3D-QSAR, E-pharmacophore and molecular docking to explore substituted sulfonamides as carbonic anhydrase inhibitors in epilepsy

Arti Gupta^1,3, Viney Lather¹, Dushyanth R. Vennapu², Sandeep Kumar⁴ , Neerupma Dhiman¹, Archana Sharma¹*

¹Amity Institute of Pharmacy, Amity University, Noida-201310. India.

²KLE University College of Pharmacy, Belagavi, India.

³Lloyd School of Pharmacy, Plot No.3, Knowledge Park-II, Greater Noida

⁴School of Pharmacy, Sharda University, Greater Noida, India.

*Corresponding Author E-mail: asharma22@amity.edu

ABSTRACT:

Background- A series of aromatic sulfonamides incorporating coumarin as a lead were designed a for epilepsy target. Carbonic anhydrase is an influential target for the expansion of lead to treat epilepsy. Experimentally known carbonic anhydrase determents were identified to develop ligand based pharmacophore for anticonvulsant model. The X-ray crystallographic make-up of carbonic anhydrases with several inhibitors were utilized to develop ten energy optimized structure based (E- pharmacophore model). Pharmacophore matched candidates were utilized for docking to reclaim hits with scaffolds. The molecules having diverse structures, high docking score and low binding energy for various crystal structures of carbonic anhydrase were selected as final hits (leads). DFT is utilized to get electronic features of hits. The docking study of ligands by discovery studio had helped to establish binding interactions. The known carbonic anhydrase was reused for the development of pharmacophore hypothesis DHHRR. Based on Insilco process we came across structurally diverse hits as noncompetitive carbonic anhydrase inhibitors with better ADME. The best three hits 4, 6 and 17 were nontoxic and were selective carbonic anhydrase inhibitors with the IC_{50 values} respectively (IC₅₀ 2.01, 2.59, 2.469). The study describes that the combined pharmacophore appeal to identify various hits which have good binding affinity for the active site of enzyme in all feasible bioactive conformations.

KEYWORDS: Sulfonamides, Coumarin, 3D- QSAR, E-Pharmacophore, Epilepsy and Central Nervous System. (ESI- Electronic supporting Information).

INTRODUCTION:

The purpose of carbonic anhydrase (CA) in epilepsy had been acknowledged. The enzyme is abundant in the brain tissue, mainly in the cytoplasm and membrane of glial cells as well as in the myelin derived from oligo-dendrocytes^1–4. The CA helps in maintaining the equilibrium in the neurological activity through the neuron-glia metabolic process. Carbonic anhydrase is involved in the rapid interconversion of H₂O and CO₂ into H+ and HCO₃- ion (or vice versa). Zinc ion is mainly present in the active site of CA which maintains

acid base balance in blood and other tissues and remove CO₂ out of tissues. Inhibition of enzyme CA increases the H+ ion concentration intracellularly and decreases the pH. The K⁺ shift to the extracellular compartment so as to buffer the acid-base stability due to which hyperpolarisation and seizure threshold of the cell increases. Hence It accelerates the hydration of CO₂ which felicitate the hydrogen and bicarbonate ions exchange process with the sodium and chloride ions respectively, inside the glial cell membrane^1–3. The CA inhibitors (Acetazolamide, Methazolamide, Topiramate, Zonisamide, and Sulthiame) have been extensively used to treat epilepsy^4–7. However there is a need for an agent that can bind to CA and show significant on-target selectivity, possess fewer side effects such as (pH change, neuronal transmission, action on excitatory receptor) as compared with conventional CA inhibitors². Recently, the coumarin nucleus has been identified as a good candidate for CA inhibition pharmacophore described in (Figure 1). The coumarin nucleus provides the hydrophobic interaction with the CA enzyme as shown in (Figure 2). ^8–11

Figure 1: Coumarin Pharmacophore

In order to make the coumarin nucleus more effective against CA enzymes, we are trying to design the fused nucleus of the coumarin with the sulfonamide, which will be an ideal candidate for the CA inhibition activity[9]. The hydrophilic interaction of the hoped scaffold is expected for the sulfonamide, while the hydrophobic interaction of the coumarin is expected for the complete activity[12–14]. The present study is based on this scaffold hopping hypothesis.

Figure 2: Sulfonamide hopped Coumarin pharmacophore

Drug discovery overall is tedious and cost-consuming process so computational techniques helps to identity relevant lead and nucleus for the target^15–17. Ligand based methods such as QSAR and E- pharmacophore modeling is applied when a set of ligand molecule is known and very less or no information is available for target^18,19. The pharmacophore model can be used to identify possible interaction of the drug with drug metabolizing enzyme by matching the equivalent chemical group of test molecule to those of drug molecules with a well-known ADME-toxicity profile. This work combines 3D-QSAR and E-pharmacophore based concept for virtual screening of carbonic anhydrase inhibitors as sulfonamide databases^20–25. In-vitro studies for carbonic anhydrase^26–30 were approved and validated with In-Silico study.

METHODOLOGY:

Ligand Preparation:

All the scaffold hoped hit molecules were sketched using Chemdraw 19.0. All the hit molecules in CDX format are converted into SDF and PDB format using Open Babel Graphical User Interface (OBGUI). The ligands in PDB format are retrieved into the workspace of Biovia Discovery studio Client 20.0.

Protein Preparation:

The Protein (1DMY) enzyme downloaded from RCSCB Protein Data Bank, the protein preparation was carried out using SPDB Viewer. Chain A used as reference and other chains were removed from the core structure of the enzyme The Grid co-ordinates of the protein were assigned by selecting the co-crystal ligand with the aid of Biovia’s Discovery Studio’s Visualizer. Residue adjustment and removal of waters away from the active site and protein structure minimization etc were done. The optimized protein reported was validated using Ramachandran plot and Hydrophobicity Plot.

Development of Flexible docking by C-Docker of Biovia Discovery Studio Client 20.0.

The site specific docking of designed hit molecules was performed by docking the hit molecules onto the active site of the receptor using C-Docker (Charm Docker) protocol of Biovia Discovery studio Client 20.0. In this docking protocol both the receptor and hit molecules were flexible and simulated using charm force field.

Docking:

The docking studies were carried out by fused coumarin and sulfonamide nucleus derivatives with CA (V) isoform as a target enzyme. The ligands used in the study are depicted in (ESI, Table 1) with their interacting amino acids in the protein. The software used for molecular docking of compounds was Biovia Discovery studio Client 20.0 for docking-based virtual screening. The docking protocol is cross validated using open source software by extra precision (XP) docking mode in which the ligands were flexible and the receptor were rigid except active site.

a) Molecular docking studies

All molecules were docked in the active site of the enzyme 1DMY. 1DMY a complex between murine mitochondrial carbonic anhydrase V and 5-Acetamido- 1, 3, 4-Thiadiazole-2-Sulfonamide ligand binds Thr 200, Thr 199, and Tyr 131 amino acid residues. The crystal structure showed antiepileptic properties when binds with amino acid residues Thr 200 of 1DMY.

b) 3D- QSAR Modelling

3D QSAR was performed using VLife 3D QSAR (VLife, 2002) installed on Core 2 Duo workstation. Three dimensional structures were drawn and molecular geometry optimized with Monte Carlo conformational search, Merck molecular force field MMFF and charges. These parameters help to find out various inferential interactions, which comes out as a set of the results through all possible conformational suitability. The cordial interactions are generally mapped with the structural coordinate to explore all pharmacophore feature if they lead to a better conclusion. The special attention of the conformational changes makes the 3D QSAR a better predictable model.

c) 3D-QSAR Model generation

For the study, MF analysis and PLS regression technique is used for generating 3D-QSAR model. Physicochemical properties, as steric and electronic descriptors as independent variable whereas biological activity (PIC50) as dependent variable. Statistically data validation is done by classifying dataset into training and test set. A dataset of 85 compound were selected out of which 80 percent will act as training set and randomly 20 percent as test set so as to maintain diversity of training set as descriptor for whole dataset.^9,31–34.

RESULT AND DISCUSSION:

Screening of Pharmacophore from hits to leads:

The scaffold hopped pharmacophore hits were designed to elucidate the augmented pharmacological activity as antiepileptic agents. In the process of development of newer leads it is always advisable to screen the hits prior to synthesis so that toxicity of the compound can also be expected which will decrease the economic burden in Drug development process.

18 Scaffold hopped hits were filtered using BIOVIA’S Discover Studio ADMET and Swiss ADME models (Swiss Admech) detail is mentioned in (ESI, Figure 1). All the molecule possesses optimum cell permeability and best fit into the criteria of absorption^35,36. The designed hits were screened for pharmacokinetic profiling/absorption studies and all the designed hits abide the optimum cell permeability following basis PSA < 140 Å2 and AlogP98 < 5.

The molecular properties of designed hit molecules i.e. variations to Lipinski and veber filter properties indicated that all the scaffold hoped hit molecules with little or no variations or drug likeness with highest amount of synthetic feasibility and lead likeness feature^37–41. Hydrophobicity and Ramachandran plot of 1DMY described (ESI, Figure 2) is used to determine protein structural analysis.

Site specific Docking of Designed Hits:

All the designed scaffold hopped pharmacophore hits were screened inside the active site of the 1DMY and ranked based on its score of the binding interaction. The validation of docking was done by redocking the co-crystal ligand with an RMSD of 0.24 A⁰as shown in (Figure 3).

Validation of Flexible docking by C-Docker of Biovia Discovery Studio Client 20.0.

In this docking protocol both the receptor and hit molecules were flexible and simulated using charm force field^42,43.

(a)

(b)

Figure 3(a) 3-D image for lead validation on pharmacophore (b) 3-D image for validation of docking

Validation was performed with Discovery studio client 2020.

Rigid Docking analysis using Discovery studio client.

All the designed scaffold hits were docked using rigid docked protocol of discovery studio The active site and docking protocol are validated using co-crystallised ligands as well as the marked substrate Acetazolamide (AZM), for the docking pose alignment. The non-bonding interactions of designed best hit molecules onto the active site within 6A⁰ are highlighted in (Figure. 4). The analysis of results was done on the basis of the specific interaction. The research was further taken for an in-silico optimization study^44–46 which predicts the quantification of the compound's docking results

Charm-Docking C-Docker Non-Bonding Interactions in 6 A⁰ active site of 1DMY

Validation of docking is done by viewing the protein interaction with ligand in terms of hydrogen bond interactions⁴⁷ in (Figure 4) and for rest of the molecules in ESI (Figure 3).

Figure 4: Non- Bonding interactions of Hits onto Active Site 1DMY

a) Docking Validation

Since discovery studio uses a stochastic search algorithm, several docking runs were performed and the best docking result among all runs are compiled in Table 1 which displays various conformer’s modes of different title compounds (1-18) with binding affinities and interactions with the target active site. The interactions with amino acids represent the conformational search part of the docking process and binding energy represents the scoring part of it. The intermolecular interaction between ligand and target 1DMY was confirmed by a docking study. Compounds 4 and 6 and 17 showed binding interactions with important amino acid residues such as Thr 200 is highlighted (Figure 5, 6 and 7). The compounds reflected similar binding interaction as shown by the crystal ligand of 1DMY.

Table-1 Docking Validation

S.No	LIGAND	BINDING ENERGY	INTERACTION
1	1	-6.8	GLN67*
2	2	-7.9	GLN67*
3	3	-7.7	THR200*
4	4	-9.5	THR200, THR62
5	5	-9.2	LYS91, THR62
6	6	-9.6	THR200, LYS91
7	7	-8.2	THR200, PHE65
8	8	-7.7	PRO201, HR200
9	9	-8.7	GLN67, GLU69
10	10	-9.2	GLN67, GLN92, THR 200,
11	11	-8.8	GLN67*
12	12	-8.9	THR200, SN139
13	13	-8.2	LEU84, GLN67, GLU69
14	14	-8.9	GLN92*
15	15	-8.8	GLU69, GLN67,
16	16	-7.8	GLN67*
17	17	-9.7	THR200
18	18	-9.1	THR200*

b) 3D QSAR Modelling

This is a widely used technique to get optimized Pharmacophore using various statistical regression methods. The algorithm of 3D QSAR is based on the essential interaction (i.e. steric or electrostatic) of any structure/ ligand to the target^48–50. This is the appropriate method to develop a ligand-based model for the optimized activity, based on their best interactive residual amino acid and the prone functional group. The Pharmacophore models were constructed by selecting five pharmacophore sites as given in Table 2 and 3.

The CPHs result analysis revealed specific suitability for only one type of DHHRR. The analysis was conducted through the vigorous site parameter (site, vector, and volume) study. Reference relative conformational energy (kJ/mol) had been aligned with-in the score, and ligand activity, which was expressed further as pIC₅₀, was included with a default weight. The Hypothesis model was rigorously screened for the modelling purpose and the result analysis found DHHRR.15 was selected for further study. The DHHRR 15 was selected based on the highest survival score as listed (Table 2) as well as the best scoring statistical parameters, which was generated from pharmacophore-based 3D-QSAR models.

The predictive capability of each model was examined through the test set compounds. In which, the more R² value (0.72) reflects that the model has strong significance. To find the excellence of the developed 3D-QSAR model established on the highest scoring pharmacophore hypothesis over the rest of the pharmacophore hypotheses, a 3D-QSAR model was established from the least scoring pharmacophore hypothesis DHHRR.30 and pharmacophore model developed as shown in (ESI, Figure 5). An outline of the statistical data for 3D-QSAR analysis of the DHHRR.15 hypotheses enlisted (Table 3).

Different statistical specifications as R², Q², SD, and RMSE were considered to check the robustness of the 3D-QSAR models and the model with four PLS component was found to be the best. The Q² (Q² = 0.8175) together with high Pearson R-value (Pearson-R = 0.8857) and R² (R² = 0.9334), reflects association between predicted and actual IC₅₀ activity values. As the difference between R² and Q² is (0.9334–0.8175 = 0.1159) the model is considered to be true and reliable⁵¹.

The Validation or the authentication of the QSAR process were done as the result is given in Table 2 and 3, which was further utilized to get the IC₅₀ and PIC₅₀values, explained in (ESI, Table 1). Based on the IC₅₀ observed and predictedvalues of the compounds, it was clearly understood that the earlier active compounds (4, 6, and 17) got again attention for the better activity. The active compounds should have a low concentration value to inhibit 50% of the enzyme concentration^10,52. The analysis of the interaction also found a peculiar interaction of the active compounds with the enzyme which is given below in the image, in the form of drug and receptor interaction. The compounds (4, 6, and 17) were found to be good candidates based on binding energy as well as IC₅₀values. The results were also rationalized through the interaction study and found that the compound with more hydrogen bonding interaction and specific interaction, which gives evidence of the best fit compounds with all in-silico activity. E-Pharmacophore Modelling 1DMY non-bonded Interactions and 10 Pharmacophores Generated and ligand profiler is also described in (ESI, Figure 4 and 6).

Validation of the QSAR model

The best way to validate any method is by comparison and correlation. The QSAR model was assessed by the various statistical measurements, like

1) Number of observation (n = 21)

2) No. of descriptors (V= 3)

3) Cross validated r2 (q2 = 0.9327)

4) Predicted r2 for test set (pred_r2 = 0.9587).

Figure 5: Interaction of Compound 4 with amino acid residues THR62, THR200

TABLE 2: Results of Hypothesis Generated (Pharmacophore site analysis result)

S.NO.	CPHs	sd	r- Squared	Stability	q- Squared	Pearson- r
1	DHHRR.15	0.2132	0.9334	0.6158	0.8175	O.8857
2	DHHRR.18	0.2160	0.8342	0.5986	0.8182	0.8560
3	DHHRR.20	0.1962	0.7896	0.4332	0.7869	0.7886
4	DHHRR.25	0.2193	0.7342	0.4112	0.7123	0.7408
5	DHHRR.30	0.1934	1.8182	0.4520	0.6581	0.6614

TABLE 3: Statistical Results of 3D QSAR Model

S.No.	Cphs	Survival	Survival Inactive	Site	Vector	Volume	Matches
1	DHHRR.15	2.830	1.932	0.27	0.918	O.668	5
2	DHHRR.18	2.810	1.571	0.25	0.917	0.665	5
3	DHHRR.20	2.762	1.305	0.23	0.856	0.661	5
4	DHHRR.25	2.540	1.629	0.21	0.641	0.440	5
5	DHHRR.30	2.320	1.508	0.20	0,459	0.330	5

Figure 6: Interaction of Compound 6 with amino acid residues THR200

Figure 7: Interaction of Compound 17 with amino acid residues THR200, AZM400

Quantitative Structure Toxicity Relationship of designed hits:

The designed scaffold hoped hit molecules were assessed for their toxicophoric features using TOPKAT toxicity prediction module of Biovia Discovery Studio Client 20.0. In comparison with the existing antiepileptic drugs all the hit molecules possess minimum toxicological features enlisted in (ESI, Table 2).

Cross Validation using DFT studies:

In depth analysis of interaction between designed ligand hits and target protein is cross validated by studying the electronic properties of the designed hits. HOMO LUMO bond gap and Ionization Potential/ Electrophilicity index of designed hits predicts the capability of hits to undergo interaction with desired protein target. Conventionally HOMO of hit ligands interact with LUMO of the protein, the ligands with higher electrophilicity index interacts with pronounced binding with the target protein as shown in (ESI, Figure 7).

CONCLUSION:

A dataset of 18 molecules containing sulfonamide moiety was subjected for carbonic anhydrase inhibition activity through the well-validated in-silico (pharmacophore-based 3D-QSAR and molecular docking) study. 85 ligands selected from literature with reported IC-50 values were used in creating 3D QSAR model. The models were validated internally, converting few molecules into test set and remaining into training set. All the molecules with their inhibitory activities were correlated with the previously reported literature. The antiepileptic activities expressed in terms of IC₅₀ were then converted to the IC_{50 predicted} (−log IC₅₀). The best-fitted model were used to predict the biological activities of training and test set molecules, compounds (4, 6 and 17) has IC₅₀ and PIC₅₀ (IC₅₀ 2.01, 2.59, 2.469) and (PIC₅₀ 5.70, 5.59, 5.61) values respectively. The compound 17 may help the scientist for the development of more potent derivatives. Repurposed strategy of utilizing coumarin hopped sulfonamides in screening as antiepileptic agents, which would pave a path in search of novel antiepileptic leads in drug discovery. Out of all screened molecules the molecules 4, 6 and 17 best fit into the 3D QSAR model and Interactive Pharmacophore model are drawn in (Figure 8).

Figure 8: Pharmacophore of best Hits

CONFLICT OF INTEREST STATEMENT:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as potential conflict of interest.

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Received on 01.12.2021 Modified on 13.04.2022

Research J. Pharm. and Tech 2022; 15(12):5521-5528.

DOI: 10.52711/0974-360X.2022.00932