subtypes is alarming and for one subtype, i.e., TNBC, pharmaceutical outputs/drugs are yet to hit the market and conventional therapies are not much effective but the only source of hope.

Earlier works on breast cancer have profoundly shown the anti-breast cancer activity of some aromatic compounds against potential drug targets. Taraxasteryl acetate has shown antiproliferative activity in vitro against TNBC MDA-MB-231 cells which are commonly used to model late-stage breast cancer (Ramos et al. 2017). Rutin has been seen as an emerging inhibitor of c-Met which can control TNBC (Elsayed et al. 2017). Cancer cell growth inhibition has been observed by benzoic acid derivatives which act on histone deacetylases (Anantharaju et al. 2017). Targeting some of these kinds of known drug targets with the existing available inputs could be seen as the bombarding strategy to tackle this disease.

The protein receptors performed well with the ligands, viz. CDK2, EGFR, and PARP2 interacted with Taraxasteryl acetate with the minimum binding energies of − 11, − 11, and − 12.4 kcal/mole respectively and HER2, PARP1, and PTK6 complexed with Rutin with minimum binding energies of − 10.8, − 12.4, and − 10.4 kcal/mole respectively. On the other hand, ERK2 and PIM1 made strong interactions with FDA-approved drugs/drug analogues. As CDKs plays critical role in governing cell cycle transitions, cell division, and cell cycle control through CDK inhibition has been re-established as an attractive option in the development of targeted cancer therapy (Ding et al. 2020), use of Taraxasteryl acetate as an CDK inhibitor could be seen as alternative in future.

In case of EGFR, it enhances cell proliferation and survival and regulates a multitude of cell signaling pathways towards ontogenesis but, in cancer conditions, EGFR either gets mutated or becomes activated without any stimulus or due to continuous production of EGFR ligand, gets stimulated constantly. In both the conditions, inhibiting EGFR may contribute to alleviate the diseased condition. EGFR-targeting therapeutics have yielded modest, unpredictable, and variable (1.7–38.7%) responses (Baselga et al. 2005; Savage et al. 2017) in breast cancer.

During HER2-positive subtype, breast cancer cells express higher than normal levels of HER2 and about one out of five breast cancers are HER2-positive. These cancers tend to grow and spread faster, more aggressive than other breast cancers but are much more likely to respond to treatment with drugs that target the HER2 protein (Weiss 2020) which calls for the opportunity to inhibit this receptor with the help of Rutin as a good alternative.

In some cases, breast cancer occurred might also be due to poly [ADP-ribose] polymerase 1 (PARP1) gene dysregulation. PARP1 and PARP2 both regulate DNA repair and transcription and PARP inhibitors cause synthetic lethality in BRCA-mutated cancer cells from defective DNA damage repair (Dziadkowiec et al. 2016). Moreover, during the current investigation, these two receptors had bound strongly (with the minimum binding energy of − 12.4) with Taraxasteryl acetate and Rutin suggesting use of these two ligands in future pharmaceuticals with desired modifications.

Breast tumor kinase (BRK, also known as PTK6) abundant in several tumor types, including prostate, ovarian, and breast cancers, overexpressed in about 85% of all breast carcinomas, is one of the important targets. It displays low or no expression in the normal mammary gland. The expression of PTK6 is directly correlated with histological tumor grade (Tsui and Miller 2015) which makes it a desirable target to be inhibited by particular ligand compound.

In today’s fast-growing industrial world, consumer demand for natural products free of synthetic chemical adulterants is increasing. This paradigm shift of going synthetic to natural has brought pressure on the available natural resources. The vast majority of these essential plant products with varied therapeutic and other potentials have merited special interest (Wang et al. 2010). These compounds are mostly secondary metabolites in nature and are opt for commercial production. The compound, 2-hydroxy-4-methoxy benzaldehyde usually found in the roots of Hemidesmus indicus is one of the major aromatic volatile constituents among other phytochemicals present in the species. HMBA gives a typical aroma to the roots and is one of the under-utilized flavouring agents (Nagarajan et al. 2001). The compound confers many properties to the plant such as anti-microbial, anti-aflatoxigenic potency, anti-oxidant, etc. It has good water solubility and is a potent tyrosinase inhibitor (Murthy et al. 2006). MBALD, an aromatic compound found in roots of HI is an isomer of vanillin and has high demand in the food and flavouring industry (Rathi et al. 2017). Therefore, quantifying the compound holds great importance from both the commercial and the therapeutical point of view. An earlier report on the quantification of MBALD from dried roots was found to be 0.2638 mg/g of tissue (Prathibha Devi et al. 2016). Whereas, the amount of MBALD in fresh roots observed is slightly different and comes around 0.221 mg/g of tissue.

Conclusion

Finding a new drug for a particular disease is now becoming a little bit easy because of computational aids like virtual screening and molecular docking as the time required for screening the ligand compounds has reduced considerably. From the current investigation, it is clear that earlier reports on the anti-cancer activity of H. indicus hold significance as the results of the current investigation strongly support the inhibitory activity of the H. indicus compounds for six potential drug targets. Moreover, the software and web servers utilized during the study were freely available which makes the investigation a cost-cutting one. Further investigation on pharmacokinetics/pharmacodynamics properties, molecular simulation, in vitro experiments, and medical trials of the inhibitory compounds can lead to a potential drug in the future for tackling this serious problem.

HPLC performed for the current study is simple and cost-effective as it utilizes only two HPLC grade chemicals namely trifluoroacetic acid and methanol. The quantification assay of the sample is easy to perform and analyse. This technique can be used routinely to serve various purposes like identifying and quantifying samples and their quality control. The retention time to achieve the peak for the sample is observed to be higher than the earlier established reports maybe because of the variations in column dimensions and HPLC conditions. Variation in the length and diameter of the existing reverse phase column could be seen as the way to reduce the retention time which will facilitate more accuracy by repeating the number of runs.