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 »  Materials and Me...
 » Results
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 Table of Contents    
RESEARCH ARTICLE
Year : 2019  |  Volume : 51  |  Issue : 3  |  Page : 181-207
 

In silico evaluation of apoptogenic potential and toxicological profile of triterpenoids


Department of Pharmacology, Maliba Pharmacy College, Tarsadi, Gujarat, India

Date of Submission14-Feb-2018
Date of Acceptance11-Jun-2019
Date of Web Publication9-Jul-2019

Correspondence Address:
Ms. Tanvi Himanshu Desai
Maliba Pharmacy College, Maliba Campus, Bardoli-Mahuva Road, Tarsadi - 394 350, Gujarat
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijp.IJP_90_18

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 » Abstract 


AIM: Caspases-3 and 8 are key mediators of intrinsic and extrinsic pathway of apoptosis, respectively. Triterpenoids of natural and synthetic origin reported as anticancer agents with apoptotic potential and hence may prove to be good candidates for in silico testing against caspases-3 and 8.
MATERIALS AND METHODS: Various naturally-occurring and synthetic triterpenoids were subjected to activity prediction using PASS Online software, and among them, 67 compounds were selected for further processing. Protein structure of caspase-3 (3DEI) and caspase-8 (3KJQ) was obtained from the protein data bank and docked with selected triterpenoids using AutoDock Tools and AutoDock Vina. Toxicological profile was predicted based on clinical manifestations using PASS online software.
RESULTS: The high docking score of -10.0, -9.9, -9.8, and -9.5 were shown by friedelin, tingenone, albiziasaponin A, and albiziasaponin C, respectively, for caspase-3, and -11.0, -9.6, -9.6, and -9.4 by β-boswellic acid, bryonolic acid, canophyllic acid, and CDDO, respectively, for caspase-8. Possible adverse events were predicted with varying degree of probability and major relevant effects were reported. Hydrostatic interactions along with formation of hydrogen bonds with specific amino acids in the binding pocket were identified with each triterpenoid.
CONCLUSION: Lead molecules identified through this in silico study such as friedelin, tingenone, albiziasaponin, bryonolic acid, and canophyllic acid may be utilized for further in vitro/in vivo studies as apoptotic agents targeting caspases-3 and 8.


Keywords: Anticancer agents, AutoDock Vina, caspases-3 and 8, PASS Online


How to cite this article:
Desai TH, Joshi SV. In silico evaluation of apoptogenic potential and toxicological profile of triterpenoids. Indian J Pharmacol 2019;51:181-207

How to cite this URL:
Desai TH, Joshi SV. In silico evaluation of apoptogenic potential and toxicological profile of triterpenoids. Indian J Pharmacol [serial online] 2019 [cited 2019 Sep 21];51:181-207. Available from: http://www.ijp-online.com/text.asp?2019/51/3/181/262462




A poptosis is a regulatory mechanism controlling cell proliferation in physiologic and pathologic conditions.[1],[2] It plays a vital role in development, aging, and defense mechanism through the immune system. In oncology, apoptosis proved to be the prime target for anticancer research which comprises two pathways: intrinsic mitochondrial-dependent pathway and the extrinsic death receptor-mediated pathway. Caspases, cysteine-dependent aspartate-directed proteases, are the vital enzymes which synchronize the cellular events of apoptosis. Caspase-8, a member of initiator caspases, mediates extrinsic pathway,[3] while caspase-3, a member of execution caspases, mediates intrinsic pathway.[4]

Triterpenoid saponins are the phytoconstituents reported to exhibit antimetastasis, antiproliferative, anti-angiogenic, and reversal of multidrug resistance effects through induction of apoptosis and help in cell differentiation.[5] Saponins were reported to inhibit the growth of human breast cancer cell lines and induce apoptosis in Jurkat cells.[6] Various triterpenoids such as gymnemic acid,[7],[8] rotundic acid,[9] and euscaphic acid[10] are either evaluated or suggested to be a promoter of caspase-3- or caspase-8-mediated apoptosis.

This proposed apoptogenic potential of various triterpenoids motivated us to perform in silico screening of naturally occurring and synthetic triterpenoids. We used combination of pharmacological activity and toxicity prediction softwares such as “PASS online”[11],[12],[13] along with molecular docking software “Autodock Vina”[14] and “LigPlot+”[15] which generate schematic two-dimensional (2D) representations of protein-ligand complexes. PASS online is an online activity prediction software which claims 95% accuracy in their results. It predicts the activity by comparing the provided structure with over 260,000 of drug-like biologically active compounds.[16] AutoDock Vina, one of the most widely used docking softwares, based on gradient optimization algorithm uses empirical scoring function to achieve speed and accuracy in predicting binding modes for ligand on protein molecules,[14] LigPlot+,” which generates 2D representations of protein-ligand complexes, was used to identify amino acids in target protein where ligand binds, and nature of binding in terms of “type of bond,” i.e., hydrogen bond, covalent bond, etc.[15]


 » Materials and Methods Top


Pharmacological activity prediction in PASS Online

Various naturally-occurring and synthetic triterpenoids were identified based on the literature search using PubMed and Google Scholar search engines. Structures of these triterpenoids were either downloaded in molecular data file format (MDL Molfile), i.e., mol extension from websites https://pubchem.ncbi.nlm.nih.gov/, http://www.chemspider.com/, and http://www.ebi.ac.uk/or downloaded in available format and converted to.mol extension using OpenBabel 2.4.1. Activity prediction is carried out with PASS online as reported elsewhere. Structures in.mol file format were uploaded in PASS online for prediction. Predicted activities were filtered by searching results for caspase-3 and caspase-8 and 67 compounds were selected for further processing. Corresponding Pa (probability to be active) and Pi (probability to be inactive) values were recorded.

Molecular docking with AutoDock Vina

The MDL Mol files of selected triterpenoid structures were converted to protein data bank (PDB) file format (.pdb format) using OpenBabel 2.4.1 and processed with AutoDock Tools 4.2.6. Protein structures, i.e., caspase-3 (PDB ID-3DEI) and caspase-8 (PDB ID-3KJQ) were obtained from Research Collaboratory for Structural Bioinformatics website in.pdb format.

Preparation of ligands for docking

The 3D.mol format structures of triterpenoids were converted to PDB file format using Open Babel 2.4.1 and accessed through AutoDock Tool. Polar hydrogens were added. Gasteiger charges were computed, nonpolar hydrogens were merged, torsion was defined, and files were saved as.pdbqt format.

Preparation of proteins for docking

Caspase-3 (PDB ID-3DEI) and caspase-8 (PDB ID-3KJQ) files accessed using AutoDock Tool. The attached ligands were identified and deleted before processing. Water molecules were removed; both polar and nonpolar hydrogens were added. Gasteiger charges were computed and files were saved as.pdbqt format.

Ligand binding sites on protein

Ligand-binding sites on protein, i.e., caspase-3 and caspase-8 were identified using MetaPocket 2.0, a meta-server which uses consensus method to predict the binding sites using eight methods: LIGSITEcs, PASS, Q-SiteFinder, SURFNET, Fpocket, GHECOM, ConCavity, and POCASA. The best three pocket sites were identified and used for docking with triterpenoid structures.

Determination of grid box and molecular docking

Parameters for grid box were decided based on pocket identified by MetaPocket 2.0. These values were used to dock the corresponding protein with triterpenoid structures in AutoDock Vina. Protein-ligand binding was visualized using UCSF Chimera and 2D schematic diagrams of interactions were obtained with LigPlot+ software. Amino acids showing interactions with ligand structures were identified.

Toxicity profile

Major adverse effects were predicted depending on their structural peculiarities by comparing with the data of over 260,000 compounds using PASS prediction software. General Unrestricted Structure-Activity Relationships (GUSAR) software is used to predict LD50 values in rats by various routes of drug administration.


 » Results Top


Pharmacological activity prediction in PASS Online

The PASS online predictions related asiaticoside, madecassoside, maniladiol, betulinic acid, oleanolic acid, hederagenin, and crotalic acid have highest probability to be active (Pa) for stimulation of caspase-3. The Pa scores for these triterpenoids were 0.990, 0.988, 0.987, 0.985, 0.984, 0.982, and 0.956, respectively. While albiziasaponin C, phytolaccagenin, arjunolic acid, hyptatic acid, coussaric acid, and glycyrrhizin showed Pa values 0.975, 0.921, 0.909, 0.909, 0.893, and 0.873, respectively, for stimulation of caspase-8. [Table 1] denotes Pa and Pi values of triterpenoids for caspase-3 and caspase-8.
Table 1: PASS online prediction and molecular docking studies of triterpenoids

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Molecular docking with AudoDock Vina

Glycyrrhizin, friedelin, canophyllal, albiziasaponin C, tingenone, withaferin A, and epifriedelanol had highest binding affinity for caspase-3. The binding-free energy for these triterpenoids was − 10.1, −10.0, −10.0, −9.9, −9.8, −9.7, and −9.5 kcal/mol, respectively. While β-boswellic acid, CDDO, canophyllic acid, tingenone, and bryonolic acid showed −10.1, −10.0, −10.0, −9.9, −9.8, −9.7, and −9.5 kcal/mol of binding-free energy, respectively, for caspase-8. [Table 1] showed results for docking with all triterpenoids with caspase-3 and caspase-8. We also identified amino acids in the binding pocket on target proteins and type of bonding to map the orientation of ligands in binding pocket [Table 1].

Toxicity profile

In silico-predicted toxicities were filtered by applying a cutoff of Pa = 0.7 and most significant were reported in [Table 2]. LD50 for various routes of administration were also predicted and presented.
Table 2: Toxicological profile of triterpenoids

Click here to view



 » Discussion Top


Triterpenoids are metabolites of isopentenyl pyrophosphate oligomers and are ubiquitously distributed throughout the plant kingdom.[45] These are a class of chemical compounds composed of three terpene units with the molecular formula C30H48.[46] Naturally-occurring[47], as well as synthetic triterpenoids[48],[49], have wide spectrum of biological and pharmacological effects, including anti-inflammatory, antibacterial, antiviral, hepatoprotective, gastroprotective, anti-ulcer, cardiovascular, hypolipidemic, antiatherosclerotic, immunoregulatory, anticancer, and cancer preventive activities.[50]

Recently, adulteration, content variation, and hence safety of herbal medicaments or formulations forced researchers to search for alternative strategy to isolate, standardize, and formulate herbal drugs.[51] Ancient approach used mixture of multiple herbal components as a drug,[52] but modern medical science shifted the approach to isolate single component and establish the pharmacological significance of it.[53] In our study, we pushed this approach further and carried out in silico testing of these herbal components, i.e., triterpenoids to predict the pharmacological activities and toxicities of these compounds.

Through literature search, we identified various natural and synthetic triterpenoids and obtained their structures [Table 1]. These triterpenoids were subjected for pharmacological activity prediction using PASS Online software and those showing Pa >0.7 either for anticancer activity, apoptogenic potential, or caspase-3 and/or caspase-8 stimulation were selected for further processing. We obtained 67 triterpenoids, the activity prediction data of which is represented in [Table 1]. Among these 67 compounds, 15 were previously reported for caspase-3 and/or caspase-8 stimulation activity with Pa of above 0.7 (11 out of 15 for caspase-3 and 9 out of 15 for caspase-8). This ascertains the vitality of in silico predictions to determine lead compounds from the herbal origin.

Further, we processed these compounds for docking against caspase-3 and caspase-8 through AutoDock Vina. We identified the binding pocket on proteins and docking was done targeting these pockets. The minimum binding-free energy of −10.0, −9.9, −9.8, and −9.5 were shown by friedelin, tingenone, albiziasaponin A, and albiziasaponin C, respectively, for caspases-3, and -11, -9.6, -9.6, -9.4 by β-boswellic acid, bryonolic acid, canophyllic acid, and CDDO, respectively, for caspase-8 [Table 1]. Most of these compounds are reported for their anticancer activity, but the exact mechanism is still remained to be established. The results obtained in our study pointed toward the apoptogenic potential through stimulation of either caspase-3 or caspase-8 as a mechanism for these compounds, and future studies may target these proteins.

Safety of triterpenoid saponins always remained as a concern for authorities,[54],[55] especially in Western countries. Uses of these saponins are even prohibited in some countries, particularly in Central and Southern America,[55] hence we addressed this issue by predicting the toxicities of triterpenoids selected for our study through GUSAR method. The results obtained are reported in [Table 2]. We even predicted LD50 of these triterpenoids in rats by various routes of administration. Most of these triterpenoids were predicted to be inflammatory in nature and produces hematotoxicity. Various previous studies reported triterpenoid saponins as hematotoxic[54],[56],[57] which supports results we obtained in our study through in silico predictions.


 » Conclusion Top


The predictions of caspase-stimulant activity (along with docking with caspase-3 and 8) and toxicity profile of triterpenoids helped in lead compound identification, e.g., albiziasaponin A, albiziasaponin C, caratuberoside A, and canophyllal for further in vitro and in vivo studies. Results obtained in our study will be helpful to researchers in planning further studies with these triterpenoids.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Tables

  [Table 1], [Table 2]



 

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