ADME Analysis of Phytochemical Constituents of Psidium guajava

 

Akshay R. Yadav*, Shrinivas K. Mohite

Department of Pharmaceutical Chemistry, Rajarambapu College of Pharmacy, Kasegaon,

Sangli, Maharashtra, India-415404.

 

ABSTRACT:

Absorption, distribution, metabolism, excretion and toxicity (ADME-Tox) properties should be considered to develop a new drug, because they are the main cause of failures for candidate molecules in drug design. The early evaluation of these properties during drug design could save time and money. Over the past 5 decades ADME played a major role in drug design process. Psidium guajava under study were analyzed for ADME properties using Swissadme servers. ADME profiles were evaluated and most of the molecules were found to be suitable for further studies. In-silico ADMET analysis is proved to be a good tool in drug discovery.

 

 

 

INTRODUCTION:

ADME means absorption, distribution, metabolism and excretion which explain about the pharmacokinetics aspects of a drug molecule. There are several incidents reporting the attrition of drug discovery projects just because of the poor ADME profiles¹. Therefore prior to synthesis and in-vivo studies, ADME profiling found to be more effective. Determination of ADME properties of compounds involves lot of experimental procedures to be followed which is time consuming and expensive. Therefore Insilco ADME models have been developed. The ADME study was carried out using SWISS ADME predictor². This is a free web tool to evaluate the pharmacokinetics, drug likeness and medicinal chemistry friendliness small molecules. As mentioned earlier, the attention was given to design the molecules which fit into the rule of drug likeness³.

 

The properties like molecular weight less than 500 g/mol, less than 5 numbers of hydrogen bond donors, less than 10 numbers of hydrogen bond acceptors and less than 10 rotatable bonds were chosen as criteria, while the selection of molecules to be synthesized. The search engine further gave a compiled result on lipophilicity and hydrophilicity of these molecules by integrating results obtained from various Log P and S prediction programs called ILOGP, XLOGP3, WLOGP, ESOL, and SILICOS-IT. Log P, a measure of lipophilicity of the molecule is the logarithm of the ratio of the concentration of drug substance between two solvents in an unionized form⁴. Lipinski rule prescribes an upper limit of 5 for druggable compounds. The lower the log P values the stronger the lipophilicity for the chemical substance. The aqueous solubility of a compound significantly affects its absorption and distribution characteristics. On the other side, low water solubility goes along with a bad absorption, and therefore, the general aim is to avoid poorly soluble compounds. Log S is a unit of expressing solubility in itself, which is the 10-based logarithm of the solubility measured in mol/L. Distribution of Log S in traded drugs reveals a value somewhere between -1 to -4, will be optimum for better absorption and distribution of drugs in the body⁵.

 

Calculation of ADME properties:

Structure were drawn in Chemsketch and SMILES of each compound was translated into molfile by online SMILES translator and structure file generator found in online tool SwissADME. In addition, pharmacokinetics such as gastrointestinal absorption, Skin permeability, Blood brain barrier and drug-likeness prediction such as bioavailability score.

 

Blood brain barrier (BBB):

BBB penetration is a parameter used to know whether the compound crosses blood brain barrier. Usually the most of the drugs must not pass the blood brain barriers if the target is not related to the nervous system⁶.

 

Skin Permeability:

Skin permeability of the compound is one of the important factor with reference to adverse drug response in case drugs taken orally to identify in case of accidental contact with skin and the skin permeability in case of the drugs to be taken transdermally where the skin penetration is an important aspect.

 

Skin permeability of a compound the result value is given as logKp. Kp [cm/hour] is defined as Kp= Km*D/h

 

Where, Km is distribution coefficient between stratum corneum and vehicle

 

and D is average diffusion coefficient [cm²/h], and h is thickness of skin [cm].

 

Permeability glycoprotein (P-gp):

The knowledge about compounds being substrate or non-substrate of the permeability glycoprotein. It suggests about the most important member among ABC-transporters which is key to appraise active efflux through biological membranes, for instance from the gastrointestinal wall to the lumen or from the brain. One important role of P-gp is to protect the central nervous system from xenobiotics. P-gp is overexpressed in some tumour cells and leads to multidrug-resistant cancers⁷.

 

Drug-likeness:

Drug-likeness means to assesses qualitatively the chance for a molecule to become an oral drug with respect to bioavailability. Drug-likeness generated from structural or physicochemical inspections of development compounds advanced enough to be considered oral drug-candidates. This notion is routinely employed to perform filtering of chemical libraries to exclude molecules with properties most probably incompatible with an acceptable pharmacokinetics profile⁸. This SwissADME section gives access to 5 different rule-based filters, with diverse ranges of properties inside of which the molecule is defined as drug-like. These filters often generated from analyses by pharmaceutical companies aiming for improving the quality of their proprietary chemical collections. The Lipinski (Pfizer) filter is the pioneer rule-of-five implemented from the Ghose (Amgen), Veber (GSK), Egan (Pharmacia) and Muegge (Bayer) methods⁹.

 

Water Solubility:

Having a soluble molecule is one of the greatly facilitates in many drug development activities, primarily the ease of handling and formulation. For discovery projects targeting oral administration, solubility is important property influences absorption and those drug meant for parenteral usage has to be highly soluble in water to deliver a sufficient quantity of active ingredient in the small volume of such pharmaceutical dosage. Two topological methods to predict Water Solubility are included in SwissADME. The first one is an implementation of the ESOL model and the second one is adapted from Ali et al. Both differ from the seminal general solubility equation since they avoid the melting point parameter then latter being challenging for prediction. They demonstrate strong linear correlation between predicted and experimental values (R2=0.69 and 0.81, respectively). SwissADME third predictor for solubility was developed by SILICOS-IT. The linear correlation coefficient of this fragmental method corrected by molecular weight is R2 = 0.75. All predicted values are the decimal logarithm of the molar solubility in water (log S). SwissADME also provides solubility in mol/l and mg/ml along with solubility classes^10-11.

 

Table 1: Phytochemical constituents of Psidium guajava:

Sr. No	Phytochemical constituents of Psidium guajava
1	Oleanolic acid
2	Lyxopyranoside
3	Arabopyranoside
4	Guaijavarin
5	Quercetin

 

RESULTS AND DISCUSSION:

The pharmacokinetic properties and drug-likeness prediction of Phytochemical constituents of Psidium guajava were performed by SwissADME online version and the data are shown in table 2 and water solubility prediction shown in table 3. According to the pharmacokinetic properties, all test compounds showed Moderately soluble and soluble gastrointestinal absorption also show no BBB permeability however drug likeness were predicted by bioavailability score.

 

 

Table 2: Pharmacokinetics and drug-likeness prediction of Phytochemical constituents of Psidium guajava:

Sr. No.	Phytochemical constituents	Pharmacokinetics			Drug-likeness
		GI absorption	BBB permeability	Log Kp (skin permeation) cm/s	Bioavailability Score
1	Oleanolic acid	Moderately Soluble	No	-2.21	0.22
2	Lyxopyranoside	Moderately Soluble	No	-3.34	0.17
3	Arabopyranoside	Soluble	No	-3.55	0.55
4	Guaijavarin	Soluble	No	-1.43	0.11
5	Quercetin	Moderately soluble	No	-3.16	0.55

 

Table 3: Water solubility prediction prediction for Phytochemical constituents of Psidium guajava

Sr. No.	Phytochemical constituents	LogP	Water Solubility
		(Consensus LogP)	LogS (ESOL)	LogS (Ali)	LogS (SILICOS-IT)
1	Oleanolic acid	2.73	Soluble	Moderately Soluble	Soluble
2	Lyxopyranoside	2.24	Soluble	Moderately Soluble	Moderately Soluble
3	Arabopyranoside	2.77	Soluble	Moderately Soluble	Moderately Soluble
4	Guaijavarin	2.58	Moderately Soluble	Soluble	Poorly soluble
5	Quercetin	2.35	Soluble	Soluble	Moderately Soluble

 

 

CONCLUSION:

All Phytochemical constituents of Psidium guajava demonstrated a significant druglikeness based on Lipinski’s rule-of-five (RO5) and predicted to be BBB non-permeant (blood–brain barrier), it means no expected neurological side effects. It was demonstrated significant bioavailability, suggesting that the molecules could be absorbed and delivered throughout the body in case of use as drug. Thus, all molecules were screened for their ADMET prediction and the Phytochemical constituents were confirmed to be suitable drug-like molecules.

 

ACKNOWLEDGEMENT:

I express my sincere thanks to Vice-principal Prof. Dr. S. K. Mohite for providing me all necessary facilities and valuable guidance extended to me.

 

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Received on 02.06.2020                    Modified on 26.06.2020

Accepted on 16.07.2020                   ©AJRC All right reserved