PREPARATION OF TWO NEW DERIVATIVES OF ELLAGIC ACID AND IN-SILICO STUDIES OF THEIR DRUG LIKE PROPERTIES
HTML Full TextPREPARATION OF TWO NEW DERIVATIVES OF ELLAGIC ACID AND IN-SILICO STUDIES OF THEIR DRUG LIKE PROPERTIES
Chitra Kamath and Swarupa Salvi *
Sathaye College, Vile-Parle, Mumbai University, Mumbai, Maharashtra, India.
ABSTRACT: Till the nineteenth century, people worldwide were relying on herbal medicines for their health problems. Today, it is common knowledge that different parts of plants contain various phytochemicals. Phytochemicals exhibit variety of physiological activities and health benefits. Due to the side effects shown by synthetic medicines, there is increased interest in phytopharmaceuticals. There are many instances where natural chemicals have played important role in new drug discovery. Chemical modification of pharmacophore in naturally occurring drug has resulted into new drugs with better drug properties. Ellagic acid is a phytochemical belonging to polyphenol class. It shows many biological and medicinal activities including antioxidant, anti-inflammatory, anti-cancer, neuroprotective, anti-diabetic. Hepatoprotective, cardioprotective, skin protective effects. It contains lactone rings, aromatic rings in form of biphenyl unit and four phenolic hydroxy groups. The two new derivatives of ellagic acid are prepared. In the first one, benzoylation of all four phenolic hydroxy groupis carried out giving tetrabenzoyl ellagic acid. In the second, the lactone rings are opened by base hydrolysis forming aoctasodium salt of hydrolysed ellagic acid. The structures were confirmed by spectroscopy. In-silico study and comparison of drug like properties of ellagic acid with its two derivatives using ADMETlab showed that among the three molecules Ellagic acid and its hydroysis derivative are showing good physiochemical properties as well as medicinal properties. Among the three molecules, Hydrolysis derivative of Ellagic Acid shows the best absorption whereas Ellagic Acid shows best distribution. All the three molecules show moderate metabolism; excretion and toxicity profiles. On the basis of this analysis Ellagic Acid is having better drug like properties than its two derivatives.
Keywords: Ellagic acid, ADMETlab, Drug like, Physiochemical
INTRODUCTION: Till the end of nineteenth century, worldwide people were dependent on plant-based medicines for all type of health issues. Towards the end of nineteenth century, synthetic medicines came into the market. But soon people turned to herbal medicines as synthetic medicines showed many serious side effects.
In recent times a new concept of phytopharma-ceuticals has become popular. These are drugs containing active component which is plant based or isolated from plant. Caffeine, Digitonin, Quinine are the examples of phytopharmaceuticals 1.
Many phytochemicals isolated from plants show biological and pharmaceutical activities. E.g. 1) morphine which is an alkaloid is obtained from poppy seeds (Papaver somniferum) is used as, 2) Streptomyces avermitilis produces a macrocyclic lactone, avermectin which is used as a antiparasitic drug, 3) quinine is an alkaloid found in cinchona plant species and 4) artemisinin obtained from Artemisia annua, are used as antimalarials, 5) lovastatin, a lactone obtained from Aspergillus terreus that is useful in controlling lipids, 6) cyclosporine, a cyclic peptide and 7) rapamycin, a protein both obtained from fungi and are useful immunosuppressants especially in organ transplantation, 8) paclitaxel obtained from the bark of Taxus brevifolia and 9) irinotecan obtained from Camptotheca acuminata are useful as anticancer drugs 2, 3.
The therapeutic actions of drug molecules are due to a perticular structural unit present in the molecule known as pharmacophore. This can be established through structure activity relationship studies. One important strategy in developing new drugs is by making small structural changes in the pharmacohores of existing drug molecules 4, 5. Such drugs are also known as designer drugs.
They are known as the structural analogs of the original drug. The original drug is named as precursor. The structural analogs may have similar or different pharmacological activity as the precursor.
If the structural analogs are having similar pharmacological activity as the precursor, then they are also known as the pharmacological or functional analogs. Thus, Heroin which is diacetyl morphine, is the structural and functional analog of morphine, whereas Fentanyl is only functional analog of morphine 6.
Heroine and morphine are analgesic drugs, but heroine is found to be more potent than morphine. Thus, structural analog is more potent than the precursor 7.
Another such example is alkaloid thebaine. Nalbuphine is a structural analog of thebaine obtained by adding a hydroxy group at C-14 and adding a cyclobutyl ring on the methyl group of tertiary amino group. Nalbuphine is anlgesic drug which is safer for use in children 8.
Quinine is an alkaloid present in the bark of cinchona species. It is an antimalarial drug which is precursor for the new antimalarial strucural homologs, Chloroquin. which is more potent than quinine 9. Guanidine present in Galega officinalis shows blood sugar lowering action. But since it is very toxic to human beings it cannot be used in treatment of diabetes. Its structural analog dimethyl biguanide popular as metformin is a safe anti-hyperglycemic drug 10.
Hundreds of such examples are availble where the structural analogs are more potent and safer drug than the precursor. Ellagic acid is a polyphenol present in many plants either in free form or as a part of complex molecule that liberates ellagic acid. It shows many biological and medicinal activities including antioxidant, anti-inflammatory, anti-cancer, neuroprotective, anti-diabetic. hepatoprotective, cardioprotective, skin protective effects 11, 12, 13. It is present in variety of plant parts especially fruits like berries, pomegranate etc. 14.
But it has very low solubility in water and low bioavailability. Even after increasing the intake for increasing the bioavailability, its human plasma concentration found to be very low (100 nM) 15. So, an attempt is made to prepere a structural analog of ellagic acid for better drug properties. From the structure of ellagic acid, it can be observed that it is containing two lactone rings, a diphenyl nucleus and four phenolic OH groups. The O-alkylation and O-acylation of phenolic OH is easier due to presence of electron withdrawing lactone rings.
So, such O-alkylated and O-acylated structural analogs of ellagic acid have been prepared by some researchers. Heur et al., synthesised 11 derivatives of ellagic acid as shown in Table 1 16.
TABLE 1: ELLAGIC ACID ANALOGS SYNTHESISED BY HEUR ET AL.,
Name of the derivative | R1 | R2 |
Ellagic acid | OH | OH |
tetrahexanoyl ellagic acid | hexanoyloxy | hexanoyloxy |
3,3 -dihexanoyloxydiphenic-2,2’,6,6 -dilactone | hexanoyloxy | H |
4,4’- dihexanoyloxydiphenic-2,2’,6,6’-dilactone | H | hexanoyloxy |
3,3’-di-p-D-glucopyranosylellagic acid diacetate | -0-tetra-0-acetyl-p-D-glucopyranosyl | Oac |
3,3’-diacetylellagic acid | Oac | OH |
3,3’-di-n-octyl-4,4’-dihexanoylellagic acid | -0-n-octyl | hexanoyloxy |
4,4’-dihexanoylellagic acid | OH | hexanoyloxy |
3,3’-dimethylellagic acid | OCH3 | OH |
3,3’-dibenzylellagic acid | -0-Benzyl | OH |
4,4’-dimethylellagic acid | OH | OCH3 |
In another study, 3,3′-di-O-benzylellagic acid was synthesised 17.
Many research groups have isolated ellagic acid derivatives from different plants like Dipentodonsinicus 18, Anisophylleadichostyla 19, Combretum yunnanensis 17 and many more 20, 21, 22.
But very few attempts have been made to synthesise derivatives of ellagic acid. Here two new derivatives of ellagic acid are prepared by using simple methods. After purification, their structures were confirmed by IR, H1-NMR and C13 NMR spectras.
One of the important parts of new drug discovery is to determine drug likeliness and toxicity of the new molecule. Drug likeliness means the drug like properties which include structural, biochemical, physiochemical, pharmacokinetic studies. The results obtained by these studies decide whether the new molecule is likely to be good drug candidate or not. If the molecule shows some good drug properties but lack certain other drug like properties, then such properties can be optimised by making necessary structural changes. The drug likeliness involves many structural parameters study like molecular weight, hydrogen bonding possibilities, lipophilicity, topological polar surface area. The physiochemical parameters include solubility, permeability, enzyme and chemical stability. The biochemical parameters involved are metabolic stability, transportation, blood brain barrier, plasma stability. The safety and toxicity studies include drug-drug interactions, mutagen city, cytotoxicity, teratogenicity. Drug distribution, half-life, bioavailability are some parameters studied in pharmacokinetics 23. The traditional methods of determining drug likeliness are time consuming and ineffective as only one property canbe determined at a time 24. Hence, in-silico prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) has gained importance as it saves time required for determining pharmacokinetic properties at the early stage of drug discovery. Many softwares are available for prediction of ADMET of a drug which work on the basis of their database library. So, the database of a software plays very important role in accurate predictions of ADMET of a molecule 25. ADMETlab is a software which has a database comprised of more than 2,88,000 entries. This web-based platform uses four function modules by which performing six types of drug-likeness analysis and predicting 31ADMET endpoints is easily possible.
The six types of drug likeliness analysis include five rules and one prediction model. 31 endpoints include three basic properties, six absorption properties, three distribution properties, ten metabolism properties, two elimination properties and seven toxicity properties. (26) Many researchers have used ADMETlab for predicting preliminary drug likeliness of their research molecules. In one of the studies the ADMET of Salubrinal and its structural analogues which were containing a cinnamic acid residue or a quinoline ring was predicted using SwissADME, ADMETlab, admetSAR 2.0 27. In another study, the physicochemical and pharmacokinetic properties of the novel active drugs, pioglitazone and rosiglitazone was carried out by using ADMETlab 2.0 software 28. Many other research groups have used ADMETlab in their study for predicting drug likeliness of their research molecules 29, 30, 31. So, in the present study the drug likeliness of the ellagic acid derivatives is predicted using ADMETlab software.
MATERIAL AND METHODS:
Tetrabenzoyl Derivative of Ellagic Acid (BEA): As the O-acylation of ellagic acid is easy due to presence of lactone ring, the first derivative of ellagic acid prepared is 3, 3’, 4, 4’-tetrabenzoyl derivative of ellagic acid. Benzoylation reaction was not tried earlier by any researcher. The procedure followed for the synthesis is Vogel's Textbook of Practical Organic Chemistry.
Procedure: 1g of Ellagic acid was dissolved in 40cm 3 of 20% NaOH solution in a 250ml beaker. After shaking it for 1 hour on magnetic stirrer, the solution was filtered. The filtrate was collected in a 250ml conical flask. Then 4ml of benzoyl chloride was added to the conical flask.
The flask was corked and content was shaken vigorously for 30 minutes. The content of the flask was poured in cold water. The product was filtered and washed with water. The reaction and the structure of the product obtained are shown below.
Octasodium Salt of Hydrolysis Derivative of Ellagic Acid (HEA): As the ellagic acid contains two 6-membered lactone rings, it can be hydrolysed when the lactone ring will open forming hydroxy carboxylic acid. Since, the hydroxy group and carboxylic acid groups, in the hydrolysed product, are present in closed proximity they immediately undergo reverse lactonization. Hence, it is difficult to open the lactone ring. The base catalysed hydrolysis of ellagic acid using sodium methoxide gives sodium salt of hydrolysed ellagic acid. This is the second derivative of ellagic acid prepared. The name of this derivative is octasodium salt of 4, 4’.5, 5’, 6, 6’-hexahydroxy-2,2’-diphenyl dicarboxylic acid. This reaction was not tried earlier by any researcher.
Procedure: The procedure followed for the synthesis is as given in Vogel's Textbook of Practical Organic Chemistry 2g of dry sodium metal was refluxed with methanol in a dry round bottom flask, until sodium dissolves completely to get clear solution of sodium methoxide. This was filtered to remove insoluble impurities. To the filtrate 1g of ellagic acid was added pinch by pinch with constant stirring using magnetic stirrer. The product obtained was filtered and washed with methanol, this removes excess of sodium methoxide. It was then washed with DMSO to remove any unreacted ellagic acid. It was again washed with methanol and dried in the air.
In-silico Drug Likeliness and Toxicity Studies of Ellagic Acid and its Derivatives: The physicochemical properties, drug likeliness and medicinal properties, absorption, distribution, metabolism, excretion and toxicity predictions of Ellagic acid (EA), O, O, O.O-tetrabenzoyl ellagic acid (BEA) and octasodium salt of hydrolysed ellagic acid (HEA) were carried out using ADMETlab2.0.
RESULTS AND DISCUSSION:
BEA: The weight of the crude product is 1.8g. The compound is pinkish white in colour. The TLC was done by using hexane and ethyl acetate in ratio 2:8. The TLC plate is shown in Fig. 1.
FIG. 1: TLC PLATE FOR PREPARATION OF TETRABENZOYL ELLAGIC ACID
The column was prepared by using silica gel in hexane. The column was loaded with the compound dissolved in hexane. Then it was eluted by using ethyl acetate and hexane mixture, slowly increasing the ethyl acetate volume till the fraction rich in benzoyl ellagic acid was obtained. The column chromatography purification and the purified compound are shown below in Fig. 2.
The melting point is 180°C and it is pinkish white in colour.
FIG. 2: COLUMN CHROMATOGRAPHY FOR PURIFICATION AND PURIFIED TETRABENZOYL ELLAGIC ACID
The purity of the product was also checked by Gas chromatography. The gas chromatogram was recorded at Shraddha analytical lab, Mumbai. An7820A GC system (Agilent Technologies, USA) equipped with a flame ionization detector (FID) was used for qualitative determination of BEA.
The GC settings were as follows: Agilent 19091J-413 column (30 m x 320 μm x 0.25 μm) and ultrapure nitrogen (> 99.99%) as the carrier gas at a column flow rate of 1.1111 mL min−1. Split mode of injection was employed and operated at 250 °C. The temperature program was 60 °C to 325 °C. The detector temperature was set at 280 °C. The chromatogram obtained is shown in Fig. 3.
FIG. 3: GAS CHROMATOGRAM OF PURIFIED BEA
Since, only one peak was obtained the sample was concluded to be pure.
Characterization of BEA: The structure of the benzoyl derivative of ellagic acid was confirmed be recording the IR spectrum, H1- NMR spectrum and C13-NMR spectrum.
IR Spectrum: It was recorded in Central Research Facility at Sathaye College, Mumbai. The FTIR spectrophotometer of Perkin Elmer and Spectrum 2 model (Serial number-120985) was used. Mid IR region with wavelength 4000 to 450 cm-1 was employed. The source of IR light was solid state silicon carbide laser whereas lithium tantanate detector was employed in the instrument. The spectrum was recorded in ATR diamond crystal mode. The software spectrum 10 is used. Few milligrams of pure sample was directly put on the diamond crystal previously cleaned with acetone. Then the probe was lowered till the pressure gauge was sufficient. Then the spectrum was recorded. The IR spectra of ellagic acid and BEA are shown in Fig. 4 and 5 respectively.
FIG. 4: IR SPECTRUM OF PURE ELLAGIC ACID
FIG. 5: IR SPECTRUM OF PURE BEA
The peaks in IR spectrum are explained in Table 2 below.
TABLE 2: IR FREQUENCY CORRELATION TABLE FOR BEA
IR frequency in cm-1 | Description |
3074 | Aromatic C-H stretching |
3063 | Aromatic C-H stretching |
1958 | Weak overtones of benzene |
1912 | Weak overtones of benzene |
1756 | Saturated 6 membered lactone |
1739 | Esters of aromatic acids |
1614 | Aromatic C-C stretching |
1599 | Aromatic C-C stretching |
1491 | Aromatic C-C stretching |
1251 | ester, C-O stretching |
1096 | ester C-O stretching |
834 | polysubstituted benzene |
817 | polysubstituted benzene |
792 | monosubstituted benzene |
767 | monosubstituted benzene |
757 | polysubstituted benzene |
692 | polysubstituted benzene |
690 | polysubstituted benzene |
H1 NMR Spectrum: This was recorded at TIFR, Mumbai. The 600MHz NMR instrument by Agilent Technologies, USA, Direct Drive Console was used for recording this spectrum.
FIG. 6: H1 NMR SPECTRUM OF TETRABENZOYL ELLAGIC ACID
The magnetic field of 14.1 Tesla, with standard bore (54mm) was applied. Computer was Dell T3500 precision with Intel dual core CPU was used. 5mm 1H/13C/15N TXI probe with 30G/cm gradient along Z axis was used. CryoQ–probe (S/N ratio ~ 3000:1) with closed cycle cryogenic system was employed. CDCl3 was used as the solvent.
TABLE 3: H1 NMR SPECTRUM CHEMICAL SHIFT CORRELATION TABLE FOR BEA
δ in ppm | Area | Explanation |
7.460 | 4.13 Corresponds to 8 protons | Due to four H3 protons |
7.472 | Due to four H6 protons | |
7.606 | 3.96 Corresponds to 8 protons | Due to four H3 protons |
7.615 | Due to four H5 protons | |
8.151 | 2.021 Corresponds to 4 protons | Due to two H2 protons |
8.163 | Due to two H7 protons | |
8.300 | 1.00 Corresponds to 2 protons | Due to two H1 protons |
C13 NMR Spectrum: This was recorded at TIFR, Mumbai. The 800MHz NMR instrument by BrükerBiospin, Switzerland, Avance AV 800 was used for recording this spectrum. The magnetic field of 18.89 Tesla, 20K pumped magnet, Standard bore (51mm) was applied. Computer was HP make HPXW4200 workstation, PIV 3.0GHz 120Gb hard disk, 1Gb RAM, DVD-RW, 20inch LCD monitor was used. 5mm 1H/13C/15N triple resonance probe fitted with gradient coil along Z axis and automated tuning / matching ATM unit was used. CHCl3 was used as the solvent.
FIG. 7: C13 NMR SPECTRUM OF TETRABENZOYL ELLAGIC ACID
TABLE 4: C13 NMR SPECTRUM CHEMICAL SHIFT CORRELATION FOR TETRABENZOYL ELLAGIC ACID
δ in ppm | Type of C | Explanation |
116.403 | Aromatic ring C | Due to C3, C3’ |
117.552 | Due to C2, C2’ | |
120.833 | Due to C1, C1’ | |
128.852 | Due to C9, C11, C17, C15, C9’, C11’, C17’, C15’ | |
130.368 | Due to C7, C13, C7’, C13’ | |
130.477 | Due to C8, C12, C14, C18, C8’, C12’, C14’, C18’ | |
134.106 | Due to C10, C16, C10’, C16’ | |
142.655 | Due to C6, C6’ | |
145.102 | Due to C4, C4’ | |
146.328 | Due to C5, C5’ | |
158.672 | C=O in lactone | Due to C19, C19’ |
165.323 | C=O in ester | Due to C20, C21, C20’, C21’ |
HEA: The melting point is 257 to 265°C and it is dark chocolate brown in colour as shown in Fig. 8. It is easily soluble in water and practically insoluble in all other solvents. The weight of the product obtained is 0.684g.
FIG. 8: HYDROLYSIS DERIVATIVE OF ELLAGIC ACID
Characterization of Octasodium Salt of Hydrolysis Derivative of Ellagic Acid (HEA): The structure of the benzoyl derivative of ellagic acid was confirmed be recording the IR spectrum, H1- NMR spectrum and C13-NMR spectrum.
IR spectrum: It was recorded in Central Research Facility at Sathaye College, Mumbai. The FTIR spectrophotometer of Perkin Elmer and Spectrum 2 model (Serial number-120985) was used. Mid IR region with wavelength 4000 to 450 cm-1 was employed. The source of IR light was solid state silicon carbide laser whereas lithium tantanate detector was employed in the instrument. The spectrum was recorded in ATR diamond crystal mode. The software spectrum 10 is used. Few milligrams of pure sample was directly put on the diamond crystal previously cleaned with acetone. Then the probe was lowered till the pressure gauge was sufficient. Then the spectrum was recorded.
FIG. 9: IR SPECTRUM OF HEA
The IR spectra of octasodium salt of hydrolysed ellagic acid and tetrabenzoyl ellagic acid are shown in Fig. 9.
TABLE 5: IR FREQUENCY CORRELATION TABLE FOR HEA
IR frequency in cm-1 | Description |
3008 | Due to aromatic C-H stretching |
1922 | weak overtone of benzene |
1820 | weak overtone of benzene |
1695 | Aromatic carboxylic acid C=O stretching |
1564 | Aromatic C-C stretching |
1454 | Aromatic C-C stretching |
1372 | phenolic C-O stretching |
1325 | phenolic C-O stretching |
1168 | phenolic C-O stretching |
1099 | C-O stretching in acid |
911 | Polysubstituted benzene |
813 | Polysubstituted benzene |
774 | Polysubstituted benzene |
742 | Polysubstituted benzene |
H1 NMR Spectrum: This was recorded at TIFR, Mumbai. The 600MHz NMR instrument by Agilent Technologies, USA, Direct Drive Console Was used for recording this spectrum.
The magnetic field of 14.1 Tesla, with standard bore (54mm) was applied. Computer was Dell T3500 precision with Intel dual core CPU was used. 5mm 1H/13C/15N TXI probe with 30G/cm gradient along Z axis was used.
CryoQ–probe (S/N ratio ~ 3000:1) with closed cycle cryogenic system was employed. D2O was used as the solvent and TSPA which is 3-(Trimethylsilyl) propionic-2,2,3,3-d4 acid d, containing nine equivalent protons as standard.
The H1 NMR spectrum of HEA is shown in Fig. 10.
FIG. 10: H1 NMR SPECTRUM OF HEA
As it can be seen from above structure that HEA contains only two protons at position 3 and 3’. Both these protons are equivalent. Hence, only one peak is observed at δ= 7.29, due to these two aromatic protons.
C13 NMR Spectrum: This was recorded at TIFR, Mumbai. The 800MHz NMR instrument by BrükerBiospin, Switzerland, Avance AV 800 was used for recording this spectrum. The magnetic field of 18.89 Tesla, 20K pumped magnet, Standard bore (51mm) was applied. Computer was HP make HPXW4200 workstation, PIV 3.0GHz 120Gb hard disk, 1Gb RAM, DVD-RW, 20inch LCD monitor was used. 5mm 1H/13C/15N triple resonance probe fitted with gradient coil along Z axis and automated tuning / matching ATM unitwas used. D2O was used as the solvent.
FIG. 11: C13 NMR SPECTRUM OF HEA
The analysis of the spectrum is given below:
TABLE 6: C13 NMR SPECTRUM CHEMICAL SHIFT CORRELATION TABLE OF HEA
δ in ppm | Type of C | Explanation |
113.801 | Aromatic ring C | Due to C3, C3’ |
117.983 | Due to C1, C1’ | |
126.903 | Due to C2, C2’ | |
156.359 | Due to C5, C5’ | |
162.829 | Due to C6, C6’ | |
164.173 | Due to C4, C4’ | |
171.082 | C=O in carboxylic acid | Due to C7, C7’ |
Results of ADMETlab Analysis for Different Parameters:
Physiochemical Properties: The physicochemical properties of include different parameters. The values of these parameters are important in predicting overall solubility and absorption of drug in gastrointestinal tract 32. The predicted value of different parameters for EA, BEA and HEA are tabulated in Table 7.
TABLE 7: PHYSIOCHEMICAL PROPERTIES PREDICTIONS FOR EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
Molecular Weight | 302.01 | 784.19 | 513.88 | Contain hydrogen atoms. Optimal:100~600 |
Volume | 265.705 | 795.173 | 634.465 | Van der Waals volume |
Density | 1.137 | 0.986 | 0.81 | Density = MW / Volume |
nHA | 8 | 11 | 10 | Number of hydrogen bond acceptors. Optimal:0~12 |
nHD | 4 | 0 | 0 | Number of hydrogen bond donors. Optimal:0~7 |
nRot | 0 | 12 | 11 | Number of rotatable bonds. Optimal:0~11 |
nRing | 4 | 9 | 2 | Number of rings. Optimal:0~6 |
MaxRing | 14 | 14 | 6 | Number of atoms in the biggest ring. Optimal:0~18 |
nHet | 8 | 11 | 18 | Number of heteroatoms. Optimal:1~15 |
fChar | 0 | 0 | 0 | Formal charge. Optimal:-4 ~4 |
nRig | 4 | 55 | 14 | Number of rigid bonds. Optimal:0~30 |
Flexibility | 0.0 | 0.218 | 0.786 | Flexibility = nRot /nRig |
Stereo centres | 0 | 0 | 0 | Optimal: ≤ 2 |
TPSA | 141.34 | 148.55 | 107.98 | Topological Polar Surface Area. Optimal:0~140 |
logS | -4.027 | -5.415 | -5.275 | Log of the aqueous solubility. Optimal: -4~0.5 log mol/L |
logP | 0.796 | 8.752 | 3.655 | Log of the octanol/water partition coefficient. Optimal: 0~3 |
logD | 0.276 | 5.263 | 1.781 | logP at physiological pH 7.4. Optimal: 1~3 |
Medicinal Chemistry: The parameters included here are important in predicting drug likeliness of the compounds using different rules for oral administration. Some of them are helpful in predicting the synthetic ease, natural product score 33. Predicted values for the medicinal chemistry parameters are tabulated in Table 8.
TABLE 8: MEDICINAL CHEMISTRY PREDICTIONS FOR EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
QED | 0.216 | 0.045 | 0.372 | A measure of drug-likeness based on the concept of desirability;
Attractive: > 0.67; unattractive: 0.49~0.67; too complex: < 0.34 |
Sascore | 2.98 | 3.327 | 5.915 | Synthetic accessibility score is designed to estimate ease of synthesis of drug-like molecules. Sascore≥ 6, difficult to synthesize; Sascore<6,easy to synthesize Fsp3 0.0 l. The number of sp3 |
Fsp3 | 0.0 | 0.104 | 0.0 | The number of sp3 hybridized carbons / totalcarbon count, correlating with melting point andsolubility. Fsp3≥ 0.42 is considered a suitable value. |
MCE-18 | 24.0 | 105.057 | 18.0 | MCE-18 stands for medicinal chemistry evolution. MCE-18 ≥45 is considered a suitable value. |
Npscore | 1.071 | 0.337 | 0.119 | Natural product-likeness score. This score is typically in the range from -5 to 5. The higher the score is, the higher the probability isthat the molecule is a NP. |
Lipinski Rule | Accepted | Rejected | Accepted | MW ≤ 500; logP≤ 5; Hacc≤ 10; Hdon≤ 5. If two properties are out of range, a poor, absorption or permeability is possible, one isacceptable. |
Pfizer Rule | Accepted | Accepted | Accepted | logP> 3; TPSA < 75, Compounds with a high log P (>3) and low TPSA(<75) are likely to be toxic. |
GSK Rule | Accepted | Rejected | Rejected | MW ≤ 400; logP≤ 4, Compounds satisfying the GSK rule may have amore favourable ADMET profile |
Golden Triangle | Accepted | Rejected | Rejected | MW ≤ 400; logP≤ 4, Compounds satisfying the GSK rule may have a
more favourable ADMET profile |
PAINS | 1 alert | 0 | 0 | 200 ≤ MW ≤ 50; -2 ≤logD≤ 5, Compounds satisfying the Golden Triangle rulemay have a 3 alertsmore favorable ADMET profile. |
ALARM NMR | 3 alerts | 2 | 2 | Pan Assay Interference Compounds, frequent hitters, Alpha-screen artifacts and reactive compound. |
BMS | 1 alert | 1 | 0 | Undesirable, reactive compounds. |
Chelator Rule | 1 alert | 0 | 0 | Chelating compounds |
Absorption: Here different parameters are used for predicting absorption of drug in intestine after oral administration. Colon adenocarcinoma (Caco-2) and Madin–Darby canine kidney (MDCK) permeability, P-glycoprotein transport are important parameters in predicting the absorption of drug in the intestine epithelial cells 34. The predictions of values for different absorption parameters are tabulated in Table 9.
TABLE 9: ABSORPTION PREDICTIONS FOR EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
Caco-2 Permeability | -5.394 | -5.09 | -4.919 | Optimal: higher than -5.15 Log unit |
MDCK
Permeability |
1.1
e-05 |
2.5
e-05 |
2.7
e-05 |
low permeability: < 2 × 10-6 cm/s
medium permeability: 2–20 × 10-6 cm/s high passive permeability: > 20 × 10-6 cm/s |
Pgp-inhibitor | 0.003 | 1.0 | 1.0 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being Pgp-inhibitor |
Pgp-substrate | 0.276 | 0.002 | 0.0 | Category 1: substrate; Category 0: Non-substrate;
The output value is the probability of being Pgp-substrate |
HIA | 0.364 | 0.012 | 0.341 | Human Intestinal Absorption
Category 1: HIA+( HIA >30%); Category 0: HIA-(HIA < 30%); The output value is the probability of being HIA+ |
F20% | 0.13 | 0.945 | 0.627 | 20% Bioavailability
Category 1: F20%+ (bioavailability < 20%); Category 0: F20%- (bioavailability ≥ 20%); The output value is the probability of being F20%+ |
F30% | 0.998 | 0.147 | 0.106 | 30% Bioavailability
Category 1: F30%+ (bioavailability < 30%); Category 0: F30%- (bioavailability ≥ 30%); The output value is the probability of being F30%+ |
Distribution: Blood brain barrier and other biological barriers, protein binding play important role in distribution of the drug 35. Such parameters are predicted for EA, BEA and HEA in Table 10.
TABLE 10: DRUG DISTRIBUTION VALUE PREDICTIONS FOR EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
PPB | 83.86% | 115.4% | 64.18% | Plasma Protein Binding, Optimal: < 90%. Drugs with high protein-bound may have a low therapeutic index. |
VD | 0.693 | 0.221 | 1.071 | Volume Distribution
Optimal: 0.04-20L/kg |
BBB penetration | 0.014 | 0.001 | 0.001 | Blood-Brain Barrier Penetration
Category 1: BBB+; Category 0: BBB-; The output value is the probability of being BBB+ |
Fu | 23.55% | 0.851% | 10.66% | The fraction unbound in plasms
Low: <5%; Middle: 5~20%; High: > 20% |
Metabolism: Here, the metabolism of drug is predicted on the basis of inhibition of Cytochrome P(CYP) or metabolism of drug by CYP isoforms 36. The prediction of metabolic behaviour of EA, BEA, HEA is shown in Table 11.
TABLE 11: METABOLISM OF EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
CYP1A2 inhibitor | 0.922 | 0.129 | 0.93 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being inhibitor. |
CYP1A2
substrate |
0.156 | 0.04 | 0.094 | Category 1: Substrate; Category 0: Non-substrate;
The output value is the probability of being substrate. |
CYP2C19
inhibitor |
0.016 | 0.485 | 0.978 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being inhibitor. |
CYP2C19
substrate |
0.05 | 0.043 | 0.069 | Category 1: Substrate; Category 0: Non-substrate;
The output value is the probability of being substrate. |
CYP2C9 inhibitor | 0.47 | 0.627 | 0.965 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being inhibitor. |
CYP2C9
substrate |
0.473 | 0.75 | 0.969 | Category 1: Substrate; Category 0: Non-substrate;
The output value is the probability of being substrate. |
CYP2D6 inhibitor | 0.013 | 0.0 | 0.003 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being inhibitor. |
CYP2D6
substrate |
0.146 | 0.023 | 0.733 | Category 1: Substrate; Category 0: Non-substrate;
The output value is the probability of being substrate. |
CYP3A4 inhibitor | 0.079 | 0.018 | 0.57 | Category 1: Inhibitor; Category 0: Non-inhibitor;
The output value is the probability of being inhibitor. |
CYP3A4
substrate |
0.014 | 0.019 | 0.51 | Category 1: Substrate; Category 0: Non-substrate;
The output value is the probability of being substrate |
Excretion: Excretion of drug is measure of removal of drug from the body. It is expressed in terms of clearance which is part of drug eliminated from the total distributed drug 37. The predictions about excretion of EA, BEA and HEA are tabulated in Table 12.
TABLE 12: EXCRETION OF EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
CL | 3.724 | 1.394 | 1.473 | Clearance, High: >15 mL/min/kg; moderate: 5-15 mL/min/kg;low: <5 mL/min/kg |
T1/2 | 0.886 | 0.129 | 0.07 | Category 1: long half-life ; Category 0: short, half-life; long half-life: >3h; short half-life: <3h. The output value is the probability of having long half-life. |
Toxicity: Along with determining drug likeliness, bioavailability, metabolism, distribution, it is equally important to determine the toxicity of drug to various body systems. This includes toxicity to heart, kidney, liver, mouth, eyes, skin, respiratory system. This also includes mutagenicity, carcinogenicity studies of drug 38. The toxicity predictions of EA, BEA, HEA for different parameters is tabulated in Table 13.
TABLE 13: TOXICITY OF EA, BEA, HEA
Property | Value | Comment | ||
EA | BEA | HEA | ||
hERG
Blockers |
0.015 | 0.001 | 0.012 | Category 1: active; Category 0: inactive;
The output value is the probability of being active. |
H-HT | 0.845 | 0.023 | 0.024 | Human Hepatotoxicity
Category 1: H-HT positive(+); Category 0: H-HTnegative(-); The output value is the probability of being toxic. |
DILI | 0.994 | 0.989 | 0.848 | Drug Induced Liver Injury.
Category 1: drugs with a high risk of DILI; Category0: drugs with no risk of DILI. The output value is theprobability of being toxic. |
AMES Toxicity | 0.189 | 0.014 | 0.513 | Category 1: Ames positive(+); Category 0: Amesnegative(-);
The output value is the probability of being toxic. |
Rat Oral Acute Toxicity | 0.039 | 0.187 | 0.992 | Category 0: low-toxicity; Category 1: high-toxicity;
The output value is the probability of being highlytoxic. |
FDAMDD | 0.697 | 0.91 | 0.878 | Maximum Recommended Daily Dose
Category 1: FDAMDD (+); Category 0: FDAMDD(-) The output value is the probability of being positive. |
Skin
Sensitization |
0.904 | 0.97 | 0.967 | Category 1: Sensitizer; Category 0: Non-sensitizer;
The output value is the probability of beingsensitizer. |
Carcinogen
city |
0.067 | 0.583 | 0.443 | Category 1: carcinogens; Category 0:
non-carcinogens; The output value is the probability of being toxic. |
Eye Corrosion | 0.121 | 0.003 | 0.106 | Category 1: corrosives; Category 0: non-corrosives
The output value is the probability of beingcorrosives. |
Eye Irritation | 0.894 | 0.711 | 0.944 | Category 1: irritants; Category 0: non-irritants
The output value is the probability of being irritants. |
Respiratory toxicity | 0.041 | 0.043 | 0.332 | Category 1: respiratory toxicants; Category 0:respiratory non-toxicants
The output value is the probability of being toxic. |
In all above tables colours used indicate- green: excellent, orange: medium, red: poor
CONCLUSION: For the preparation of tetrabenzoyl ellagic acid (BEA) from ellagic acid, very commonly used and simple method for O-benzoylation of phenol was employed. The conversion was verified by TLC and mixture of Hexane and ethyl acetate (8:2) was used. Good yield of pinkish white product was obtained. It has better solubility in methanol, chloroform, hexane. The product was purified by column chromatography by using silica gel in hexane and eluting it with the mixture of hexane and ellagic acid. The purity of product was checked with gas chromatography which gave a single peak, indicating absence of any impurity in the product. From the comparison of the IR spectrum of ellagic acid and benzoyl ellagic acid, the peak for phenolic -OH in ellagic acid disappeared in benzoyl ellagic acid. This confirms the structure of product. The structure of the product was further confirmed by H1 and C13 NMR spectras. For the preparation of octasodium salt of hydrolysis of ellagic acid (HEA) from ellagic acid, very commonly used and simple method for base catalysed hydrolysis of lactones was employed. Good yield of dark brown product was obtained. It has solubility only in water. From the comparison of the IR spectrum of ellagic acid and sodium salt of hydrolysis of ellagic acid, the peak for lactone in ellagic acid disappeared in sodium salt of hydrolysis of ellagic acid. This confirms the structure of product. The structure of the product was further confirmed by H1 and C13 NMR spectras. The in-silico study of drug like properties of EA, BEA, HEA was carried out using ADMETLab2 online platform. The predictions about the physiochemical properties, medicinal properties, absorption, distribution, metabolism, excretion, toxicity were carried out using the structure of these molecules. From these predictions it can be seen that among the three molecules EA and HEA are showing good physiochemical properties as well as medicinal properties. Among the three molecules, HEA shows the best absorption whereas EA shows best distribution. All the three molecules show moderate metabolism; excretion and toxicity profiles. On the basis of this analysis EA is having best drug like properties than HEA and BEA.
ACKNOWLEDGEMENT: Nil
CONFLICTS OF INTEREST: Nil
REFERENCES:
- Wagner H: Synergy research: approaching a new generation of phytopharmaceuticals. Fitoterapia 2011; 82(1): 34-7.
- Ezzat SM, Jeevanandam J, Egbuna C, Kumar S and Ifemeje JC: Phytochemicals as sources of drugs. Phytochemistry: An in-silico and in-vitro Update: Advances in Phytochemical Research 2019; 3-22.
- Harvey AL: Natural products in drug discovery. Drug Discovery Today 2008; 13(19-20): 894-901.
- Guha R: On exploring structure–activity relationships. In-silico Models for Drug Discovery 2013; 81-94.
- Saganuwan SA: Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy. DARU J of Pharma Scienc 2024; 32(2): 781-800.
- Egbuna C, Ezzat SM, Tijjani H and Srivastav VK: Synthetic analogs of phytochemicals. Phytochemistry: An in-silico and in-vitro Update: Advances in Phytochemical Research 2019; 23-55.
- Lu W, Zhang R, Sheng W, Feng L, Xu P, Wang Y, Xie Y, Xu H, Wang G and Aa J: Identification of morphine and heroin-treatment in mice using metabonomics. Metabolites 2021; 11(9): 607.
- Fist AJ, Byrne CJ and Gerlach WL: Harvesting poppy capsules of stably reproducing papaver somniferum to produce straw containing thebaine and oripavine in at least 50% by weight of alkaloid mixture formed, chemically extracting thebaine and/or oripavine 2002.
- Wongsrichanalai C, Pickard AL, Wernsdorfer WH and Meshnick SR: Epidemiology of drug-resistant malaria. Lancet Infect Dis 2002; 2: 209–18.
- Güthner T, Mertschenk B and Schulz B: Guanidine and derivatives. Ullmann's encyclopedia of Industrial Chemistry 2000.
- Lorenzo JM, Munekata PE, Putnik P, Kovačević DB, Muchenje V and Barba FJ: Sources, chemistry, and biological potential of ellagitannins and ellagic acid derivatives. Studies in Natural Products Chemistry 2019; 60: 189-221.
- Evtyugin DD, Magina S and Evtuguin DV: Recent advances in the production and applications of ellagic acid and its derivatives. A review. Molecules 2020; 25(12): 2745.
- Ríos JL, Giner RM, Marín M and Recio MC: A pharmacological update of ellagic acid. Planta medica. 2018; 84(15): 1068-93.
- Maas JL, Galletta GJ and Stoner GD: Ellagic acid, an anticarcinogen in fruits, especially in strawberries: a review. Hort Science 1991; 26(1): 10-14.
- Cerdá B, Tomás-Barberán FA and Espín JC: Metabolism of antioxidant and chemopreventive ellagitannins from strawberries, raspberries, walnuts, and oak-aged wine in humans: identification of biomarkers and individual variability. J of Agri and Food Chem 2005; 53(2): 227-35.
- Heur YH, Zeng W, Stoner GD, Nemeth GA and Hilton B: Synthesis of ellagic acid O-alkyl derivatives and isolation of ellagic acid as a tetrahexanoyl derivative from Fragaria ananassa. J of Natural Products 1992; 55(10): 1402-7.
- Asami Y, Ogura T, Otake N, Nishimura T, Xinsheng Y, Sakurai T, Nagasawa H, Sakuda S and Tatsuta K: Isolation and synthesis of a new bioactive ellagic acid derivative from Combretumy unnanensis. Journal of Natural Products 2003; 66(5): 729-31.
- Ye G, Peng H, Fan M and Huang CG: Ellagic acid derivatives from the stem bark of Dipentodonsinicus. Chemistry of Natural Compounds 2007; 43: 125-7.
- Khallouki F, Haubner R, Hull WE, Erben G, Spiegelhalder B, Bartsch H and Owen RW: Isolation, purification and identification of ellagic acid derivatives, catechins, and procyanidins from the root bark of Anisophylleadichostyla R. Br. Food and Chem Toxicology 2007; 45(3): 472-85.
- Evtyugin DD, Magina S and Evtuguin DV: Recent advances in the production and applications of ellagic acid and its derivatives. A review. Molecules 2020; 25(12): 2745.
- Da Silva SL, Calgarotto AK, Chaar JS, Marangoni S. Isolation and characterization of ellagic acid derivatives isolated from Casearia sylvestris SW aqueous extract with anti-PLA2 activity. Toxicon 2008; 52(6): 655-66.
- Ito A, Chai HB, Lee D, Kardono LB, Riswan S, Farnsworth NR, Cordell GA, Pezzuto JM and Kinghorn AD: Ellagic acid derivatives and cytotoxic cucurbitacins from Elaeocarpus mastersii. Phytoche 2002; 61(2): 171-4.
- Ito A, Chai HB, Lee D, Kardono LB, Riswan S, Farnsworth NR, Cordell GA, Pezzuto JM and Kinghorn AD: Ellagic acid derivatives and cytotoxic cucurbitacins from Elaeocarpus mastersii. Phytochemistry 2002; 61(2): 171-4.
- Norinder U and Bergström CA: Prediction of ADMET properties. ChemMedChem: Chemistry Enabling Drug Discovery 2006; 1(9): 920-37.
- Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK and Kumar P: Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity 2021; 25: 1315-60.
- Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, Lu AP and Cao DS: ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics 2018; 10: 1-11.
- Zadorozhnii PV, Kiselev VV and Kharchenko AV: In-silico ADME profiling of salubrinal and its analogues. Future Pharmacology 2022; 2(2): 160-97.
- Varenichenko SA, Kharchenko AV and Farat OK: Synthesis and in-silico Admet profiling of novel 5-arylidene-2-(2, 3, 5, 6, 7, 8-Hexahydroacridin-4 (1h)-Ylidene)-1, 3-Thiazolidin-4-Ones. Issues of Chemistry & Chemical Technology/Voprosy Khimii & Khimicheskoi Tekhnologii 2023; 1(6).
- Aljelehawy Q, Mal Allah OR and Sourazur G: Physicochemical properties, medicinal chemistry, toxicity, and absorption of quercetin and its interaction with spike glycoprotein of SARS-CoV-2: Molecular docking. Nano Micro Biosystems 2022; 1(1): 32-9.
- Valencia E, Olarte W, Galvis M and Sastoque F: Revisión, Caracterización Y Análisis Bioinformático De Nueva Delhi Metalo-Β-Lactamasa-1 (Ndm-1) Y Sus Variantes. Revista de la Facultad de Ciencias 2023; 12(1): 59-76.
- Živančević K, Bozic D, Baralić K, Ćurčić M, Miljaković EA, Antonijević B and Đukić-Ćosić D: In-silico prediction of physicochemical, pharmacokinetic and toxicological properties of sulforaphane. MPB 2022; 68: 331-2.
- Hörter D and Dressman JB: Influence of physicochemical properties on dissolution of drugs in the gastrointestinal tract. Adv Drug Delivery Reviews 2001; 46(1-3): 75-87.
- Leeson PD and Springthorpe B: The influence of drug-like concepts on decision-making in medicinal chemistry. Nature Reviews Drug Discovery 2007; 6(11): 881-90.
- Volpe DA: Drug-permeability and transporter assays in Caco-2 and MDCK cell lines. Future Medicinal Chemistry 2011; 3(16): 2063-77.
- Wanat K: Biological barriers, and the influence of protein binding on the passage of drugs across them. Molecular Biology Reports 2020; 47(4): 3221-31.
- Zhao M, Ma J, Li M, Zhang Y, Jiang B, Zhao X, Huai C, Shen L, Zhang N, He L and Qin S: Cytochrome P450 enzymes and drug metabolism in humans. International Journal of Molecular Sciences 2021; 22(23): 12808.
- Shargel L, Wu-Pong S and Yu AB: Chapter 6. Drug elimination and clearance. Applied Biopharmaceutics & Pharmacokinetics. 6th Edition. New York (NY): The McGraw-Hill Companies 2012; 106.
- Gupta R, Polaka S, Rajpoot K, Tekade M, Sharma MC and Tekade RK: Importance of toxicity testing in drug discovery and research. In Pharmacokinetics and toxicokinetic considerations. Acad Press 2022; 117-144.
How to cite this article:
Kamath C and Salvi S: Preparation of two new derivatives of ellagic acid and in-silico studies of their drug like properties. Int J Pharm Sci & Res 2025; 16(8): 2332-47. doi: 10.13040/IJPSR.0975-8232.16(8).2332-47.
All © 2025 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Article Information
20
2332-2347
1168 KB
36
English
IJPSR
Chitra Kamath and Swarupa Salvi *
Sathaye College, Vile-Parle, Mumbai University, Mumbai, Maharashtra, India.
swarupa.salvi@sathayecollege.edu.in
26 February 2025
23 March 2025
07 April 2025
10.13040/IJPSR.0975-8232.16(8).2332-47
01 August 2025