Retrieval and optimisation of structure
The 3.40 Å resolution complex 3 D structures of Human NLRP3-deltaPYD hexamer (7ZGU.pdb) was retrieved from RCSB Protein Data Bank (https://www.rcsb.org/). Using the SPDVB-4.10 version, initial tasks were first completed for protein preparation, including charge assignment, solvation parameters, and fragmental volumes (Morris et al., 2009). Using AutoDock4 Tool for molecular docking, the protein molecules were further improved [28].
Secondary structure predictions
Using the Self-Optimized Prediction Method with Alignment (SOPMA) online web server at https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html, protein secondary structures were proposed (Geourjon & Deléage, 1995) [29]. SOPMA predicts four conformational states: helix, beta sheets and bridges, turns, and coils.
Preparation of Targeted single chain unit of NLRP3
This complex protein consists of hexamer unit of delta PYD NLRP3 units. Using Biovia Discovery Studio 2021 the first the Heteroatoms (including water molecules and the repeated unit hexamer chains with excluded with except for the single chain A unit. The entire single molecular unit of protein consist of NACHT, LRR and PYD domains in assembly of A, B, C, D, E chains. The sequence single chain A protein consists of 924 amino acid chain length. The complete 3D structure of NLRP3-deltaPYD hexamer and retrieved single chain molecule is represented in the Fig. 2. Using SwissProtein Data Base Viewer version 4.10, the crystallographic structures of the NACHT, LRR and PYD proteins were analyzed for errors and broken chains, and then repaired [30]. After target chain (chain A) preparation the polar hydrogen atoms were added for a more realistic depiction of hydrogen bonding patterns during molecular docking simulations and the chain was saved in AutoDock 4.2 readable PDB(.pdb) format for further modifications.
Selected protein chain validation
The selected NLRP3 chain A was validated using the Swiss-Model ExPASy web server, and these computational assessments confirmed the high quality of the homology model developed with the SWISS MODEL (https://swissmodel.expasy.org/) server. Additionally, the Protein Structure Analysis (ProSA) web tool (https://prosa.services.came.sbg.ac.at/) evaluated the protein model quality by comparing its Z-score to native structures. By analyzing dihedral angles and Z-scores, the in-silico assessments provided critical confirmation of the modelled protein structure prior to further computational analyses.
Phytochemicals ligands retrieval and selection
The PubChem database (https://pubchem.ncbi.nlm.nih.gov) provided the ligand compounds' chemical composition, structural characteristics, and canonical SMILES. CORINA, an online program, was used to obtain the compound's PDB structures using canonical SMILES. Table 1 and Table 2 provides relative information on the phytochemicals employed in the study. Based on their prior investigation's findings about their anti-inflammatory abilities, each phytoconstituents was approved and considered for docking studies.
Table 1 Selected phyto-compounds and their known pharmacological properties.
Plant-derived compound
|
Species / Extracted from
|
Reported activity
|
References
|
Oxyacanthine
|
Berberis vulgaris / Root
|
Anti-inflammatory
|
[33]
|
Neferine
|
Nelumbo nucifera/ Seed
|
Anti-cancer, Anti-diabetic, Anti-
inflammatory
|
[34]
|
Magnoflorine
|
Berberis kansuensis, Tinospora cordifolia (Willd.)/
Branch, leaves and Stem
|
Anti-inflammatory, Anti- diabetic activity, Anti-oxidant
activity
|
[35]
|
Corynoline
|
Corydalis bungeana Turcz.
|
Anti-inflammatory activity
|
[36]
|
Columbamine
|
Tinospora sagittata (Oliv.)
Gagnep
|
Anti-inflammatory and anti-
nociceptive activities
|
[37]
|
Berberine
|
Berberis vulgaris / Root
|
Anti-inflammatory
|
[33]
|
Berbamine
|
Berberis amurensis,
|
Anti-inflammatory
|
[38]
|
Palmatine
|
Berberis cretica
|
Anti-inflammatory
|
[39]
|
Tranilast (Positive Control)
|
Synthetic Drug Derivative of cinnamamides and
carboxamide.
|
Anti-inflammatory
|
[40]
|
Oridonin
(positive control)
|
Isodonrubescens
natural terpenoids
|
Anti-inflammatory
|
[41]
|
Preparation of ligands
Using the Chem Draw and tool in Chem Office-16, AGP structures was used to optimized the structures. Then, using the Chem3D tool, they were transformed into 3D structures and saved as .sdf or. mol files for later use. All the 2D chemical structures are represented in Fig. 4. The structures of all the compounds were depicted using ACD/ChemSketch and ChemBio3D Ultra 12.0 (www.cambridgesoft.com), which was also used to optimise ligand geometry and run MM2 energy minimization on the 3D structures [31]. The OpenBabel program was utilized to transform the ligands' three-dimensional (3D) structure from sdf to pdb file format [32].
Molecular docking simulation and visualisation
Molecular docking simulations were performed using Autodock v4.2.6/ Vina tool to investigate the binding interactions of ligands with protein Chain-A of NLRP 3.
Protein and ligand preparation involved the following steps:
Docking parameters included:
-
Grid box centered at 118.184, -170.692, -128.025 with spacing of 1.000 and dimensions of 62x96x66.
-
Lamarckian Genetic Algorithm (LGA) for optimization and stabilization of the protein structure.
Docking procedure:
Each ligand was docked thrice. The 10 conformations generated for each docking run were evaluated based on binding energy. The conformation with the lowest binding energy was selected as the final representative pose. Final poses were visualized as 2D diagrams using Biovia Discovery Studio Visualizer v19.1.0.18287 to illustrate ligand-protein interactions. To quantify the binding strength and inhibitory potential of the docked ligands, their inhibition constants (Ki) and inhibitory efficiencies (Ki^-1) were calculated using the following equations:
Equation 1: ΔG = -RTlnKi Eq. 2: Ki^-1 = e^(-ΔG/RT)
Where:
-
ΔG represents the binding energy derived from docking simulations.
-
R is the gas constant (1.987 x 10^-3 kcal/mol).
-
T is the absolute temperature (298.15 K).
Visual analysis of the docking results was performed using PyMol and Biovia Discovery Studio 2021 to gain further insights into the ligand-protein interactions.
Grid generation and Molecular docking analysis
Using AutoDock 4.2 and AutoDock Vina, all the chosen phytochemicals were docked into the binding site of the NLRP3 protein's Chain A, adhering to the protocol outlined by Morris et al. (1998) [42]. Using the modeling tool and visualizer in BIOVIA Discovery Studio, protein optimization was carried out as part of chain preparation by first eliminating water and other atoms and then adding a polar hydrogen group. The grid-based approach and blind docking were used to enable quick assessment of the conformational binding energy. The target proteins were placed into the grid box, which was created using AutoDock 4.2. While enclosing the active residues of the protein, the XYZ coordinates were changed as per the size of the protein. It guaranteed that ligand molecules and proteins would bind precisely in that defined area. The XYZ dimension was 60 × 90 × 66 and the grid spacing was 1 A°. The active site served as the grid's center, and the macromolecule's XYZ coordinates were 118.184 170.692 128.025A°. For docking, all other settings were left at their initial configurations. The values for exhaustiveness and energy range are 10 and 4, accordingly.
Pre-Docking process:
For parameterized optimization of the improved docking simulation in the AutoDock 4.2, the ligand and proteins were pre-processed. The partial kollman and gasteiger charges, which are based on electron density and improve computation accuracy, were added in order to optimize the protein (Gasteiger and Marsili 1980) [43]. After allocating AD4 type atoms, the parametrized pdb protein file was saved as a pdbqt file. While the ligand was optimized by choosing and detecting the root and futher saved to pdbqt file.
Enrichment and string network analysis for NLRP3 protein
In the pursuit of categorizing the cellular pathways associated with the NLRP3 protein, KEGG annotations were extracted from the KEGG database (https://www.genome.jp/kegg/pathway.html) [44]. The intention behind this retrieval was to systematically assess the amalgamation of interactive information emanating from NLRP3 and its affiliated proteins, thereby implying pathways entwined in diverse biological functions. For the exploration of potential interactions among genes linked to NLRP3, the Search Tool for Retrieval of Interacting Genes (STRING) database (https://string-db.org) was harnessed [45]. STRING, functioning as a predictive and integrative platform for protein–protein interactions, enabled the identification of functional relationships and intricate interactions between proteins. Employing the STRING tool facilitated the examination of potential gene interactions associated with NLRP3, revealing a network characterized by nodes, edges, average node degrees, protein–protein interaction (PPI) enrichment p-values, as well as average and local clustering coefficients. Notably, STRING not only served as a tool for elucidating gene interactions but also provided a comprehensive platform suggesting plausible biological processes, molecular functions, and cellular components associated with the candidate genes under scrutiny. This multifaceted analysis, derived from the STRING database, allowed for a nuanced understanding of the functional implications of NLRP3 and its associated proteins within cellular pathways. The integration of KEGG annotations and STRING database analyses contributes to a holistic exploration of the cellular pathways and interactive relationships involving NLRP3. This combined approach not only enriches our comprehension of the molecular landscape associated with NLRP3 but also provides valuable insights for further scientific inquiry and potential therapeutic interventions.
Atlas Map of Protein Contacts
The Protein Contacts Atlas (https://pca.mbgroup.bio) is an interactive web database for visualizing atomic-level interactions between ligands, molecules, proteins, and nucleic acids. It utilizes residue-residue interaction networks and computational modeling of crystal structures to enable multi-scale visualization of complexes. By precomputing structural data from experimental and predictive sources, this tool allows interactive exploration of contacts within and between biological molecules from proteins to small ligands.
Post dock computational ADME/T and Pharmacokinetics Studies
Drug-likeness and in silico ADMET studies
The SwissADME, ProTox 2[46, 47] online tool, along with the Molinspiration property engine, was used to provide computational predictions about the toxicity, pharmacokinetics, drug-likeness, and ADME of several drugs. Determined by [48] Lipinski’s rule of five, molecular weights (MW), hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), topological polar surface area (TPSA), octanol/water partition coefficient (LogP), and rotatable bond count (RB) were all determined through physicochemical analysis [48]. Results started to show up soon as the canonical smiles of all compounds were submitted to the Molinspiration, ProTox 2 and ADMET/SAR programmes. A CSV file containing the SwissADME database's results was downloaded. Mutagenic, tumorigenic, reproductively active, and irritating were listed as the expected toxicities [49]. The Molinspiration database (http://www.molinspiration.com) was used to forecast the bioactivities of phytochemicals. The substances' expected bioactivities included GPCR ligand, nuclear receptor ligand, kinase inhibitor, protease inhibitor, enzyme inhibitor, and ion channel modulator [50]. All ligands' gastrointestinal absorption and access to the brain were predicted using the BOILED-Egg graph (Brain Or Intestinal permeation method) [46, 47]. These characteristics are essential when looking for a new treatment, hence all ligands make ideal candidates for being possible medications (Figs. 12–14). The Swiss ADME service calculated the bioavailability radar to determine how closely the molecule resembled the medication. For ligands, lipophilicity (LIPO) ranged between 1.01 < XlogP3 < 2.03, whereas polarity (POLAR) fell between 54.56 and 111.46 TPSA, where TPSA stands for topological polar surface area.
Bioactivity Score and Bio-availability Radar
Ligand bioactivity scores were assessed using the Molinspiration software, available online at http://www.molinspiration.com/. Canonical SMILES of ligands sourced from PubChem were employed in this analysis. Properties scrutinized encompassed G-protein coupled receptors (GPCR), enzyme inhibitors (EI), kinase inhibitors (KI), nuclear receptor ligands (NRL), and ion channel modulators (ICM). Additionally, the bioavailability of the ligands was determined using Swiss ADME (http://www.swissadme.ch/index.php), which rapidly indicates whether a compound exhibits oral bioavailability. This integrated approach provides a comprehensive evaluation of ligand properties, essential for understanding their potential biological activities and therapeutic relevance.
Figure 5 The predicted secondary structure of the Chain A of NLRP3 using SOPMA software, whereas h: Alpha helix, e: Extended strand, t: Beta-turn and c: Random coil. The graphical representation of secondary elements in Chain A of NLRP3 protein (blue: alpha helix, red: extended, green: beta turn, yellow: random coil).
Result and discussion:
Molecular docking was first performed to predict protein-ligand interactions by minimizing ligand energy and calculating binding energies. Ligand binding inhibits the target enzyme, demonstrating the potential to alter its biochemical activity. Docking algorithms relate a drug's structure to its cytotoxicity by evaluating inhibitory and activator properties of the ligand with the receptor protein. In summary, molecular docking studies predicted inhibitory interactions between phytochemical ligands and target proteins to assess their potential biochemical effects. To perform the target protein analysis, Cryo-EM structure of NLRP3 bound to NEK complex (PDB ID: 6NPY) was were retrieved from PDB databank from https://www.rcsb.org/structure/6NPY. The cryo-electron microscopy (cryo-EM) structure provides structural insights into the NLRP3 inflammasome complex. The NEK7 were served as an HETATOM(heteroatom) and was removed along with the water molecule (Fig. 2). Chain A represents the NLRP3 protein component within the larger inflammasome assembly. To assess the structural quality and characteristics of Chain A, a Ramachandran plot was generated, which depicts the distribution of phi and psi torsion angles for each amino acid residue, allowing evaluation of the favored and unfavored conformations adopted by the protein backbone. Additionally, a hydrophobicity plot was constructed for Chain A of the NLRP3 protein, illustrating the varying degrees of hydrophobicity along the amino acid sequence, which can provide insights into potential protein folding patterns, solubility, and interactions with other biomolecules within the inflammasome complex (Fig. 3). Figure 4. represents all the 2D Structures of phytochemicals used in this study.
Secondary structure prediction
The secondary structure prediction of chain A in protein NLRP3 revealed a composition of 29.08% α-helix (h), 27.12% extended strand (e), 11.44% beta-turn (t), and 32.35% random coil (c) elements. The results are visualized through two graphs, one illustrating the prediction and the other displaying score curves for all predicted states (Fig. 5). Additionally, pertinent parameters such as window width and the number of states utilized for the prediction are presented, offering a comprehensive insight into the methodology employed for this structural analysis.
Protein Chain Validation
The protein model exhibited a Z-score within the range of native structures, validating its high quality as assessed by ProSA and Swiss- model analysis (Fig. 6 and Fig. 7). The QMEAN Z-Score of 0.72 for NLRP3 (Fig. 6A) indicates perfect modeled structures score. ProSA analysis of human NLRP3 yielded a Z-score of -10.22, validating overall quality by comparing total energy deviation to random configurations. The ProSA results (Fig. 6) plot acquired protein structures from various sources differentiated by color (Fig. 6A), allowing assessment of the modeled NLRP3 against experimental structures. These computational validations confirm the high quality of the homology models developed using SWISS MODEL.
Molecular docking analysis
All the compounds (alkaloids) were selected on the on the basis of their anti-inflammatory properties and were downloaded from Pubchem database. Prepared Chain A of NLRP3 protein were docked with all the selected alkaloids. The docking score of all the significant interactions (above ~ 10 kcal/mol) is shown in Table 3. All the compounds showed outstanding binding energies with the respective Chain A. Tranilast (Synthetic Positive Control) Oridonin and (Natural Positive Control) were taken as controls for the target as it has been currently used and have shown good efficacy as selective drugs against NLRP3 induced inflammasoms. The results of binding affinities, inhibition constant (Ki) and ligand efficiency (L.E) obtained are shown in Table 4. All the formula used in calculation of are present in the below section. In the present study, Oxyacanthine showed the highest binding affinity score of -15.2/mol followed by Berbamine − 13.9 kcal/mol. Similarly, Berberine and Neferine produced the least binding energy of − 10.4 kcal/mol. Among the drugs, Tranilast (Synthetic Positive Control) had the lowest energy of -10.3 kcal/mol whereas Oridonin (Natural Positive Control) showed the highest binding energy of -11.6 kcal/mol. All the amino acid interaction along with the type of bond are represented in Table 3 and Table 4.
Calculation for Inhibition Constant, binding energies and ligand efficiency
-
The inhibition constant (Ki) (µM/nM) for the ligands was calculated from the binding energy (ΔG) using the equation Ki (µM) = exp (ΔG/RT), where R is the gas constant (1.985 × 10 − 3 kcal/mol − 1 K − 1) and T is the temperature (298.15 K). This allowed prediction of Ki values for the ligand interactions from the computed binding energies.
• The Gibbs free binding energy was calculated using udergiven formula:
ΔG = − RTlnKi …… 1 equation.
Ki = e[−ΔGRT] …….2 equation.
where R = Gas constant (1.987 × 10–3 kcal/mol); T = 298.15 K (absolute temperature); Ki = Inhibition constant; ΔG = Binding energy (the binding free energy).
• The ligand efficiency was calculated using the under given formula:
Ligand Efficiency(LE) = − B.E ÷ Heavy atoms (H.A)
LE scale = 0.873e − 0.026× H.A − 0.064
FQ = LE ÷ LE scale
STRING protein network and KEGG enrichment analysis of NLRP3
The study employed KEGG analysis to explore the biological significance of the identified target and potential involvement of NLRP3 protein. Through protein-protein interaction (PPI) network analysis, a set of 19 key targets, including NEK 7, PYCARD, DHX33 along with GBPs (GTPase binding proteins) were identified through PPI network analysis. These targets were primarily associated with biological processes linked to NLRP3 and its associated protein. The KEGG enrichment analysis revealed signalling pathways closely linked to inflammation system, as illustrated in Fig. 8. The STRING protein interaction network of NLRP3 was analyzed using the STRING database. Specific evidence lines represented by colored links between nodes denote functional associations, with confidence indicated by internode distances per Bayesian scoring (Fig. 9). Co-expression analysis utilized gene correlation testing across large transcriptomic and proteomic datasets to infer functional relationships. Observed bovine gene co-expression is depicted in heatmaps (Fig. 10), where color intensity in triangle matrices indicates confidence of association between co-expressed proteins. Additionally, gene co-occurrence similarities provided computed protein similarity scores between organisms, with color intensity describing the degree of inter-protein similarity (Fig. 11A and 11B). This comprehensive examination enhances our understanding of the intricate interplay among these targets and their relevance to critical pathways implicated in both inflammation and its related systems.
Contacts Atlas of NLRP3 Chain A
The Protein Contacts Atlas tool provided atomic-level visualization of the NLRP3 protein complex. Nodes in the network represent nucleic acids or proteins, with links between nodes denoting interaction surfaces between subunits. Chord plots displayed secondary structure contacts in NLRP3 (Fig. 11A). Asteroid plots showed the residue/ligand-centric atomic neighbourhood (Fig. 11B). Scatter plots provided quantitative insights per residue (Fig. 11C). Together, these models enabled multi-scale study of the NLRP3 complex from secondary structure contacts to atomic interactions using network graph, asteroid plot, and scatter plot visualizations.
Table 2 List of phytochemicals and positive drug molecules used for docking studies.
Phytochemical
|
Canonical smiley
|
Molecular formula
|
Molecular weight (g/mol)
|
Oxyacanthine
|
CN1CCC2=CC(=C(C3=C2C1CC4=CC=C(C=C4)OC5=C(C=CC(=C5)CC6C7=CC(=C(C=C7CCN6C)OC)O3)O)OC)OC
|
C37H40N2O6
|
608.7
|
Neferine
|
CN1CCC2=CC(=C(C=C2C1CC3=CC=C(C=C3)OC)OC4=C(C=CC(=C4)CC5C6=CC(=C(C=C6CCN5C)OC)OC)O)OC
|
C38H44N2O6
|
624.8
|
Magnoflorine
|
C[N+]1(CCC2=CC(=C(C3=C2C1CC4=C3C(=C(C=C4)OC)O)O)OC)C
|
C20H24NO4+
|
342.4
|
Corynoline
|
CC12C(CC3=CC4=C(C=C3C1N(CC5=C2C=CC6=C5OCO6)C)OCO4)O
|
C21H21NO5
|
367.4
|
Columbamine
|
COC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC(=C(C=C4CC3)OC)O)OC
|
C20H20NO4+
|
338.4
|
Berberine
|
COC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC5=C(C=C4CC3)OCO5)OC
|
C20H18NO4+
|
336.4
|
Berbamine
|
CN1CCC2=CC(=C3C=C2C1CC4=CC=C(C=C4)OC5=C(C=CC(=C5)CC6C7=C(O3)C(=C(C=C7CCN6C)OC)OC)O)OC
|
C37H40N2O6
|
608.7
|
Palmatine
|
COC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC(=C(C=C4CC3)OC)OC)OC
|
C21H22NO4+
|
352.4
|
Tranilast
(Positive Control)
|
COC1=C(C=C(C=C1)C=CC(=O)NC2=CC=CC=C2C(=O)O)OC
|
C18H17NO5
|
327.3
|
Oridonin (Positive control)
|
C=C1C2CCC3C(C1=O)(C2O)C1(O)OCC23C(O)CCC(C2C1O)(C)C
|
C20H28O6
|
364.43
|
Table 3 Amino acids of Chain A of NLRP3 protein involved in interactions with selected alkaloids with their binding affinity.
Compounds
|
Interaction Type
|
Binding affinity
|
Interacting amino acid residues
|
Inhibition constant
|
Ligand Efficiency
|
Oxyacanthine
|
Pi-cation, Charge-charge
Positive-Positive
|
-15.2
|
GLU 375, HIS 724, ARG 172
|
0.1679
|
-5.06
|
Neferine
|
Pi-alkyl, alkyl, Conventional, Pi-stacked, Pi-Pi Donor, Pi cation, Carbon hydrogen, Charge-Charge
|
-10.4
|
ASP 662, ASN 656, GLU 629, VAL 353, PRO 352,
|
2.289
|
-2.08
|
Magnoflorine
|
Pi-Pi cation, Conventional, Positive-Positive
|
-11.3
|
ARG 378, ARG 172, PHE 210
|
1.004
|
-3.76
|
Corynoline
|
Pi-Pi cation, Conventional, Positive-Positive
|
-11.8
|
TRP 416, GLY 509, ARG 167
|
0.586
|
-3.93
|
Columbamine
|
Conventional, Positive-Positive
|
-10.4
|
ARG 167, SER 658
|
2.289
|
-5.2
|
Berberine
|
Carbon hydrogen, Pi-Pi cation, Positive-Positive
|
-10.4
|
THR 169, ARG 167, TRP 416
|
2.289
|
-3.46
|
Berbamine
|
Carbon hydrogen, Pi-Pi cation, Positive-Positive
|
-13.9
|
ARG 167, TRP 416, ILE 521
|
0.122
|
-4.63
|
Palmatine
|
Carbon hydrogen,
Positive-Positive
|
-10.8
|
ARG 167
|
0.975
|
-10.8
|
Tranilast
(Synthetic Positive Control)
|
Carbon hydrogen,
Positive-Positive, Pi-Pi cation
|
-10.3
|
TRP 416
THR 169
ARG 167
|
1.881
|
-3.43
|
Oridonin
(Natural Positive Control)
|
Conventional hydrogen,
Carbon hydrogen, Bump Conventional hydrogen bond
|
-11.6
|
ARG 167
|
0.300
|
-5.8
|
Table 5 Summary of predicted Molinspiration bioactivity score for all selected alkaloids as compared to standard.
Compounds
|
|
Bioactivity Score
|
|
GPCR ligand
|
Ion channel modulator
|
Kinase inhibitor
|
Nuclear receptor ligand
|
Protease inhibitor
|
Enzyme inhibitor
|
Oxyacanthine
|
-0.01
|
-0.61
|
-0.49
|
-0.56
|
-0.08
|
-0.32
|
Neferine
|
-0.11
|
-0.76
|
-0.55
|
-0.59
|
-0.09
|
-0.45
|
Magnoflorine
|
0.37
|
0.78
|
-0.20
|
-0.26
|
-0.19
|
0.26
|
Corynoline
|
0.28
|
0.04
|
-0.19
|
0.05
|
-0.03
|
0.09
|
Columbamine
|
-0.07
|
0.82
|
-0.19
|
-0.59
|
-0.30
|
0.90
|
Berberine
|
-0.86
|
-0.36
|
-1.14
|
-0.78
|
-1.29
|
-0.41
|
Berbamine
|
-0.01
|
-0.61
|
-0.49
|
-0.56
|
-0.08
|
-0.32
|
Palmatine
|
-0.10
|
0.74
|
-0.22
|
-0.65
|
-0.29
|
0.81
|
Tranilast
|
-0.14
|
-0.22
|
-0.22
|
-0.02
|
-0.25
|
-0.09
|
Oridonin
(Natural compound)
|
0.10
|
0.27
|
-0.19
|
0.73
|
0.08
|
0.53
|
Table 6 ADMET predicted profile for active compounds- Metabolism
Parameter
|
Corynoline
|
Magnoflorine
|
Neferine
|
Oxyacanthine
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridonin
|
GI absorption
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
BBB permeant
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
P-gp substrate
|
No
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
Yes
|
CYP1A2 inhibitor
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
No
|
No
|
No
|
CYP2C19 inhibitor
|
Yes
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
CYP2C9 inhibitor
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
Yes
|
No
|
CYP2D6 inhibitor
|
Yes
|
No
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
CYP3A4 inhibitor
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
Log Kp (skin permeation)
|
-6.62 cm/s
|
-6.44 cm/s
|
-5.35 cm/s
|
-5.51 cm/s
|
-5.94 cm/s
|
-5.87 cm/s
|
-5.51 cm/s
|
-5.79 cm/s
|
-6.02 cm/s
|
-8.47 cm/s
|
Table 7 ADMET predicted profile for active compounds- Toxicity
Target
|
Corynoline
|
Magnoflorine
|
Neferine
|
Oxyacanthine
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridinin
|
Hepatotoxicity
|
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Carcinogenicity
|
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Immunotoxicity
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Inactive
|
Inactive
|
Mutagenicity
|
Active
|
Inactive
|
Active
|
Active
|
Active
|
Active
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Cytotoxicity
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Aryl hydrocarbon Receptor (AhR)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Androgen Receptor (AR)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Androgen Receptor Ligand Binding Domain (AR-LBD)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Aromatase
|
Inactive
|
Inactive
|
Inactive
|
Inctive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Estrogen Receptor Alpha (ER)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Estrogen Receptor Ligand Binding Domain (ER-LBD)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Heat shock factor response element (HSE)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Mitochondrial Membrane Potential (MMP)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Phosphoprotein (Tumor Supressor) p53
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
ATPase family AAA domain-containing protein 5 (ATAD5)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Average similarity: 79.64%; Prediction accuracy: 69.26% for all the compounds.
Table 8 Drug likeness prediction for Lipinski’s rule of five of compounds
Compounds
|
Violation
|
mlogP
|
HBA
|
HBD
|
RB
|
TPSA
(Ų)
|
BA scores
|
Molar Refractivity
|
Oxyacanthine
|
1 violation: MW>500
|
6.24
|
8
|
1
|
3
|
72.87
|
0.55
|
181.60
|
Neferine
|
1 violation: MW>500
|
6.18
|
8
|
1
|
10
|
72.87
|
0.55
|
188.02
|
Magnoflorine
|
0 violation
|
-1.26
|
5
|
2
|
2
|
58.92
|
0.55
|
101.87
|
Corynoline
|
0 violation
|
2.81
|
6
|
1
|
0
|
60.39
|
0.55
|
100.81
|
Columbamine
|
0 violation
|
1.78
|
4
|
1
|
3
|
97.33
|
0.55
|
51.80
|
Berberine
|
0 violation
|
2.19
|
4
|
0
|
2
|
40.80
|
0.55
|
94.87
|
Berbamine
|
1 violation: MW>500
|
3.55
|
8
|
1
|
3
|
72.86
|
0.55
|
181.60
|
Palmatine
|
0 violation
|
2.01
|
4
|
0
|
4
|
40.80
|
0.55
|
101.80
|
Tranilast
|
0 violation
|
2.08
|
5
|
2
|
7
|
84.86
|
0.56
|
90.31
|
Oridonin
|
1 violation: WLOGP<-0.4
|
-1.22
|
7
|
5
|
5
|
119.61
|
0.55
|
71.78
|
Table 9 Lipophilicity and water solubility of all the alkaloids in comparison to standard drugs.
Lipophilicity
|
Oxyacanthine
|
Neferine
|
Magnoflorine
|
Corynoline
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridonin
|
Log Po/w (iLOGP)
|
4.69
|
5.21
|
-0.66
|
3.41
|
-0.07
|
-0.00
|
4.92
|
0.10
|
2.68
|
1.98
|
Log Po/w (XLOGP3)
|
6.34
|
6.70
|
2.74
|
2.70
|
3.42
|
3.62
|
6.34
|
3.75
|
3.20
|
0.08
|
Log Po/w (WLOGP)
|
5.45
|
5.35
|
2.31
|
1.65
|
3.08
|
3.10
|
5.45
|
3.38
|
2.75
|
0.38
|
LogPo/w (MLOGP)
|
3.55
|
3.46
|
-1.71
|
2.20
|
1.78
|
2.19
|
3.55
|
2.01
|
2.08
|
0.86
|
Log Po/w (SILICOS-IT)
|
5.50
|
6.64
|
0.59
|
2.96
|
3.43
|
3.74
|
5.50
|
3.97
|
2.71
|
1.24
|
Consensus Log Po/w
|
5.11
|
5.47
|
0.65
|
2.58
|
2.33
|
2.53
|
5.15
|
2.64
|
2.69
|
0.91
|
Water Solubility
|
|
|
|
Log S (ESOL)
|
-7.80
|
-7.66
|
-3.91
|
-4.15
|
-4.37
|
-4.55
|
-7.80
|
-4.58
|
-3.79
|
-2.15
|
Solubility
|
9.54e-06 mg/ml ; 1.57e-08 mol/l
|
1.36e-05 mg/ml ; 2.18e-08 mol/l
|
4.19e-02 mg/ml ; 1.22e-04 mol/l
|
2.61e-02 mg/ml ; 7.12e-05 mol/l
|
1.45e-02 mg/ml ; 4.28e-05 mol/l
|
9.53e-03 mg/ml ; 2.83e-05 mol/l
|
9.54e-06 mg/ml ; 1.57e-08 mol/
|
9.30e-03 mg/ml ; 2.64e-05 mol/l
|
5.27e-02 mg/ml ; 1.61e-04 mol/l
|
2.58e+00 mg/ml ; 7.08e-03 mol/l
|
Class
|
Poorly soluble
|
Poorly soluble
|
soluble
|
Moderately soluble
|
Moderately soluble
|
Moderately soluble
|
Poorly soluble
|
Moderately soluble
|
Soluble
|
Soluble
|
Bioactive and bioavailability score
Table 5 showed the Molinspiration bioactivity predicted score of all the selected alkaloids. Predicted data suggests that this class of nitrogenous organic compounds possesses diverse potential biological activities. Notably, they may interact with G protein-coupled receptors (GPCRs), modulate ion channels, inhibit kinases, bind strongly to nuclear receptors, mildly inhibit proteases, and moderately inhibit enzymes. These predictions offer valuable insights into the compounds' potential pharmacological effects and pave the way for further experimental investigations into their specific biological actions. Our results highlight five natural compounds – Magnoflorine, Corynoline, Columbamine, Palmatine, and Oridonin – as potential NLRP3 protein interactors. The evaluated scores indicate that values greater than 0.00 correspond to high activity, scores ranging from − 0.5 to − 0.00 suggest moderate activity, and scores lower than − 0.5 imply inactivity [51]. Out of all the ligands, Magnoflorine, Corynoline, Columbamine Palmatine, Tranilast, Oridonin (Natural compound) were found to have good bioactivity scores. For GPCR and ICM Magnoflorine showed the highest score of 0.37 and 0.78, respectively; palmatine and columbamine showed the highest score of 0.82;0.74 for ICM and 0.90;0.81 for EI; Corynoline had the highest score of 0.28 for GPCR. The remaining scores are represented in the table, which indicates the scoring profile for all the ligand compounds. Out of the two selected Standard drug Oridonin indicate the most significant bioactive score for ICM, NR and EI. Their predicted and comparable bioactivity scores to control compounds suggest that these phytochemicals could serve as alternative or adjuvant therapeutic agents for NLRP3-related inflammatory diseases.
Table 5
Summary of predicted Molinspiration bioactivity score for all selected alkaloids as compared to standard.
Compounds
|
Interactions Type
|
Binding Pocket
|
3-D ligand interaction
|
Oxyacanthine
|
Pi-cation, Charge-charge
Positive-Positive
|
|
|
Neferine
|
Pi-alkyl, alkyl, Conventional, Pi-stacked, Pi-Pi Donor, Pi cation, Carbon hydrogen, Charge-Charge
|
|
|
Magnoflorine
|
Pi-Pi cation, Conventional hydrogen, Positive-Positive
|
|
|
Corynoline
|
Pi-Pi cation, Conventional hydrogen, Positive-Positive
|
|
|
Columbamine
|
Conventional hydrogen, Positive-Positive
|
|
|
Berberine
|
Carbon hydrogen, Pi-Pi cation, Positive-Positive
|
|
|
Berbamine
|
Carbon hydrogen, Pi-Pi cation, Positive-Positive
|
|
|
Palmatine
|
Carbon hydrogen,
Positive-Positive
|
|
|
Tranilast
(Synthetic Positive Control)
|
Carbon hydrogen,
Positive-Positive, Pi-Pi cation
|
|
|
Oridonin
(Natural Positive Control)
|
Conventional,
Carbon hydrogen, Bump Conventional hydrogen
|
|
|
By evaluating all these parameters, a bioavailability radar profile was predicted and the graphical output for all the alkaloid compounds are presented in Fig. 12 This radar plot helps in assessing the viability of new drug candidates. For a phyto-compound to be considered drug-like, its radar plot must fall within the optimal pink area across all parameters. Two essential determinants of bioavailability are flexibility and polarity. Compounds with over 10 rotatable bonds, indicating high flexibility, are predicted to have low oral bioavailability. Compounds with a topological polar surface area between 20–130 Å2 have optimal polarity for high oral bioavailability. By assessing flexibility and polarity along with other key parameters, bioavailability radars can effectively predict the oral absorbability of novel compounds in a drug discovery pipeline. Based on radar plot data all the compound seems to fulfil the minimum criteria to be orally bioavailable (Fig. 12).
Drug-likeness and in silico ADMET studies
In silico pharmacokinetic analysis revealed the phytochemicals have favorable drug-likeness and ADMET properties comparable to standard drugs, including high gastrointestinal bioavailability but low blood-brain barrier permeability as predicted by ADMET modelling (Fig. 12) (Data are tabulated for metabolism and toxicity in Table 6 and Table 7). All the alkaloids Corynoline, Columbamine, Berberine, Palmatine and Tranilast satisfied all of Lipinski's criteria, while other alkaloids deviated in at most one parameter, still in the acceptable range (Table 8). Estimated solubility revealed the phytochemicals as moderately less soluble than the standards, except Oridonin which showed exceptionally high solubility high gastrointestinal absorption (GIA) and normal bioavailability scores were predicted for the ligands (Fig. 13). Importantly, Corynoline, Oxyacanthine, Columbamine, Berberine, Neferine and Tranilast were identified as non-substrates of p-glycoprotein, making them preferable for cancer treatment by avoiding efflux [52, 53]. Regarding cytochrome P450 inhibition, the phytochemicals showed a balance of inhibitors/non-inhibitors of CYP3A4 and CYP1A2, minimizing risks of undesirable metabolic drug interactions [54]. In summary, in silico screening predicted that these alkaloid phytochemical compounds have favourable pharmacokinetics and ADME properties, including solubility, permeability, bioavailability, and limited interactions with clearance mechanisms, supporting their potential as therapeutic agents.
Table 6
ADMET predicted profile for active compounds- Metabolism
Compounds
|
|
Bioactivity Score
|
|
GPCR ligand
|
Ion channel modulator
|
Kinase inhibitor
|
Nuclear receptor ligand
|
Protease inhibitor
|
Enzyme inhibitor
|
Oxyacanthine
|
-0.01
|
-0.61
|
-0.49
|
-0.56
|
-0.08
|
-0.32
|
Neferine
|
-0.11
|
-0.76
|
-0.55
|
-0.59
|
-0.09
|
-0.45
|
Magnoflorine
|
0.37
|
0.78
|
-0.20
|
-0.26
|
-0.19
|
0.26
|
Corynoline
|
0.28
|
0.04
|
-0.19
|
0.05
|
-0.03
|
0.09
|
Columbamine
|
-0.07
|
0.82
|
-0.19
|
-0.59
|
-0.30
|
0.90
|
Berberine
|
-0.86
|
-0.36
|
-1.14
|
-0.78
|
-1.29
|
-0.41
|
Berbamine
|
-0.01
|
-0.61
|
-0.49
|
-0.56
|
-0.08
|
-0.32
|
Palmatine
|
-0.10
|
0.74
|
-0.22
|
-0.65
|
-0.29
|
0.81
|
Tranilast
|
-0.14
|
-0.22
|
-0.22
|
-0.02
|
-0.25
|
-0.09
|
Oridonin
(Natural compound)
|
0.10
|
0.27
|
-0.19
|
0.73
|
0.08
|
0.53
|
Table 7
ADMET predicted profile for active compounds- Toxicity
Parameter
|
Corynoline
|
Magnoflorine
|
Neferine
|
Oxyacanthine
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridonin
|
GI absorption
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
High
|
BBB permeant
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
P-gp substrate
|
No
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
Yes
|
CYP1A2 inhibitor
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
No
|
No
|
No
|
CYP2C19 inhibitor
|
Yes
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
CYP2C9 inhibitor
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
No
|
Yes
|
No
|
CYP2D6 inhibitor
|
Yes
|
No
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
CYP3A4 inhibitor
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
Log Kp (skin permeation)
|
-6.62 cm/s
|
-6.44 cm/s
|
-5.35 cm/s
|
-5.51 cm/s
|
-5.94 cm/s
|
-5.87 cm/s
|
-5.51 cm/s
|
-5.79 cm/s
|
-6.02 cm/s
|
-8.47 cm/s
|
Target
|
Corynoline
|
Magnoflorine
|
Neferine
|
Oxyacanthine
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridinin
|
Hepatotoxicity
|
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Carcinogenicity
|
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Immunotoxicity
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Active
|
Inactive
|
Inactive
|
Mutagenicity
|
Active
|
Inactive
|
Active
|
Active
|
Active
|
Active
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Cytotoxicity
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Aryl hydrocarbon Receptor (AhR)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Androgen Receptor (AR)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Androgen Receptor Ligand Binding Domain (AR-LBD)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Aromatase
|
Inactive
|
Inactive
|
Inactive
|
Inctive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Estrogen Receptor Alpha (ER)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Estrogen Receptor Ligand Binding Domain (ER-LBD)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Active
|
Inactive
|
Inactive
|
Heat shock factor response element (HSE)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Mitochondrial Membrane Potential (MMP)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Phosphoprotein (Tumor Supressor) p53
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
ATPase family AAA domain-containing protein 5 (ATAD5)
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Inactive
|
Average similarity: 79.64%; Prediction accuracy: 69.26% for all the compounds. |
Table 8
Drug likeness prediction for Lipinski’s rule of five of compounds
Compounds
|
Violation
|
mlogP
|
HBA
|
HBD
|
RB
|
TPSA
(Ų)
|
BA scores
|
Molar Refractivity
|
Oxyacanthine
|
1 violation: MW > 500
|
6.24
|
8
|
1
|
3
|
72.87
|
0.55
|
181.60
|
Neferine
|
1 violation: MW > 500
|
6.18
|
8
|
1
|
10
|
72.87
|
0.55
|
188.02
|
Magnoflorine
|
0 violation
|
-1.26
|
5
|
2
|
2
|
58.92
|
0.55
|
101.87
|
Corynoline
|
0 violation
|
2.81
|
6
|
1
|
0
|
60.39
|
0.55
|
100.81
|
Columbamine
|
0 violation
|
1.78
|
4
|
1
|
3
|
97.33
|
0.55
|
51.80
|
Berberine
|
0 violation
|
2.19
|
4
|
0
|
2
|
40.80
|
0.55
|
94.87
|
Berbamine
|
1 violation: MW > 500
|
3.55
|
8
|
1
|
3
|
72.86
|
0.55
|
181.60
|
Palmatine
|
0 violation
|
2.01
|
4
|
0
|
4
|
40.80
|
0.55
|
101.80
|
Tranilast
|
0 violation
|
2.08
|
5
|
2
|
7
|
84.86
|
0.56
|
90.31
|
Oridonin
|
1 violation: WLOGP<-0.4
|
-1.22
|
7
|
5
|
5
|
119.61
|
0.55
|
71.78
|
Table 9
Lipophilicity and water solubility of all the alkaloids in comparison to standard drugs.
Lipophilicity
|
Oxyacanthine
|
Neferine
|
Magnoflorine
|
Corynoline
|
Columbamine
|
Berberine
|
Berbamine
|
Palmatine
|
Tranilast
|
Oridonin
|
Log Po/w (iLOGP)
|
4.69
|
5.21
|
-0.66
|
3.41
|
-0.07
|
-0.00
|
4.92
|
0.10
|
2.68
|
1.98
|
Log Po/w (XLOGP3)
|
6.34
|
6.70
|
2.74
|
2.70
|
3.42
|
3.62
|
6.34
|
3.75
|
3.20
|
0.08
|
Log Po/w (WLOGP)
|
5.45
|
5.35
|
2.31
|
1.65
|
3.08
|
3.10
|
5.45
|
3.38
|
2.75
|
0.38
|
LogPo/w (MLOGP)
|
3.55
|
3.46
|
-1.71
|
2.20
|
1.78
|
2.19
|
3.55
|
2.01
|
2.08
|
0.86
|
Log Po/w (SILICOS-IT)
|
5.50
|
6.64
|
0.59
|
2.96
|
3.43
|
3.74
|
5.50
|
3.97
|
2.71
|
1.24
|
Consensus Log Po/w
|
5.11
|
5.47
|
0.65
|
2.58
|
2.33
|
2.53
|
5.15
|
2.64
|
2.69
|
0.91
|
Water Solubility
|
|
|
|
Log S (ESOL)
|
-7.80
|
-7.66
|
-3.91
|
-4.15
|
-4.37
|
-4.55
|
-7.80
|
-4.58
|
-3.79
|
-2.15
|
Solubility
|
9.54e-06 mg/ml ; 1.57e-08 mol/l
|
1.36e-05 mg/ml ; 2.18e-08 mol/l
|
4.19e-02 mg/ml ; 1.22e-04 mol/l
|
2.61e-02 mg/ml ; 7.12e-05 mol/l
|
1.45e-02 mg/ml ; 4.28e-05 mol/l
|
9.53e-03 mg/ml ; 2.83e-05 mol/l
|
9.54e-06 mg/ml ; 1.57e-08 mol/
|
9.30e-03 mg/ml ; 2.64e-05 mol/l
|
5.27e-02 mg/ml ; 1.61e-04 mol/l
|
2.58e + 00 mg/ml ; 7.08e-03 mol/l
|
Class
|
Poorly soluble
|
Poorly soluble
|
soluble
|
Moderately soluble
|
Moderately soluble
|
Moderately soluble
|
Poorly soluble
|
Moderately soluble
|
Soluble
|
Soluble
|
Water solubility and lipophilicity
The in-silico predictions indicate the phytochemical compounds have a wide range of lipophilicity (log Po/w 0.65–5.47) but are poorly water-soluble (log S -2.15 to -7.80) (Table. 9). While the high lipophilicity may improve permeation into cells, it could also lead to solubility issues and potential toxicity. The compounds seem to violate Lipinski's guidelines for lipophilicity, which may negatively impact oral absorption and bioavailability. However, the poor aqueous solubility suggests that specialized formulations like prodrugs or nanocarriers may be required to enable parenteral delivery. In summary, the high lipophilicity but low solubility presents both challenges and opportunities in developing the phytochemicals as therapeutic agents, necessitating innovative solutions to improve their delivery while retaining bioactivity.