2.1 Protein and ligand preparation
The signaling protein, Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2 PDB ID: 3V2A), obtained from protein database bank, was cleaned using Schrödinger module before visualization of its X-ray diffraction data with RasMol software; depicted in ribbon and backbone model [Fig. 1]. The target protein has two chains with 280 groups of 2114 atoms and total 2160 bonds. There are three spring-shaped alpha-helices in red-color, and 25 beta-strands in blue color, however no turns and loops. The binding of ligand could be anticipated from protein-folds of the target VEGF protein. Before docking, ligand preparation was done in the LigPrep module of Schrodinger suite, 2013.
2.2 Molecular Docking
The docking results of the 12 established compounds in the first cavity which is the largest cavity of the target protein VEGF listed in Table 1. Among all the established compounds[Table 7], AEE788 (PubChem ID 10297043) is resulted as the best-established compounds as it shows the lowest re-rank score among all compounds and has the highest affinity towards the VEGF. The compound also has high-affinity and physicochemical properties such as molecular weight 440.595 g/mol, hydrogen bond donor counts 2 and hydrogen bond acceptor count 5, and logP value of 4.6. The compound holds a re-rank score-95.9802 with the H-bond interaction score of -2.5. Hence, this compound was identified as the best- established compound against the target protein VEGF.
Table 1
Established compounds docking study result.
Ligand
|
Filename
|
MolDock Score
|
Rerank Score
|
HBond
|
MW
|
10297043
|
[02]10297043
|
-121.765
|
-95.9802
|
-2.5
|
440.583
|
123631
|
[00]123631
|
-116.826
|
-91.3569
|
-1.27851
|
446.902
|
9933475
|
[01]9933475
|
-121.08
|
-90.3479
|
-2.26465
|
450.505
|
216239
|
[04]216239
|
-106.066
|
-90.2369
|
-4.5783
|
464.825
|
9809715
|
[03]9809715
|
-130.606
|
-88.876
|
-1.88135
|
539.625
|
123631
|
[01]123631
|
-106.039
|
-87.1955
|
-1.95681
|
446.902
|
9911830
|
[00]9911830
|
-120.891
|
-86.9845
|
-8.73156
|
454.863
|
9911830
|
[03]9911830
|
-116.349
|
-85.2625
|
-1.99962
|
454.863
|
9911830
|
[01]9911830
|
-116.111
|
-84.7526
|
-1.6096
|
454.863
|
10297043
|
[04]10297043
|
-114.998
|
-84.607
|
0
|
440.583
|
10113978
|
[00]10113978
|
-110.567
|
-84.4218
|
-2.45945
|
437.518
|
9933475
|
[02]9933475
|
-112.536
|
-84.2488
|
-2.42747
|
450.505
|
2.3 Virtual screening
Similarity search of the best-established compound against PubChem database resulted in 80 compound structures that gave a = > 95 similarity percentage. Table 2 records the top 11 compounds that exhibit the greatest affinity to the target protein VEGF. The compound with PubChem ID 88265020 was recognized to hold the lowest re-rank score and was therefore confirmed as a compound with the greatest affinity towards the targeted protein. Some of the physical properties of the virtual screened compound comprise of molecular weight of 569.783g/mol, hydrogen bond donor count of 5, hydrogen bond acceptor count of 7 and a logP value of 6.1. The re-rank score of this compound attains at -112.171 and the H-bond interaction score is -8.6315. Therefore, between a total of 80 compounds, the compound PubChemID-88265020 possess a much greater ability to inhibit the target protein VEGF against ovarian cancer subjected to further analysis.
Table 2
Docking study result for virtual screened compounds with reference to high-affinity Virtual screened compounds with reference to high affinity established compound AEE788 (PubChem ID 10297043)
Ligand
|
Filename
|
MolDock Score
|
Rerank Score
|
HBond
|
MW
|
88265020
|
[00]88265020
|
-149.521
|
-112.171
|
-8.6315
|
569.783
|
88265020
|
[01]88265020
|
-149.487
|
-109.471
|
-1.81064
|
569.783
|
88265020
|
[04]88265020
|
-128.011
|
-101.733
|
-3.45831
|
569.783
|
71313049
|
[00]71313049
|
-125.348
|
-100.642
|
-7.07561
|
440.583
|
44629455
|
[00]44629455
|
-131.699
|
-99.8896
|
-2.5
|
442.599
|
10297042
|
[03]10297042
|
-128.873
|
-99.1998
|
-0.10172
|
440.583
|
69170098
|
[04]69170098
|
-122.43
|
-98.656
|
-2.5
|
412.53
|
69232929
|
[00]69232929
|
-125.312
|
-98.3018
|
-7.16336
|
412.53
|
16071547
|
[01]16071547
|
-128.745
|
-97.5661
|
-3.87498
|
516.679
|
16071547
|
[03]16071547
|
-118.562
|
-96.8667
|
0
|
516.679
|
69232929
|
[01]69232929
|
-127.435
|
-96.8654
|
-4.88497
|
412.53
|
2.4 Molecular Dynamics Simulation
The dynamic simulation of a molecule provides details of probable conformational changes over a trajectory, comparable to biological environment. The simulation result is analyzed over structural analysis by RMSD and RMSF, ligand properties, and protein-ligand interaction. The root mean square deviation (RMSD) graph suggests structural stability of protein-ligand interaction, lower RMSD confers greater stability. Root mean square fluctuation (RMSF) graph indicates mobility of protein residue. The inter-residue interaction for established compounds shows to be more stable compared to virtually screened compounds. The protein RMSD for AEE788 is in between 3.0 to 4.2 Å with deviation around 20ns, and the ligand graph follows the estimated trend as protein except for deviation between 40-60ns. [Fig. 2(I)]. This suggests the protein-ligand interaction is steadier; confirmed with the RMSF graph for AEE788 ranged 1–8 Å, where peaks are more frequent implying more flexible amino acids are on protein’s C⍺ backbone. [Fig. 2 (II)]. The RMSD graph for protein-lead compounds is more volatile comparatively. The protein RMSD fluctuates from 2.4 to 6.5 Å at frequent intervals and lead compound RMSD spans over 2.5 to 22.5Å with recurrent variation over the trajectory. [Fig. 2(III)]. It is noted that mean value of protein-ligand interaction for virtually screened compounds is more than AEE788, hence less stable. Beside there are fewer peaks in the RMSF graph which alludes less flexibility in protein’s C⍺ backbone. [Fig. 2(IV)].
2.4.2 Protein-Ligand interaction
Molecular dynamic simulation gives insight into probable protein-ligand interaction for established compound and virtually screened compound, depicted with histogram and heatmap [Fig. 3] [Fig. 4]. The interface between protein and ligand comprises four types of bonds: hydrogen bonds, hydrophobic interaction, ionic bonds, and water bridges; hydrogen bond plays significant role in ligand binding and drug specificity.The histogram for protein-established compound propones its hydrophobic nature which means pi-cation, pi-pi, and other nonspecific interactions are present [Fig. 3 (C)]. The amino acid residues PRO_49, MET_78, MET_81, HIS_133, MET_197 and ILE_215 exhibits more hydrophobic interaction, of these, HIS_133 showed strongest interaction [Fig. 3(A)], as confirmed by heatmap [Fig. 3(B)]. The hydrogen bonding which strongly influences drug specificity, metabolization and adsorption is by few residues LYS_48, GLN_79 and TYR_165, alongside, only GLU_30 residue shows ionic interaction inferring the established compound is not much ligand specific. However, some residues GLU_30, LYS_48, SER_50, CYS_51, PRO_53, THR_77, GLN_79, ARG_164, TYR_165, GLY_196 are forming water bridges (hydrogen bonded protein-ligand interaction mediated by water molecules). The [Fig. 3(B)] is a heatmap for individual residue interaction with protein over a trajectory frame, the intensity of color enumerates interactions of amino acids.
The protein-lead compound interaction, depicted in [Fig.
4] suggest overwhelming hydrophobic interaction and water bridges. This compound shows more hydrogen bonding, owing to hydroxylamine octyl chain. TYR_165 asserts strongest hydrophobic interaction along with PRO_166, TYR_194, MET_197, PHE_199 residues [Fig.
4(A)]. This interaction is by strong pi-pi bonding of residue with aromatic ring [Fig.
4(C)], while other hydrophobic interaction is quite less strong. The virtually screened compound is showing ionic bond by residue GLU_167, plausibly with additional hydroxylamine functional group. And residues like MET_213 and GLN_132 are forming hydrogen bond by back donating [Fig.
7]. The water bridges are formed by residues TYR_165, GLU_167, TYR_194, GLN_210. Which also connote this surface is exposed for binding.The heatmap for this interaction insinuate only few residues like TYR_165, PRO_166, GLU_167, TYR_194, MET_197, and PHE_199 have a greater number of contacts over trajectory frame compared to others. The number of specific contacts made by protein with ligand vary from zero to nine [Fig.
4(B)].
2.4.3 Ligand property
The ligand property was analyzed over a range of parameters: root-mean square deviation (RMSD), radius of gyration (rGyr), molecular surface area (MolSA), solvent accessible surface area (SASA), and polar surface area (PSA), giving structural details.The RMSD graph demonstrating stability of the compound, for established compounds is ranged from 1–3 Å with minimal fluctuation and mean near 2 Å. The radius of gyration is the distance of an atom from the center of mass of the molecule, in order to obtain the same moment of inertia; it provides insight into the overall dimension of the protein. The rGyr value is in between 4.8-6 Å with mean at 5.2 Å. The molecular surface area is calculated using 1.4 probe radius; it gives insight on Van Der Waals surface area. The MolSA is ranged between 424–448 Å2 with minimal fluctuation around 80 ns. The solvent surface area is a water accessible area which ranges from 200 to 500 Å2 and is highly variable with two equilibrium peaks around 300 Å2 and 400 Å2. The polar surface area is surface accessible for binding of ionic molecules. The PSA value was stable over the trajectory with mean at 80 Å2. [Fig.5]
The virtual screened compound’s RMSD trajectory is erratic range from 1–4 Å with fluctuation between 20 to 80 ns. The rGyr is stable between 20 to 60 ns and 80–100 ns, ranging between 6.4 to 8.8 Å, which is higher than the established compound. The molecular surface area ranged from 580 to 600 Å2 with fluctuation around 50 ns and equilibrium approximately 595 Å2 which is more compared to the established compound (432 Å2). Also, the solvent accessible surface area is more erratic varying from 500 to 1000 Å2 and mean polar surface area is 160 Å2. As the surface area of virtual screened compounds is relatively more compared to established compounds, attributed to the octyl hydroxylamine chain, the mean value of surface areas and radius of gyration is also higher and it also gives more flexibility to ligands which could be seen from the RMSD graph. [Fig. 6]. The ligand property of virtually screened compound (PubChem CID: 88265020) is studied more with pharmacophore mapping.
2.5 Drug - Drug Comparative study
Drug-drug Comparative study records the MolDock Scores and re-ranks scores of the best- established compound and the best virtual screened compound against the target protein VEGF on ovarian cancer [Table 3]. The table indicates that the best-virtual screened compound holding PubChem ID: 88265020 has a higher binding affinity to the target VEGF protein if correlated to the best- established compound AEE788 (PubChem ID: 10297043) due to a lower re-rank score counts − 112.158. The compound holding PubChem ID 88265020 discloses lower MolDock Scores and re-rank scores for several additional essential characteristics like External ligand interactions, protein-ligand interactions, hydrogen bonds which intimates that this compound occupies a greater affinity to the VEGF protein. A steric value measured by PLP (Piecewise Linear Potential) is lower for the best virtual screened compound whereas, in the case of LJ12-6(Leonard-Jones approximation) method, it shows lower value for the best-established compound. Therefore, it illustrates that both the compounds have comparable possible inhibition against the VEGF protein.
Table 3
Drug-Drug comparative study
|
Established Compound
PubChem ID: 10297043
|
Virtual Screened Compound
(PubChem ID: 88265020)
|
Energy overview: Descriptors
|
MolDock Score
|
Rerank Score
|
MolDock Score
|
Rerank Score
|
Total Energy
|
-121.763
|
-95.967
|
-149.505
|
-112.158
|
External Ligand interactions
|
-143.736
|
-122.464
|
-174.491
|
-144.108
|
Protein - Ligand interactions
|
-143.736
|
-122.464
|
-174.491
|
-144.108
|
Steric (by PLP)
|
-141.236
|
-96.888
|
-165.861
|
-113.781
|
Steric (by LJ12-6)
|
|
-23.596
|
|
-23.493
|
Hydrogen bonds
|
-2.5
|
-1.98
|
-8.63
|
-6.835
|
Hydrogen bonds (no directionality)
|
|
0
|
|
0
|
Electrostatic (short range)
|
0
|
0
|
0
|
0
|
Electrostatic (long range)
|
0
|
0
|
0
|
0
|
Cofactor - Ligand
|
0
|
0
|
0
|
0
|
Steric (by PLP)
|
0
|
|
0
|
|
Steric (by LJ12-6)
|
|
0
|
|
0
|
Hydrogen bonds
|
0
|
0
|
0
|
0
|
Electrostatic
|
0
|
0
|
0
|
0
|
Water - Ligand interactions
|
0
|
0
|
0
|
0
|
Internal Ligand interactions
|
21.973
|
26.497
|
24.986
|
31.95
|
Torsional strain
|
7.658
|
7.183
|
16.15
|
15.148
|
Torsional strain (sp2-sp2)
|
|
3.952
|
|
3.436
|
Hydrogen bonds
|
|
0
|
|
0
|
Steric (by PLP)
|
14.315
|
2.462
|
8.836
|
1.52
|
Steric (by LJ12-6)
|
|
12.9
|
|
11.845
|
Electrostatic
|
0
|
0
|
0
|
0
|
Soft Constraint Penalty
|
0
|
|
0
|
|
Search Space Penalty
|
0
|
|
0
|
|
2.6 Pharmacophore mapping
Pharmacophore mapping provides the quintessentialsystemic spatial feature of the molecular interaction of the ligand with the target receptor, apart from the method of molecular docking, for understanding the interactive characterization and provide a factual query on the suitable target interface. It imitates the aligned poses of the molecule and identifies the apt interaction between the target protein and the lead compound. Owing to this admirable affinity and good interaction profile of the virtually screened compound (PubChem ID 88265020), the study was conducted to obtain various kinds of analyzed pharmacophore interactions. The desired compounds were in .sdfformat for pharmacophore studies. The residue interaction of the best virtually screened compound (PubChem ID 88265020) in the cavity of VEGF protein was studied.
The Van Der Waals coupling of the VEGF protein structure and the virtually screened compound (PubChem ID 88265020) is displayed in [Fig. 7]. The electrostatic interaction is shown by Gly196, Met197, His133, Tyr194, Tyr165, Lys48, Met213 and Gln132 residues, symbolically encircled in pink, these residues are also involved in hydrogen bonding; whereas Pro49, Ala195, Pro166, Glu167, Ile215, and Ile212 residues are involved in van der Waals interactions, encircled in green. The residues represented with various shades and size blue halo around it infers the solvent-accessible surface of an interacting residue such as Tyr165 and Met213 residues: the size of halo is proportional to the accessible surface area.Furthermore, the solvent-accessible surface of an atom is represented by a blue halo around the atom and the diameter of the circle is proportional to the solvent-accessible surface. Met213, Gln132, and Tyr194 show the Hydrogen bond interactions with amino acid main chains are represented by a green dashed arrow directed towards the electron donor. Tyr165 with the compound shows pi-pi interaction is represented by an orange line with the pi symbol indicating the interaction.
The H-bond interactions of the virtual screened compound (PubChem ID: 88265020) having the lowest re-rank score possessing immense affinity at the active sites of the VEGF protein cavity [Fig. 8]. Hydrophilic and hydrophobic, electrostatic and H-bond interactions are the example of the pharmacophoric characteristics of the ligand-receptor interaction. Figure 8 represents the compound binding of the specific amino acid residues of the VEGF protein through a hydrogen bond. An amino acid Ile212, Met213, and Ala195 residue shows the four H-bond interactions represented by the blue dotted line [Fig. 8]. The virtual screened compound (Pubchem ID: 88265020) has the number of hydrogen bond interactions as compared to the established compound, AEE788 (PubChem ID 10297043).
The electrostatic interaction of virtual screened compound (PubChem ID-88265020) [Fig. 9] manifests that the clusters of charged and polar residues, that are detected on protein-protein interfaces, may intensify the stability of the complex; though the net effect of electrostatics is generally destabilizing. The virtually screened compound (PubChem ID-88265020) with VEGF protein having the high affinity was embedded in the protein cavity. The positive and negative areas of the protein are demonstrated by two types of variant colors: the red color evince the electro-negativity zone, whereas the electropositive zone with blue color. The electrically neutral zone is shown with white colored surface. Most of the atoms of the target compound in the protein cavity were observed to be biased towards blue color zone inferring the high electro-positivity [Fig. 9].
Figure 10 represents the aromatic interactions with the most effective virtual screened compound (PubChem ID-88265020). The aromatic interaction is indicative of effective binding stability of the compound with protein. The aromatic conformation imparts an effect on the function of the complex molecule. The identified virtual screened compound (PubChem ID: 88265020) shows a higher affinity aromatic interaction in the VEGF protein binding site. In [Fig. 10], the blue color symbolizes the edges of the protein cavity with light shade surfaces and shade surfaces having color orange signifying the face. Here an amino acid His133, Met197, Gly196, Tyr165, Pro166, Ala195, Tyr194, Leu161, and Glu132 residues are showing aromatic interaction. These interactions coincide with molecular dynamic observation.
2.7 ADMET studies-Pharmacological and metabolic properties
AdmetSAR software was used to estimates various ADMET properties of the best-established compound AEE788 (PubChem ID 10297043) and the best virtual screened compound holding PubChem ID: 88265020 [Tables 4 & 5]. The SwissADME software briefing six essential properties of oral bioavailability is represented with bioavailability radar for best of the two best-established compounds and virtually- screened compounds [Fig. 11]; the pink region in radar is range of optimal value.
2.7.1 Absorption prediction of the compound
After the drug administration, ADMET blood-brain barrier model foretells the penetration of the drug across blood-brain barrier [Table 4]. The intestinal absorption of a drug is prognosticated by HIA after oral administration. While comparing with the established drug, ADMET absorption level of compound is revealing good absorption [Table 4]. A monolayer tissue culture of an ideal human colon adenocarcinoma (referred as, Caco-2) is acknowledged as a standard for testing drug permeability in drug discovery, the foretold effects exhibit permeability in both cases.P-glycoprotein is associated in many purposes like clearance of xenobiotics compound, transport of small molecules to vital areas, and present in multidrug-resistant malignant cells: its inhibition can be utilized to decrease the multidrug-resistant characteristic. It is also an ABC transporter. On analysis, compounds displayed excellent circumstances in this case. Also, the inhibitory characteristics against P-glycoprotein imply that compound can be applied for the treatment of multidrug-resistant cancer cell lines.
2.7.2 ADMET aqueous solubility
In bioavailability of the drug, the aqueous solubility acts as an essential parameter. At 25° C, it foretells the solubility of the compound in water. Compound holding a PubChem ID: 88265020 confers almost excellent aqueous solubility level by displaying a value of -2.7704 [Table 5]. It indicates that the virtual screened compound holding a PubChem ID: 88265020 are more polar and more soluble in the aqueous medium correlated with the established compound AEE788 (PubChem ID 10297043).
2.7.3 Compound metabolism
In this section, the expression of the relevant isoforms of cytochrome P450 as a substrate or inhibitor of the virtual screened compound is prognosticated. The compound acts as non-substrate of 2C9, 2D6, and 3A4 isoform of cytochrome P450. In inhibition forecast, it did not expose any inhibitory outcome in 2C9, 2D6, 2C19, and 3A4 isoforms [Table 4] but it displayed in 1A2 isoform of CYP450. Comparison of the score intimates that the virtual screened compound is sufficiently metabolized with respect to cytochrome P450 while comparing with the established compound.
2.7.4 Toxicity prediction
The mutagenicity of a compound is determined by the AMES toxicity test. In the case of the established compound, a negative AMES toxicity test result was designated by the processed ligand compound which indicates that the compound is non-mutagenic. Also, the virtual screened compound is non-carcinogenic and it is showing a lower value contrasted to the established compound. In acute oral toxicity prediction, the virtual screened compound is displaying a slightly higher score compared to the established compound. LD50 dose in the rat model which is the most significant parameter is calculated applying admetSAR. A compound is more lethal when it holds the lower LD50 value compared to a compound having the higher LD50 value. Additionally, it is observed that the virtual screened compound had relatively comparable LD50 value when differentiating with the established compound (2.63 and 2.77 sequentially) [Table 5]. The graphical representation based on parameters HIA, BBB, ADME toxicity, and LD50 is in Fig. 12.
Table 4
Corresponding ADMET profile analysis of the best-established inhibitor (PubChemID:10297043) and best virtual screened compound holding Pub-ChemID 88265020
|
Established Compound AEE788 (PubChem ID: 10297043)
|
Virtual Screened Compound PubChem ID: 88265020
|
Model
|
Result
|
Probability
|
Result
|
Probability
|
Absorption
|
|
|
|
|
Blood-Brain Barrier
|
BBB+
|
0.8872
|
BBB+
|
0.9262
|
Human Intestinal Absorption
|
HIA+
|
0.9964
|
HIA+
|
0.996
|
Caco-2 Permeability
|
Caco2-
|
0.5453
|
Caco2-
|
0.638
|
P-glycoprotein Substrate
|
Substrate
|
0.8334
|
Substrate
|
0.7012
|
P-glycoprotein Inhibitor
|
Inhibitor
|
0.5299
|
Non-inhibitor
|
0.543
|
Inhibitor
|
0.7895
|
Inhibitor
|
0.6269
|
Renal Organic Cation Transporter
|
Inhibitor
|
0.6161
|
Non-inhibitor
|
0.5375
|
|
Established Compound
|
Virtual Screened Compound
|
Distribution
|
Result
|
Probability
|
Result
|
Probability
|
Subcellular localization
|
Nucleus
|
0.5677
|
Mitochondria
|
0.4583
|
Metabolism
|
|
|
|
|
CYP450 2C9 Substrate
|
Non-substrate
|
0.8446
|
Non-substrate
|
0.7538
|
CYP450 2D6 Substrate
|
Non-substrate
|
0.696
|
Non-substrate
|
0.7285
|
CYP450 3A4 Substrate
|
Non-substrate
|
0.5221
|
Non-substrate
|
0.5396
|
CYP450 1A2 Inhibitor
|
Non-inhibitor
|
0.597
|
Inhibitor
|
0.6884
|
CYP450 2C9 Inhibitor
|
Non-inhibitor
|
0.7066
|
Non-inhibitor
|
0.8274
|
CYP450 2D6 Inhibitor
|
Non-inhibitor
|
0.7209
|
Non-inhibitor
|
0.8123
|
CYP450 2C19 Inhibitor
|
Non-inhibitor
|
0.6596
|
Non-inhibitor
|
0.8159
|
CYP450 3A4 Inhibitor
|
Non-inhibitor
|
0.8663
|
Non-inhibitor
|
0.8503
|
CYP Inhibitory Promiscuity
|
High CYP Inhibitory Promiscuity
|
0.8082
|
Low CYP Inhibitory Promiscuity
|
0.5736
|
|
Established Compound
|
Virtual Screened Compound
|
Excretion
|
Result
|
Probability
|
Result
|
Probability
|
Toxicity
|
Human Ether-a-go-go-Related Gene Inhibition
|
Weak inhibitor
|
0.6689
|
Strong inhibitor
|
0.6529
|
Inhibitor
|
0.8604
|
Inhibitor
|
0.75
|
AMES Toxicity
|
Non AMES toxic
|
0.7699
|
AMES toxic
|
0.5252
|
Carcinogens
|
Non-carcinogens
|
0.8755
|
Non-carcinogens
|
0.7132
|
Fish Toxicity
|
High FHMT
|
0.9324
|
High FHMT
|
0.5096
|
Tetrahymena Pyriformis Toxicity
|
High TPT
|
0.9706
|
High TPT
|
0.9026
|
Honey Bee Toxicity
|
Low HBT
|
0.8444
|
Low HBT
|
0.8037
|
Biodegradation
|
Not ready biodegradable
|
0.9975
|
Not ready biodegradable
|
0.9963
|
Acute Oral Toxicity
|
III
|
0.5151
|
III
|
0.5992
|
Carcinogenicity (Three-class)
|
Non-required
|
0.6468
|
Non-required
|
0.4987
|
Table 5 ADMET - Regression study
ADMET Predicted Profile --- Regression
|
Established Compound
|
Virtual Screened Compound
|
Model
|
Value
|
Unit
|
Value
|
Unit
|
Absorption
|
Aqueous solubility
|
-3.2214
|
LogS
|
-2.7704
|
LogS
|
Caco-2 Permeability
|
0.1928
|
LogPapp, cm/s
|
0.2113
|
LogPapp, cm/s
|
Distribution
|
Metabolism
|
Excretion
|
Toxicity
|
Rat Acute Toxicity
|
2.778
|
LD50, mol/kg
|
2.6319
|
LD50, mol/kg
|
Fish Toxicity
|
1.2483
|
pLC50, mg/L
|
1.6451
|
pLC50, mg/L
|
Tetrahymena Pyriformis Toxicity
|
0.6549
|
pIGC50, ug/L
|
0.423
|
pIGC50, ug/L
|
2.8 Boiled EGG PLOT analysis
Weak bioavailability and pharmacokinetics are result of failures in drug development,apart from ADMET, efficacy, and toxicity. Gastrointestinal consumption and brain access are the foremost two pharmacokinetic exercises central to appraisal at the numerous points of the drug discovery processes. Here, the Brain or IntestinaLEstimateD permeation method (BOILED-Egg) renders a specific forbidding model that estimates the physicochemical properties of small molecules i.e., polarity and lipophilicity [Table 6]. The investigation explicates that the established compound AEE788 (PubChem ID: 10297043) pitching inside the yellow ellipse (i.e. the yolk) exposes the possibility of a high BBB crossing. Whereas, the best Virtual screened compound holding a PubChem ID: 88265020 pitches inside the white ellipse signifying the possibility of huge intestinal absorption [Fig. 13].
Table 6
Best 2 Established Docked compounds and Best 2 Virtual screened compounds used for Boiled Egg plot
Molecule
|
PubChem ID
|
MW (g/mol)
|
TPSA
|
MLOGP
|
GI absorption
|
BBB permeant
|
AEE-788
|
10297043
|
440.58
|
60.08
|
3.22
|
High
|
Yes
|
Gefitinib
|
123631
|
446.9
|
68.74
|
2.82
|
High
|
Yes
|
CID88265020
|
88265020
|
569.78
|
101.13
|
3.77
|
High
|
No
|
CID71313049
|
71313049
|
440.58
|
60.08
|
3.22
|
High
|
Yes
|