In silico ADME prediction of Phytochemicals present in Piper longum

 

Saptarshi Samajdar*

Department of Pharmaceutical Technology, Brainware University, 398,

Ramkrishnapur Road, Barasat, North 24 Parganas, Kolkata – 700125.

 

ABSTRACT:

Modern pharmacological techniques can be complemented by plants, therefore conventional medicinal plant analysis has risen globally over the years. As computer science advanced, in silico methods such as network analysis and screening were widely used to provide insight on the pharmacological underpinnings of the actions of traditional medicinal plants. In this method, network pharmacology, in silico screening, and pharmacokinetic screening can increase the number of active substances among the candidates and reveal the therapeutic plant's mode of action. The application of the insilico ADME tool SwissADME for the pharmacological and pharmacognostic profiling of Piper longum Lam is the current focus. The findings of these investigations can be used by researchers to look into in vitro and in vivo studies to uncover the pharmacological underpinnings of conventional medicinal herbs.

 

 

 

INTRODUCTION:

For centuries, humankind have possessed rich knowledge regarding usage of medicinal plants as herbal medicine. With more than half of the world still depends on the natural resources for their food shelter clothing and medicines, the knowledge regarding their optimum usage especially on the medicinal side needs to be expanded more. With diseases like cancer, inflammatory bowel diseases and diabetes increasing rampantly around the world, natural medicines can be very much useful to combat them^1,2.

 

Traditional systems of medicine recommends number of medicinal plants for treatment of different disorders, one them is Piper longum of family Piperaceae is a dioscious climber indigenous to southern parts of India as well as in western ghats mountain range. Piper longum is known to show various pharmacological activities such as antifungal, insecticidal, antimicrobial, antiamoebic, antidiabetic, antioxidant, anti-cancerous and effect on respiratory system^3,4.

 

To modernize the use of traditional medicinal plants, it is essential to understand and anticipate the pharmacological underpinnings of their therapeutic action. Given the intricate and varied phytoconstituents of these plants, ADME studies can be a useful tool for defining these activities. The goal of the current investigation was to analyse the individual ADME behaviour and interpret the results utilizing in silico ADMET screening of the bioactive chemicals found in Piper longum using the Swiss ADME website^5,6 (http://www.swissadme.ch/index.php).

 

MATERIALS AND METHODS:

Swiss ADME:

Swiss ADME software (www.swissadme.ch) of Swiss institute of bioinformatics (http://www.sib.swiss) was accessed in a web server that displays the Submission page of Swiss ADME in internet was used to estimate individual ADME behaviors of the compounds from Piper longum. The canonical SMILES of phytocompounds from Piper longum were put in Swiss ADME and the results are presented in table form⁷.

 

Structural properties:

The bioavailability radar encompasses the preliminary reports regarding bioavailability and drug likedness of the phytocompounds. The parameters under consideration are Lipophilicity, size, polarity, insaturation, insolubility and flexibility⁸.

 

Physicochemical properties:

Various physicochemical parameters like molecular weight, molecular formula, no. of heavy atoms and aromatic heavy atoms, no. of rotatable bonds, H-bond donor, H-bond acceptors and TPSA, were considered for physicochemical properties of Piper longum phytocompounds⁹.

 

Lipophilicity:

Lipophilicity is a crucial factor in drug discovery and design since it complements the one successful and informative physicochemical property in medicinal chemistry. It is experimentally shown as distribution coefficients or as partition coefficients (log P) (log D). The partition equilibrium of an un-ionized solute between water and an immiscible organic solvent is shown by the graph P. Greater lipophilicity is correlated with larger log P values. Swiss ADME offers five publicly available models, namely XLOGP3, WLOGP, MLOGP, SILICOS-IT, and iLOGP, to analyse the lipophilicity character in a molecule. XLOGP3, an atomistic approach with knowledge-based library and corrective elements¹⁰.

 

Solubility:

A compound's solubility is highly dependent on the solvent employed as well as the surrounding temperature and pressure. The range of solubility is defined as the saturation concentration at which the concentration of the solute in the solution does not rise with the addition of more solute. When the greatest dose strength of a medication dissolves in 250 mL or less of aqueous media with a pH range of 1 to 7.5, the medication is said to be very soluble¹¹.

 

The Swiss ADME includes two topological methods for predicting water solubility; the first is the use of the ESOL model (Solubility class: Log S Scale: Insoluble<-10 poorly<-6, moderately<-4 soluble<-2 very<0<highly) and the second one is adapted from Ali et al, 2012 (Solubility class: Log S Scale: Insoluble<-10 poorly<-6, moderately<-4 soluble<-2very<0<highly). SILICOS-IT produced the third Swiss ADME predictor (Solubility class: Log S Scale: Insoluble<-10 poorly<-6, moderately<-4 soluble<-2 very<0<highly) where the linear coefficient is corrected by molecular weight (R2=0.75).

 

Pharmacokinetics:

On a plot of two calculated descriptors, ALOGP versus PSA, the delineation is in a zone with favourable qualities for GI absorption. The Egan egg, which is used to evaluate the accuracy of the model for GI passive absorption and prediction for brain access by passive diffusion to finally lay the BOILED-Egg, is an elliptical region that is most inhabited by molecules that have been effectively absorbed (Brain or Intestinal  Estimated  permeation predictive model). The BOILED-Egg model provides a quick, impulsive, accurately mimic but raucous technique to forecast passive GI absorption that is useful for drug discovery and development^12,13.

 

Drug likeness:

With the use of five different rule-based filters from major pharmaceutical firms, Swiss ADME filters chemical libraries to exclude molecules with characteristics that are incompatible with a good pharmacokinetics profile in order to improve the quality of proprietary chemical collections. Molecular weight (MW) less than 500, MLOGP≤  4.15, N or O ≤ 10, NH or OH≤5, and the Lipinski filter (Pfizer) are the pioneer rules of five that describe tiny compounds based on physicochemical property profiles. All nitrogen and oxygen are rigorously regarded by Lipinski as H-bond acceptors, and all nitrogen and oxygen containing at least one hydrogen are regarded as H-bond donors. Additionally, alinine nitrogen is neither a donor nor an acceptor, and aliphatic fluorines are acceptors. According to physicochemical properties, the presence of functional groups, and substructures, the Ghose filter (Amgen) defines tiny molecules. The qualifying range for small molecules is between 20 and 70 atoms, whereas the qualifying range for large molecules is between 160 and 480 Da, WlogP between -0.4 and 5.6, and molar refractivity (MR) between 40 and 130. Veber filter (GSK filter) model classifies compounds as drug-like if they have 12 or less H-bond donors and acceptors, a TPSA of 140 or less, and fewer than 10 rotatable bonds. Reduced TPSA correlates with increased penetration rate, increased rotatable bond counts correlate with decreased permeation rate, and compounds having these qualities will have good oral bioavailability¹⁴.

 

Medicinal chemistry:

Supporting medicinal chemists in their ongoing efforts to identify new drugs is the goal of this section. PAINS (Pan Assay Interference Compounds), also known as frequent hitters or promiscuous compounds, are molecules that exhibit a strong response in assays regardless of the protein targets. In particular, such compounds are reported to be active in a variety of assays, which can be thought of as potential starting points for further exploration. In the event that such moieties are identified in the molecule being evaluated, SwissADME issues cautions. To increase the potential for lead optimization, Brenk's alternative model takes into account molecules that are smaller and less hydrophobic in addition to those that do not fall under "Lipinski's rule of 5" ¹⁵.

 

RESULTS:

Table 1: General Characteristics of Phytoconstituents of Piper longum.

 

Table 2: Lipophilicity of the Phytoconstituents of Piper longum

 

Table 3: Water solubility of the Phytoconstituents of Piper longum

 

Table 4: Pharmacokinetic Parameters of the Phytoconstituents of Piper longum

 

 

Table 5: Drug likeness of the Phytoconstituents of Piper longum

 

Table 6: Medicinal Chemistry Properties of Phytoconstituents of Piper longum Lam

 

Fig 1: Boiled Egg Model of the Phytoconstituents of Piper longum

 

DISCUSSION:

In the present study we used SwissADME online software tool which is available free for the users to evaluate the ADME properties of Piper longum respectively. The phytoconstituents of the plants were enlisted through the software includes, Piperine, Piperettine, Episesamin, Pellitorine, Piperlongumine, Brachystamide, Pipercide, Sesamin, Fargesin, and Caryophyllene. Accordingly, the phytoconstituents were analyzed for ADME properties and depicted in respected tables and figures. Further, the values can be used as monographs by researchers and scientists for development of potential semisynthetic and synthetic drugs for multifarious usage^16,17.

 

CONCLUSION:

Computer aided drug design CADD has significantly altered research and development routes in drug candidate discovery as a result of the quick expansion in biological and chemical information. In terms of implementation, time, and cost, the use of computational techniques in the drug discovery and development process is frequently praised. In these studies, a free online tool called SwissADME is offered to assess the ADME qualities of phytoconstituents found in the Piper longum plant. These details may serve as the basis for a more thorough assessment of the plant's biological and pharmacological characteristics.

 

ACKNOWLEDGEMENT:

We would like to acknowledge Brainware University for providing us with the inspiration to work on this research.

 

CONFLICT OF 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.

 

REFERENCE:

1.      Gurib-Fakim A. Medicinal plants: traditions of yesterday and drugs of tomorrow. Molecular aspects of Medicine. 2006; 1; 27(1):1-93 doi:10.1016/j.mam.2005.07.008.

2.      Jahan Y, Mahmood T, Bagga P, Kumar A, Singh K, Mujahid M. Future prospects of cough treatment, herbal medicines v/s modern drugs. Int J Pharm Sci Res. 2015; 1; 6(9):1000-9.

3.      Kumar S, Kamboj J, Sharma S. Overview for various aspects of the health benefits of Piper longum linn. fruit. Journal of acupuncture and meridian studies. 2011; 1; 4(2):134-40. doi:10.1016/S2005-2901(11)60020-4

4.      Kumar, S., Kamboj, J., & Sharma, S. (2011). Overview for various aspects of the health benefits of Piper longum linn. fruit. Journal of acupuncture and meridian studies, 4(2), 134-140. http://nopr.niscpr.res.in/handle/123456789/9429

5.      Mahanthesh MT, Ranjith D, Yaligar R, Jyothi R, Narappa G, Ravi MV. Swiss ADME prediction of phytochemicals present in Butea monosperma (Lam.) Taub. Journal of Pharmacognosy and Phytochemistry. 2020; 9(3):1799-809.

6.      Sun Y, Yang AW, Hung A, Lenon GB. Screening for a Potential Therapeutic Agent from the Herbal Formula in the 4 th Edition of the Chinese National Guidelines for the Initial-Stage Management of COVID-19 via Molecular Docking. Evidence-Based Complementary and Alternative Medicine. 2020; 24; 2020. doi:10.1155/2020/3219840

7.      Egan WJ, Merz KM, Baldwin JJ. Prediction of drug absorption using multivariate statistics. Journal of medicinal chemistry. 2000; 19; 43(21):3867-77. doi:10.1021/jm000292e

8.      Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports. 2017; 3; 7(1):42717. doi:10.1038/srep42717

9.      Papadatos G, Brown N. In silico applications of bioisosterism in contemporary medicinal chemistry practice. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2013 Jul; 3(4):339-54. doi10.1002/wcms.1148

10.   Jindal D, Rani V. In silico studies of phytoconstituents from Piper longum and Ocimum sanctum as ACE2 and TMRSS2 inhibitors: strategies to combat COVID-19. Applied Biochemistry and Biotechnology. 2022 Feb 14:1-8. doi:10.1007/s12010-022-03827-6

11.   Nutrizio M, Pataro G, Carullo D, Carpentieri S, Mazza L, Ferrari G, Chemat F, Banović M, Režek Jambrak A. High voltage electrical discharges as an alternative extraction process of phenolic and volatile compounds from wild thyme (Thymus serpyllum L.): in silico and experimental approaches for solubility assessment. Molecules. 2020 Sep 10; 25(18):4131. doi: 10.3390/molecules25184131.

12.   Di L, Artursson P, Avdeef A, Ecker GF, Faller B, Fischer H, Houston JB, Kansy M, Kerns EH, Krämer SD, Lennernäs H. Evidence-based approach to assess passive diffusion and carrier-mediated drug transport. Drug discovery today. 2012 Aug 1; 17(15-16):905-12. doi:10.1016/j.drudis.2012.03.015

13.   Lu T, Yang J, Gao X, Chen P, Du F, Sun Y, Wang F, Xu F, Shang H, Huang Y, Wang Y. Plasma and urinary tanshinol from Salvia miltiorrhiza (Danshen) can be used as pharmacokinetic markers for cardiotonic pills, a cardiovascular herbal medicine. Drug Metabolism and Disposition. 2008 Aug 1; 36(8):1578-86. doi:10.1124/dmd.108.021592.

14.   Kadam RU, Roy N. Recent trends in drug-likeness prediction: a comprehensive review of in silico methods. Indian Journal of Pharmaceutical Sciences. 2007; 69(5):609. doi: 10.4103/0250-474X.38464.

15.   Barlow DJ, Buriani A, Ehrman T, Bosisio E, Eberini I, Hylands PJ. In-silico studies in Chinese herbal medicines’ research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date. Journal of ethnopharmacology. 2012 Apr 10; 140(3):526-34. doi:10.1016/j.jep.2012.01.041.

16.   Lakhera S, Devlal K, Ghosh A, Rana M. In silico investigation of phytoconstituents of medicinal herb ‘Piper longum’against SARS-CoV-2 by molecular docking and molecular dynamics analysis. Results in chemistry. 2021 Jan 1; 3:100199. doi:10.1016/j.rechem.2021.100199

17.   Nahak G, Sahu RK. Phytochemical evaluation and antioxidant activity of Piper cubeba and Piper nigrum. Journal of Applied Pharmaceutical Science. 2011; 30:153-7.

 

Received on 20.02.2023           Modified on 08.03.2023

Accepted on 18.03.2023   ©Asian Pharma Press All Right Reserved