Computer-Assisted Prediction of Drug-Like Properties of Selected Phytochemicals from Terminalia chebula

INTRODUCTION

Terminalia chebula, commonly known as Haritaki, is a medicinal plant widely used in traditional medicine systems such as Ayurveda, Unani and traditional Chinese medicine. The fruit of Terminalia chebula is revered for its wide range of therapeutic properties, including antioxidant, anti-inflammatory, antimicrobial, anticancer and hepatoprotective effects. These effects are attributed to its rich phytochemical content, which includes tannins, flavonoids, glycosides and triterpenoids (Kumar et al., 2016; Sharma et al., 2017). For a compound to be considered a viable drug candidate, it must exhibit favorable drug-like properties such as solubility, permeability and bioavailability. These properties are crucial for ensuring that the compound can reach the target site in the body at therapeutic concentrations. Lipinski’s Rule of Five is commonly used to predict drug-likeness, taking into account factors such as molecular weight, hydrogen bond donors and acceptors and the octanol-water partition coefficient (logP) (Lipinski et al., 1997). Compounds that violate more than one of these criteria are generally considered less likely to be orally active drugs. The integration of computational tools in drug discovery, known as Computer-Aided Drug Design (CADD), has significantly enhanced the efficiency of the drug development process. Techniques such as molecular docking, Quantitative Structure-Activity Relationship (QSAR) modeling and pharmacophore modeling allow researchers to rapidly screen and optimize potential drug candidates (Kitchen et al., 2004; Sliwoski et al., 2014). These computational methods not only expedite the drug discovery process but also improve the accuracy of predicting a compound’s drug-like properties and potential therapeutic efficacy. Phytochemicals such as chebulagic acid, chebulinic acid, gallic acid, ellagic acid and quercetin have been identified in Terminalia chebula. These compounds have demonstrated various biological activities, including anti-inflammatory, antioxidant and anticancer effects, making them potential candidates for drug development (Singh et al., 2020). However, their viability as drug candidates must be assessed through the evaluation of their drug-like properties using computational methods. Several computational tools and databases are available for predicting the drug-like properties of phytochemicals. SwissADME, Molinspiration and PreADMET are commonly used to predict properties such as molecular weight, logP, Topological Polar Surface Area (TPSA) and bioavailability (Daina et al., 2017). Molecular docking studies can predict the binding affinity of phytochemicals to specific target proteins, while QSAR modeling can estimate the biological activity of these compounds based on their chemical structure (Cherkasov et al., 2014).

Recent research has focused on the in silico analysis of Terminalia chebula phytochemicals to predict their drug-like properties and therapeutic potential. For instance, molecular docking studies have shown that chebulagic acid has a strong binding affinity toward cancer-related proteins, suggesting its potential as an anticancer agent (Kumar et al., 2021). Similarly, QSAR studies have been utilized to predict the antioxidant and anti-inflammatory activities of various Terminalia chebula phytochemicals (Patil et al., 2019). While computational methods offer significant advantages in predicting drug-like properties, they also have limitations. The accuracy of these predictions is highly dependent on the quality of the input data and the computational algorithms used. Additionally, in silico predictions need to be validated through in vitro and in vivo studies to confirm their relevance and applicability (Ekins et al., 2007). Future research should focus on integrating computational predictions with experimental validation to advance the drug discovery process for Terminalia chebula phytochemicals. The application of computational methods to predict the drug-like properties of phytochemicals from Terminalia chebula holds great potential for drug discovery. These methods allow for the rapid screening and optimization of potential drug candidates, offering valuable insights into their therapeutic potential. However, further research is necessary to validate these predictions and translate them into effective therapeutic agents.

In this study, we employ advanced computational techniques to predict the drug-like properties of selected phytochemicals derived from Terminalia chebula. This approach provides valuable insights into the potential of these compounds as drug candidates, highlighting their suitability for further development and optimization. The integration of computational methods in evaluating Terminalia chebula phytochemicals underscores the growing importance of in silico tools in modern drug discovery. By bridging traditional knowledge with cutting-edge technology, this research seeks to identify promising drug-like candidates from Terminalia chebula, paving the way for new therapeutic developments and contributing to the field of natural product-based drug discovery.

MATERIALS AND METHODS

Software’s and Servers used

Pubchem database is used for collection of canonical smiles and downloading SDF files. Molsoft software is used for determination of drug likeness (Kim et al., 2016; Totrov and Abagyan, 2008).

List of Phytochemicals

The list of selected phytochemicals included in the study are, 1, 16-Hexadecanediol, 1, 19-Eicosadiene, 1, 2-Benzenedicarboxylic acid, 1,2,3,6-Tetrakis-o-galloyl-beta-D-glucose, 10-Nonadecanone, 1-Decanol, 1H-Indene, 1-Octanol, 1-Tricosene, 2-Alpha-Hydroxyursolic acid, 2-Undecanone, 3, 4-Dimethoxy quercetin [5, 7-Dihydroxy-2-(3-hydroxy-4- met hoxyphenyl)-3-Methoxy-4H-chromen-4-one] 3’-Methoxy quercetin 4-o-methylgallic acid, 8-Pentadecanone, 9, 12, 15-Octadecatrienoic acid 9-Eicosene, 9-Heptadecanone, 9-Octadecene, 9-Octadecenoic acid ethyl ester, 9-Tricosene, Acetic acid, Alpha-Phellandrene, Arachidic acid [icosanoic acid], Arjunetin, Arjungenin, Arjunglucoside I, Arjunglucoside II, Arjunic acid, Arjunolic acid, Ascorbic acid, Behenic acid [Docosanoic acid], Bellericoside, Beta-caryophyllene, Beta-Sitosterol, Caffeic acids, Casuarinin, Chebulagic acid, Chebulanin, Chebulinic acid, Chebuloside II, Corilagin Cyclododecane, Cyclooctacosane, Cylohexane, Daucosterol, Eicosyl trifluoroacetate, Ellagic acid, Ethanedioic acid, Ethyl gallate, Eugenol, Ferulic acid, Gallic acid, Heptacosanoic acid, Heptafluorobutyric acid, Heptylcyclohexane, Hexacosanoic acid, Hexacosyl pentafluoropropionate, Hexadecane, Ibogamin-9(17H)-ol [(9α)-12-Methoxy-16, 17-didehydro-9, 17-dihydroibogamin9-ol], Isoquercetin, Kaempferol-3-rutinoside, Linoleic acid, Linoleic acid ethyl ester, Luteolin, Maslinic Acid, Melilotic acid, Methyl gallate, Octacosanoic acid, Octatriacontyl pentafluoropropionate, Oleic acid, Oxirane, Palmitic acid, p-Coumaric acid, Pelargonidin, Pentatriacontane, Phloroglucinol, Phthalic acid, Punicalagin, Punicalin, Pyrogallol, Quercetin, Ricinoleic acid, Rutin, Shikimic acid, Squalene, Stearic acid, Sulfurous acid, Terchebin, Terchebulin, Terflavin A, Terflavin B, Terminolic acid, Terpinen-4-ol, Terpinolene, Tetracosanoate, Tetracosanoic acid [Lignoceric acid], Tetracosyl heptafluorobutyrate, Tetradecane, Tetratetracontane, Tetratriacontane, Tetratriacontyl heptafluorobutyrate, Tetratriacontyl pentafluoropropionate, Triacontane, Triacontanoic acid, Tricosyl pentafluoropropionate, Tritetracontane, Vanillic acid and Vitamin E (Saleem et al., 2002; Sabu and Kuttan, 2002; Bag et al., 2013; Wang et al., 2015).

Collection of Canonical SMILES of selected Phytochemicals from PubChem Database

In order to collect canonical SMILES for selected phytochemicals, we have accessed the PubChem website and searched for each phytochemical by entering its name in the search bar. After locating the compound in the search results, we clicked on it to open its compound summary page on that page, we scrolled down to the "Chemical and Physical Properties" section and found the "Canonical SMILES" field, usually located under "Molecular Descriptors." Then we copied the canonical SMILES string and repeated this process for each phytochemical.

Chemical structures of selected phytochemicals

In order to draw the chemicals structures of selected phytochemicals in ChemDraw using a canonical SMILES string, we have accessed the "Structure" menu in ChemDraw. From there, we have selected "Convert SMILES to Structure" and then inserted canonical SMILES string into the input box that appears and click "OK". ChemDraw automatically generated structure then adjusted using ChemDraw's tools, such as the bond and atom tools. Table 1 represents the chemical structure of selected phytochemicals.

Determination of Drug Likeness Score

MolSoft web servers (https://molsoft.com/mprop/) were used to forecast the drug-like characteristics of phytocompounds. Lipinski's rule of five was used to compute drug-like qualities, which states that molecules should have a molecular weight of 500, a C logP of 5, less than 10 hydrogen bond acceptors and less than 5 hydrogen bond donors. To anticipate drug-like features of compounds, the canonical Simplified Molecular Line-Entry Systems (SMILES) were retrieved from PubChem and entered into the MolSoft online server (Hatano et al., 2005; MolSoft, 2024; Daina et al., 2017; Egan et al., 2000; Muegge et al., 2001; Patil et al., 2020; Suryawanshi et al., 2020; Suryawanshi et al., 2020; Sampat et al., 2020).

RESULTS

The concept of drug likeness is crucial in drug design, indicating how closely a substance aligns with the characteristics of a typical drug, particularly in terms of bioavailability. In this study, various parameters related to drug similarity were computed for selected phytochemicals to gain insights into their bioavailability in the human body. The study focused on determining whether these phytoconstituents possess properties that make them suitable candidates for drug development. To evaluate the drug likeness of the compounds, several physicochemical parameters were considered, including Molecular Weight (MW), Hydrogen Bond Acceptors (HBA), Hydrogen Bond Donors (HBD), partition coefficient (Log P) and adherence to Lipinski's rule of five (RO5). The permissible ranges for these parameters, indicative of good oral bioavailability, were defined as follows: MW<500 Daltons, HBA≤10, HBD≤5, Log P≤5 and RO5 violations≤1. Utilizing the MolSoft web server, the draggability of the specified medicines and phytoconstituents was assessed. The results, as presented in Table 1, indicate that the majority of the selected antibiotic medicines exhibited satisfactory levels of Hydrogen Bond Donors (HBD) and partition coefficients (Log P) within the acceptable ranges. However, some compounds did not meet the typical values for key parameters such as Molecular Weight (MW) and the number of Hydrogen Bond Acceptors (HBA). Table 2 represents the drug Likness Profile of Phytochemicals from Terminalia Chebula.

Data Analysis: Drug-Likeness Evaluation of Selected Phytochemicals

In this study, the drug-likeness of selected phytochemicals from Terminalia chebula was evaluated using key physicochemical parameters that influence bioavailability in the human body. These parameters include Molecular Weight (MW), Hydrogen Bond Acceptors (HBA), Hydrogen Bond Donors (HBD), partition coefficient (Log P) and adherence to Lipinski’s rule of five (RO5). The evaluation was conducted using the MolSoft web server to determine the suitability of these phytochemicals as drug candidates.

Molecular Weight (MW)

Molecular weight is a critical factor in drug design as it influences the ability of a compound to be absorbed, distributed, metabolized and excreted. Lower molecular weight compounds are generally more likely to be absorbed and to penetrate cell membranes. (For good oral bioavailability, MW should be less than 500 Daltons.) We observed that most selected compounds adhered to the molecular weight criterion; however, a few exceeded the 500 Daltons threshold, which may impact their bioavailability negatively.

Hydrogen Bond Acceptors (HBA)

The number of hydrogen bond acceptors in a molecule influences its solubility and permeability. A higher number of HBAs can enhance the solubility but may reduce membrane permeability. (A good drug candidate should have no more than 10 hydrogen bond acceptors). The analysis revealed that while most compounds met the HBA criterion, a subset exceeded this limit, which might impede their permeability and, consequently, their bioavailability.

Hydrogen Bond Donors (HBD)

Hydrogen bond donors are crucial for drug-receptor interactions. However, an excessive number of HBDs can reduce the ability of a compound to cross cell membranes. An ideal drug candidate should have no more than 5 hydrogen bond donors. The majority of the compounds exhibited acceptable levels of HBDs, falling within the permissible range, which suggests they have a good balance between solubility and membrane permeability.

Partition Coefficient (Log P)

Log P is the measure of a compound’s hydrophobicity and indicates its ability to pass through the lipid bilayer of cell membranes. It also affects the solubility of the compound in water versus organic solvents. A Log P value of ≤ 5 is indicative of good membrane permeability and solubility balance. Most of the selected phytochemicals showed Log P values within the desirable range, suggesting good permeability and bioavailability. However, compounds with Log P values higher than 5 may have issues with excessive hydrophobicity, potentially leading to poor solubility in aqueous environments.

Lipinski’s Rule of Five (RO5)

Lipinski’s Rule of Five provides a set of guidelines to evaluate whether a compound has properties consistent with good oral bioavailability. Compounds that violate more than one of these rules are generally considered less likely to be orally active drugs. The compound should have RO5 violations≤1. The analysis showed that most of the selected compounds adhered to Lipinski’s rules, indicating their potential as orally active drugs. However, a few phytochemicals violated more than one of these rules, suggesting that they might require modification to improve their drug-likeness.

The results of this study highlight that most of the selected phytochemicals from Terminalia chebula possess favorable drug-likeness properties based on the evaluated parameters. The majority of these compounds met the criteria for molecular weight, hydrogen bond donors and acceptors and partition coefficient, making them suitable candidates for further drug development. However, certain compounds that did not fully comply with the typical values, particularly in terms of molecular weight and the number of hydrogen bond acceptors, may require structural modification to enhance their drug-likeness. Further studies, including in vitro and in vivo testing, are recommended to validate these findings and explore the therapeutic potential of these phytochemicals. The findings underscore the importance of computational tools in the early stages of drug discovery, providing valuable insights that can guide experimental studies.

DISCUSSION

Lipinski's Rule of Five serves as a well-established framework in drug design, assessing the drug-likeness of a compound based on its physicochemical characteristics. Typically, compounds that breach more than one of these rules are viewed as less likely to be orally active drugs. The study’s analysis revealed that most of the selected phytochemicals from Terminalia chebula complied with Lipinski’s guidelines, suggesting their potential as promising drug candidates. Nonetheless, a few phytochemicals exhibited multiple violations of these rules, indicating that they may need further optimization to improve their oral bioavailability and overall drug-likeness. The outcomes of this study have important implications for the development of new drugs derived from natural products, especially phytochemicals from Terminalia chebula. Utilizing computational tools to predict drug-likeness offers an effective and economical method for identifying potential drug candidates, reducing the need for more costly experimental procedures in the initial stages. The fact that many of the evaluated phytochemicals met the drug-likeness criteria highlights their potential for further exploration as therapeutic agents.

CONCLUSION

A thorough analysis was conducted on 109 phytoconstituents extracted from Terminalia chebula to assess their drug similarity and physicochemical properties. The findings indicated that a significant majority of these phytoconstituents complied with Lipinski's rule of five, suggesting favorable drug likeness. Additionally, their physicochemical characteristics aligned within the recommended ranges, indicating optimal membrane permeability and bioavailability. These results strongly suggest that the phytoconstituents from Terminalia chebula exhibit considerable potential as therapeutic candidates. Their adherence to Lipinski's rule of five and favorable physicochemical properties highlight their suitability for further exploration and development in pharmaceutical research, emphasizing their promising prospects for effectively addressing various health conditions.