Home J Young Pharm. Vol 16/Issue 4/2024 Innovative Thiophene Schiff Bases: Synthesis and Evaluation as Antitubercular Agents

Innovative Thiophene Schiff Bases: Synthesis and Evaluation as Antitubercular Agents

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Corresponding author.

Correspondence: Dr. Balakrishnan Shanthakumar Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRMIST, Kattankulathur, Chengalpattu-603203, Tamil Nadu, INDIA. Email: [email protected]
Received July 19, 2024; Revised July 30, 2024; Accepted August 22, 2024.
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Citation

1.Paleti G, Jena A, Krishnu G, Chagaleti BK, Bhimarao DP, Balakrishnan S. Innovative Thiophene Schiff Bases: Synthesis and Evaluation as Antitubercular Agents. Journal of Young Pharmacists [Internet]. 2024 Nov 4;16(4):735–44. Available from: http://dx.doi.org/10.5530/jyp.2024.16.93
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Published in: Journal of Young Pharmacists, 2024; 16(4): 735-744.Published online: 01 November 2024DOI: 10.5530/jyp.2024.16.93

ABSTRACT

Background

Tuberculosis (TB) is a leading airborne disease, impacting millions annually and ranking among the top ten causes of global mortality. Post-COVID-19, TB incidence has increased due to its pulmonary nature, which facilitates infection spread. Current TB treatments primarily control rather than prevent infection and are associated with mycobacterial resistance and significant side effects.

Purpose

This study aims to design and evaluate thiophene based Schiff bases as potential antitubercular agents targeting polyketide synthase 13 (Pks 13), crucial for mycolic acid production and less prone to resistance.

Materials and Methods

Thiophene-based Schiff bases were designed based on Structure-Activity Relationship (SAR) analysis and subjected to in silico approaches, including molecular docking against Pks 13. Compounds with the best docking scores underwent further in silico analysis (ADME, drug-likeness, toxicity). These compounds were synthesized, recrystallized, characterized and evaluated for in vitro antitubercular activity using the Microplate Alamar Blue Assay (MABA).

Results

Compounds Ca3 and Ca5 had the best docking scores (-8.6 and -8.4 kcal/mol) and showed significant antitubercular activity in vitro at 25 μg/mL and 12 μg/mL, respectively. In silico and in vitro results correlated well, indicating strong binding affinity and potency against Pks 13.

Conclusion

Compounds Ca3 and Ca5 show promise as potent antitubercular agents targeting polyketide synthase 13, supporting further development and optimization of thiophene-based Schiff bases for TB treatment.

Keywords: Polyketide synthase 13, Mycolic acids, Docking, Array, MABA

INTRODUCTION

Tuberculosis (TB) is a highly contagious disease caused by Mycobacterium tuberculosis, primarily affecting the lungs but also spreading to other parts of the body (extra-pulmonary TB). It spreads through the air via droplets from infected individuals. The death rate from TB has increased in recent years, particularly among those with compromised immune systems such as HIV/AIDS patients, who are 18 times more likely to develop TB. COVID-19 has also contributed significantly to TB-related mortality due to overlapping infection sites.1,2 In 2022, approximately 1.3 million people died from TB, including 167,000 with HIV, while 10.6 million people globally fell ill with TB.

Despite the availability of antitubercular drugs and ongoing vaccine trials, tuberculosis remains a significant challenge due to antimicrobial resistance. Resistance arises from both intrinsic factors, which occur naturally at high levels and extrinsic factors, including acquired mutations due to sub-optimal drug exposure. Factors such as bacterial load, mutation rates and virulence contribute to this resistance. This has led to the emergence of resistant forms like MDR-TB, XDR-TB and TDR-TB, prompting global concern.35 Identifying essential metabolic pathways and components vital for the organism’s survival is critical for developing effective anti-TB drugs.

The cell wall of Mycobacterium tuberculosis is rich in mycolic acids, which are crucial for the bacterium’s survival and resistance. Among the key enzymes in mycolic acid synthesis, Pks13, a type I polyketide synthase, plays a pivotal role by condensing fatty acid intermediates into mycolic acids.610 Pks13 is less sensitive to resistance and helps form a mycolic acid layer that reduces drug permeability, making it an attractive target for tuberculosis treatment.1113 This has led to a focus on discovering new drugs with novel mechanisms, either as independent treatments or in combination.

Heterocyclic rings have shown potent antitubercular activity. This study involves designing thiophene derivatives through a Schiff base reaction. Schiff bases, derived from carbonyl compounds and primary amines, are biologically active and found in many natural and synthetic compounds. Although thiophene-containing heterocyclic derivatives have shown promise as antitubercular agents, none are currently on the market, justifying further research and development.1417

Schiff bases have gained importance in medicinal chemistry for their diverse biological activities, including anti-inflammatory, analgesic,18 anticonvulsant, anticancer, antioxidant, anthelmintic, antimicrobial19 and antitubercular properties.20 Those derived from aromatic aldehydes are particularly noted for their antibacterial and antifungal effects. With the rise of multi-drug-resistant and extensively drug-resistant tuberculosis, there is a crucial need for new, effective agents. Research indicates that Schiff bases with aromatic and heterocyclic rings show promising activity against Mycobacterium tuberculosis and other microorganisms, making them potential candidates to combat drug resistance in tuberculosis treatment.1517

Computer-Aided Drug Design (CADD) uses structural data from target proteins (structure-based) or known bioactive compounds (ligand-based) to identify potential drug candidates. Virtual screening, a key in silico technique, aids in discovering new compounds. Rational drug design within CADD leverages knowledge-driven approaches to elucidate protein-ligand interactions and binding affinities.21,22 This investigation focuses on evaluating Schiff bases and thiophene heterocyclic amino ester derivatives for their potential antibacterial and anti-mycobacterial activity.

MATERIALS AND METHODS

Experimental Section

Materials

Schiff base thiophene derivatives were synthesized by reacting cyclohexanone and substituted aliphatic cyanoacetate in the presence of diethyl-λ4-sulfanimine and ethanol which act as catalysts to produce an intermediate compound, further intermediate compound reacts with various types of aromatic aldehyde. The reactants like cyclohexanone and aliphatic cyanoacetate and various aldehydes like bromobenzaldehyde, 1,3-dinitro benzaldehyde, 1-chloro-4-vinyl benzaldehyde, benzaldehyde, 4-chloro benzaldehyde were purchased from Southern India Scientific Corporation Limited Chennai.

Methods

Using conventional techniques, Schiff base thiophene derivatives were synthesized and their melting points determined by the open capillary tube method. Chemical purity was confirmed using thin-layer chromatography. The designed compounds were sketched with Chem3D Pro 12.0 and docked using AutoDock 4.2. Protein preprocessing involved UCSF Chimera, assigning Kollmann charges, generating grid points and performing molecular docking to analyze binding affinities and interactions.23 Docked complexes were visualized with Biovia Molecular Discovery Studio. The pdb 5v3W protein, validated by resolution and outliers, was selected for docking. The best-ranked compounds underwent in silico analyses, including drug-likeness (Molsoft), bioactivity (Molinspiration) and ADMET parameters (pkCSM).24

Computational Studies

Molecular Docking

Molecules were designed based on literature surveys and SAR studies, leading to a library of Schiff base compounds created from amino ester derivatives and various aromatic aldehydes. The molecules were then evaluated using in silico methods such as docking, ADMET, molecular properties and bioactivity. Molecular docking involved five steps: protein preparation, ligand preparation, grid generation, docking and result analysis. The protein, downloaded from the protein databank in .pdb format, was validated using the Ramachandran plot. Preprocessing included selecting the suitable chain, extracting the co-crystallized ligand, analyzing the active site, generating grid points and minimizing the protein using the OPLS2005 force field. Molecular structures were drawn with ChemDraw Ultra 12.0,25 minimized with Chem3D Pro 12.0 and scrutinized using the MMFF94 force field. The grid was generated to enclose the active site fully and docking was performed to form macromolecular complexes ranked by energy. Interactions were visualized using Biovia Discovery Studio Visualizer.26

In silico Analysis

Molecular properties were analyzed using Molinspiration cheminformatics by sketching the compounds and extracting their SMILES codes. These were then subjected to analysis. The Lipinski rule of five was used, focusing on molecular weight <500 Da, log P≤5, hydrogen bond donors ≤5 and hydrogen bond acceptors ≤10, which correlate with approximately 90% of orally bioavailable drugs reaching phase II clinical trials. Next, the pharmacokinetic profile, including ADMET27,28 properties (Absorption, Distribution, Metabolism, Excretion and Toxicity), was analyzed using the pkCSM server, considering factors like intestinal permeability and aqueous solubility.

Experimental Section

Synthesis of the Compounds

Various aromatic ketones are treated with the active methylene groups like Ethyl and Methyl cyanoacetate to produce the amino esters in the presence of sulfur and ethanol and further, the amino esters are treated with various aromatic aldehydes to form the corresponding Schiff base product2931 and it shown in Figure 1.

Figure 1:
Scheme of the designed schiff base derivatives.

Characterization

The melting points and Rf values of the synthesized compounds were preliminarily checked for purity and homogeneity. The final compounds were found to be soluble in organic solvent Ethanol/ methanol; compounds were also subjected to FTIR spectral studies, Nuclear Magnetic Resonance (NMR) Spectroscopy, Mass Spectroscopy and Elemental Data Analytical studies for structural elucidation, showed good results indicating successful completion of the reaction and absence of impurities.

In vitro Antitubercular Activity

The anti-mycobacterial activity of compounds against M. tuberculosis was evaluated using the Microplate Alamar Blue Assay (MABA). This method uses a thermally stable reagent and correlates well with proportional and BACTEC radiometric methods. To prevent medium evaporation, 200 μL of sterile deionized water was added to the outer perimeter wells of a 96-well plate. Each well received 100 μL of Middlebrook 7H9 broth and serial dilutions of the compounds were prepared on the plate, with final concentrations ranging from 100 to 0.2 μg/ mL. The plates were sealed with parafilm and incubated at 37°C for 5 days. After incubation, 25 μL of a 1:1 mixture of Alamar Blue reagent and 10% Tween 80 were added to each well and incubated for another 24 hr. Blue wells indicated no bacterial growth, while pink indicated growth. The MIC was the lowest concentration preventing the color change from blue to pink. The standard strain used was M. tuberculosis (H37 RV strain) ATCC No., with Pyrazinamide, Ciprofloxacin and Streptomycin serving as controls at 3.125 μg/mL, 3.125 μg/mL and 6.21 μg/mL, respectively.32

RESULTS

Molecular Docking Studies: In silico docking simulations were used to identify ligands with high predicted binding affinity for protein PDB 5V3W. The top-ranked compounds were selected for synthesis and docking results, ranked by lowest energy, are presented in Table 1. Figures 2 and 3 shows the 2D and 3D images of the docked complexes, highlighting ligand-protein interactions.

Figure 2:
3D Interactions of Ca1 -Ca 10.

Figure 3:
2D Interactions of Ca1 -Ca 10.

Compound Code Binding energy (K. Cal) Amino Acids Type of Interactions
Ca 1 -7.7 TYR B: 1582 Pi-Sulfur
ALA B:1583 Alkyl
TYR B: 1637 Amide-Pi stacked
ALA B: 1586 Pi-Alkyl
VAL B: 1614 Alkyl
VAL B: 1618 Pi-Alkyl
PRO B :1595 Alkyl
Ca 2 -7.6 PHE B: 1670 Pi-Pi T-Shaped
TYR B:1582 Amide-Pi Stacked
VAL B:1614 Alkyl
TRP B:1579 Amide-Pi Stacked
VAL B:1614 Alkyl
ALA B:1586 Pi-Alkyl
Ca 3 -8.6 TYR B:1582 Pi-Pi T-Shaped
VAL B:1614 Alkyl
LEU B:1615 Pi-Alkyl
ARG B:1634 Alkyl
VAL B:1611 Alkyl
PHE B:1670 Hydrogen Bond
MET B:1669 Vander Waals
Ca 4 -8.2 TRP B:1579 Amide-Pi Stacked
TYP B:1582 Amide-Pi Stacked
VAL B:1614 Alkyl
ASN B:1640 Hydrogen Bond
Ca 5 -8.4 TYR B:1582 Pi-Sulfur
TRP B:1579 Vander Waals
ALA B:1583 Alkyl
VAL B:1614 Pi-Alkyl
ALA B:1586 Alkyl
PRO B:1595 Vander Waals
Ca 6 -7.2 TYR B:1582 Amide-Pi Stacked
TRP B:1579 Pi-Sulfur
VAL B:1618 Alkyl
LEU B:1615 Alkyl
Ca 7 -7.2 ALA B:1586 Alkyl
VAL B:1614 Alkyl
ALA B:1583 Alkyl
LEU B:1615 Pi-Alkyl
ARG B:1634 Alkyl
VAL B:1611 Pi-Alkyl
TYR B:1637 Alkyl
TYR B:1582 Pi-Sigma
Ca 8 -7.2 TYR B:1637 Amide-Pi Stacked
TYR B:1582 Amide-Pi Stacked
Ca 9 -7.4 SER B:1636 Pi-Sigma
TYR B:1637 Amide-Pi Stacked
TYR B:1582 Amide-Pi Stacked
VAL B:1611 Alkyl
Ca 10 -8.0 ALA B: 1583 Alkyl
VAL B:1614 Pi-Alkyl
ILE B:1597 Alkyl
TRP B:1579 Alkyl
TYR B:1582 Pi-Sigma
TYR B:1637 Alkyl
Table 1:
Molecular Docking Results.

ADME Properties: The pharmacokinetic properties of 10 designed molecules were evaluated using the pkCSM server for ADMET prediction, focusing on drug-likeness. These properties are detailed in Table 2.

Absorption Distribution Metabolism Excretion Toxicity
Compound code Water Solubility Intestinal absorption VDss (Human) Fraction Unbounded CYP2 D6 CYP3A4 Total clearance Renal OCT2 Substrate Max total clearance Oral Rat Acute T
Ca1 -6.48 92.31 0.61 0 No Yes -0.05 No 0.34 2.68
Ca2 -6.16 92.79 0.49 0 No Yes -0.04 No 0.27 2.75
Ca3 -5.44 98.77 0.35 0 No Yes 0.29 No -0.76 2.74
Ca4 -5.22 98.01 0.31 0 No No 0.34 No -0.76 2.69
Ca5 -6.68 91.79 0.57 0 No Yes -0.01 No 0.15 2.74
Ca6 -4.89 92.86 0.18 0.05 No Yes -0.08 No 0.08 2.29
Ca7 -5.24 92.26 0.26 0.04 No Yes -0.13 No 0.16 2.23
Ca8 -5.38 95.34 0.44 0 No Yes 0.14 Yes 0.16 2.50
Ca9 -6.25 93.03 0.52 0 No Yes 0.08 Yes 0.26 2.66
Ca10 -5.57 95.42 0.30 0 No Yes 0.06 Yes 0.07 2.62
Isoniazid -1.65 92.61 -0.35 0.72 No No 0.72 No 1.16 2.30
Table 2:
Pharmacokinetic and toxicity Prediction.

Molecular Properties of Synthesized Compounds: Key molecular properties, such as log P, TPSA, volume, molecular weight, total number of atoms, number of rotatable bonds, hydrogen bond acceptors and hydrogen bond donors, are shown in Table 3.

Compound Code Log P TPSA Natomas MW Nrotb Volume Hydrogen Bond Acceptor Hydrogen Bond Donor
Ca1 5.38 38.67 23 392.32 5 305.01 4 0
Ca2 5.00 38.67 22 378.29 3 288.20 4 0
Ca3 4.41 130.32 28 403.42 6 333.79 8 0
Ca4 4.04 130.32 27 389.39 5 316.99 8 0
Ca5 5.62 38.67 24 359.88 4 311.27 4 0
Isoniazid -0.97 68.01 10 137.14 1 122.56 3 2
Table 3:
Molecular Properties of Synthesized Compounds.

Bioactivity: The selected compounds were evaluated for bioactivity against various receptors, including nuclear ligand-receptors, G-protein coupled receptors, tyrosine kinase-linked receptors, ion channels and protease and enzyme inhibitors. The results are shown in Table 4.

Compound Code GPCR Ligand Ion channel Kinase Inhibitor Nuclear Receptor Ligand Protease Inhibitor Enzyme Inhibitor
Cal -0.36 -0.47 -0.60 -0.58 -0.50 -0.26
Ca2 -0.34 -0.49 -0.58 -0.62 -0.49 -0.27
Ca3 -0.64 -0.95 -0.68 -0.85 -0.86 -0.85
Ca4 -0.63 -0.49 -0.65 -0.91 -0.84 -0.46
Ca5 -0.37 -0.75 -0.67 -0.65 -0.70 -0.38
Isoniazid -1.39 -1.45 -1.05 -2.23 -1.23 -0.66
Table 4:
Bioactivity of the Synthesized Compounds.

Antitubercular Activity by MABA Test

The antitubercular activity for the synthesized compounds was performed by microplate alamar blue assay method and the results were tabulated in Table 5.

Sample 100 μg/mL 50 μg/mL 25 μg/mL 12.5 μg/mL 6.25 μg/mL 3.12 μg/mL 1.6 μg/mL 0.8 μg/mL
Ca1 S S R R R R R R
Ca2 S S R R R R R R
Ca3 S S S R R R R R
Ca4 S S R R R R R R
Ca5 S S S S R R R R
Table 5:
Anti-tubercular activity for synthesized compound.

Characterization

Ethyl2-((2,4-bromobenzylidene) amino)-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carboxylate (1)

Yield 76%, Melting point: 145°C; IR (Cyclohexane cm-1): 3310 (N-H stretching), 760 (Thiophene, C-H bending), 1710 (C=O stretching), 1180 (C-O stretching), 2920 (C-H stretching), 1590 (C=N stretching), 1480 (C=C stretching), 630 (C-Br stretching); 1H NMR(400 MHz, DMSO): 7.65 (d, 2H, Ar-H, J≈8 Hz), 7.50 (d, 2H, Ar-H, J≈8 Hz,), 8.40 (s, 1H), 1.90 (m, 4H, CH2), 2.60 (m, 4H, CH2), 4.20 (q, 2H, OCH2, J≈7 Hz), 1.30 (t, 3H, CH3, J≈7 Hz). Compound -1 Molecular formula: C17H16BrNO2S, Molecular weight: 392.31, Elemental Analysis: C (53.97), H (4.26), Br (21.12), N (3.70), O (8.46), S (8.48).

Methyl 2-{[(4bromophenyl) methylidene] amino}4,5, 6,7-tetrahydrobenzo[b] thiophene-3carboxylate (2)

Yield 75%, Melting point: 163°C, IR (Cyclohexane cm-1): 1730-1750 (C=O stretching), 1600-1620 (C=C stretching), 1650-1690 (C-N stretching), 500-600 (C-Br stretching), 690-740 (C-S stretching), 2850-2950 (C-H stretching), 3000-3100 (Aromatic C-H stretching); 1H NMR(400 MHz, DMSO): 7.2-7.8 (m, 4H), 8.0-8.5 (s, 1H, CH=N), 3.5-4.0 (s, 3H,OCH3), 1.2-2.5 (m, 8H). Compound-2 Molecular formula: C18H18BrNO2S, Molecular weight: 364.26, Elemental Analysis: C (55.11), H (4.62), Br (20.37), N (3.57), O (8.16), S (8.17).

Ethyl 2-{[(2,4-dinitrobenzylidene) amino)-4,5,6,7-tetrahydrobenzo[b]thiophene-3carboxylate (3)

Yield 85%, Melting point: 136°C, IR (Cyclohexane cm-1): 1730-1750 (C=O stretching), 1600-1620 (C=C Aromatic stretching), 1630-1680 (C-N stretching), 1510-1560, 1320-1350 (NO2 stretching), 690-740 (C-S stretching), 2850-2950 (Alkyl C-H stretching), 3000-3100 (Aromatic C-H stretching); 1H NMR(400 MHz, DMSO): 7.4-8.0 (m, 4H), 8.0-8.5 (s, 1H, CH=N), 1.2-1.4 (d, 3H, CH2CH3, 1.5-2.5 (m, 8H, CH2 groups); Compound-3 Molecular formula: C18H17N3O6S, Molecular weight: 403.41, Elemental Analysis: C (53.59), H (4.25), N (10.42), O (23.80), S (7.95).

Methyl 2-((2,4-dinitrobenzylidene) amino)-4,5,6,7-tetrahydrobenzo[b]thiophene-3carboxylate (4)

Yield 72%, Melting point: 141°C, IR (Cyclohexane cm-1): 1730-1750 (C=O stretching), 1600-1620 (C=C Aromatic stretching), 1630-1680 (C-N stretching), 1510-1560, 1320-1350 (NO2 stretching), 690-740 (C-S stretching), 2800-2950 (Methyl stretching), 3000-3100 (Aromatic C-H stretching); 1H NMR (400 MHz, DMSO): 7.4-8.0 (m, 4H), 8.0-8.5 (s,1H, CH=N), 2.0-2.5 (s, 3H, CH3), 1.5-2.5 (m, 8H, CH2); Compound-4 Molecular formula: C17H15N3O6S, Molecular weight: 389.38, Elemental Analysis: C (52.44), H (3.88), N (10.79), O (24.65), S (8.23).

Methyl 2-((3-(4-chlorophenyl) allylidene) amino)-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carboxylate (5)

Yield 84%, Melting point: 185°C, IR (Cyclohexane cm-1): 1730-1750 (C=O stretching), 1600-1620 (C=C Aromatic stretching), 1630-1680 (C-N stretching), 1600-1650 (C=C stretching,), 700-800 (C-Cl stretching), 690-740 (C-S stretching), 2800-2950 (C-H stretching), 3000-3100 (Aromatic C-H stretching); 1H NMR (400 MHz, DMSO): 7.0-8.0 (m, 5H, Aromatic protons), 5.5-6.5 (m, 2H, CH=CH2), 8.0-8.5 (s, 1H, CH=N), 2.0-2.5 (s, 3H,CH3), 1.5-2.5 (m, 8H, CH2 groups); Compound-5 Molecular Formula: C19H18ClNO2S Molecular weight: 359.87, Elemental Analysis: C (63.41), H (5.04), N (3.89), O (8.89), S (8.91), Cl (9.85).

DISCUSSION

Ten novel compounds were designed and subjected to docking studies. Compounds Ca3 and Ca5 exhibited the lowest binding energies and highest binding affinities. Ca3 had a binding energy of -8.6 kcal/mol, interacting with amino acids TYR B:1582, VAL B:1614, LEU B:1615, ARG B:1634, VAL B:1611, PHE B:1670 and MET B:1669 through pi-pi stacking, T-shaped interactions, alkyl, pi-alkyl, conjugated hydrogen bonding and van der Waals forces. Ca5 had a binding energy of -8.4 kcal/mol, interacting with TYR B:1582, TRP B:1579, ALA B:1583, VAL B:1614, ALA B:1586 and PRO B:1595 through Pi-Sulfur, Van der Waals, alkyl and pi-alkyl interactions. The docking results, ranked by lowest energy, are shown in Table 1 and Figures 2 and 3 present the 2D and 3D images of the docked complexes, highlighting the ligand-protein interactions.

Using pkCSM server results, predicted water solubility (log P) ranged from -6.48 to -4.89, indicating moderate to low water solubility. Intestinal absorption was high (91.79 to 98.77) and the Volume of distribution (Vd) ranged from 0.18 to 0.61, suggesting wider distribution for compounds with lower Vd values. Compounds showed varying interactions with metabolic enzymes Cytochrome P2D6 and P3A4. Total renal clearance ranged from -0.08 to 0.34, suggesting moderate clearance, with oral rat acute toxicity between 2.23 to 2.75 indicating moderate oral clearance rates in rats. Compounds with desirable pharmacokinetic properties and good molecular docking results were selected for further studies.

The compounds’ LogP values ranged from 4.04 to 5.62, indicating moderate to low water solubility. TPSA values ranged from 38.67 to 130.2 Å2, suggesting moderate polarity; compounds with lower TPSA may have better passive membrane diffusion and higher oral absorption, while higher TPSA compounds may have lower diffusion due to greater polarity. The molecular weights ranged from 359.88 to 403.42 Da and the hydrogen bond acceptors, donors and numbers of rotatable bonds were within the acceptable range for orally administered drugs according to the Lipinski Rule of Five.

Compounds with values ranging from -0.34 to -0.64 indicate weak to moderate binding affinity with GPCR, with lower values suggesting weaker binding. Ion channel interaction showed stronger inhibition at -0.47 and weaker inhibition at -0.95. For IC50 values, a lower value (closer to -0.58) indicates a stronger inhibitor, requiring a lower concentration for 50% inhibition, while a higher value (closer to -0.68) indicates a weaker inhibitor. Inhibition of protease enzymes ranged from -0.50 to -0.91, indicating weak inhibition. Similarly, enzyme inhibition values ranged from -0.26 to -0.85, also indicating weak inhibition.

Molecules with favorable docking, ADME predictions and biological activity were synthesized. Cyclohexanone (0.1 moles) reacted with ethyl cyanoacetate, methyl cyanoacetates, sulfur and diethyl amine (0.125 moles) in 20 mL of ethanol. The mixture was stirred for 3 hr at room temperature and refrigerated overnight. After adding 20 mL of ice-cold water, an amino ester was obtained. In the second step, the amino ester reacted with various aromatic aldehydes in 10 mL of ethanol to yield an imine base. Melting points ranged from 136-185°C, with yields between 72% and 85%. Reaction completion was monitored by TLC using n-hexane and ethyl acetate. The Rf values varied and ethanol was used for recrystallization.

Figure 4:
Anti-tubercular activity by MABA test.

It shows that the values range between the concentration ranging from 0.8-100 μg/mL. The compounds Ca3 and Ca5 were found to be active at 25 μg/mL and 12 μg/mL respectively whereas Ca1, Ca2 and Ca4 were active only at 50 μg/mL concentration. The color change from blue to pink in the microtitre plate is scored as growth, whereas blue indicates no growth and it is shown in Figure 4.

CONCLUSION

In this study, a series of new thiophene derivatives were synthesized using Schiff base reaction and screened for in silico antitubercular activity. All compounds satisfied Lipinski’s Rule of Five, indicating good oral bioactivity. Molecular docking revealed that compounds Ca3 and Ca5 had the best interactions with the target, showing the lowest binding energy. These compounds were optimized for future development. This investigation suggests that these thiophene derivatives can be used as leads for novel antitubercular agents.

Cite this article

Paleti G, Jena A, Krishnu G, Chagaleti BK, Dudhe PB, Shanthakumar B. Innovative Thiophene Schiff Bases: Synthesis and Evaluation as Antitubercular Agents. J Young Pharm. 2024;16(4):735-44.

ACKNOWLEDGEMENT

We thank the Research Council of SRMIST and the Dean of SRM College of Pharmacy for their valuable support.

ABBREVIATIONS

TB: Tuberculosis
Pks13: Polyketide Synthase 13
ADMET: Absorption Distribution Metabolism Excretion Toxicity
MABA: Microplate Alamar Blue Assay
HIV: Human Immunodeficiency Virus
MDR-TB: Multidrug-resistance Tuberculosis
XDR-TB: Extensively drug-resistance Tuberculosis
TDR-TB: Total drug-resistance tuberculosis
M.tb: Mycobacterium tuberculosis
CADD: Computer-aided Drug Design
PDB: Protein Data Bank
SAR: Structural Activity Relationship
MMFF94: Merck Molecular Force Field
OPLS2005: Optimized Potentials for Liquid Simulations
FTIR: Fourier Transform Infrared Spectroscopy
NMR: Nuclear Magnetic Resonance
TPSA: Topological Polar Surface Area
TLC: Thin layer Chromatography

References

  1. Tuberculosis. 2023 Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis.
  2. World Health Organization. Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis. World Health Organization. 2014 [Google Scholar]
  3. Uppumavuluri NT, Krovvidi SR, Mailavaram RP, Mohanty SK, Deb PK, Venugopala KN, et al. Pks 13 inhibitors-a promising target for future antitubercular agents. Med Chem Res. 2023;32(8):1574-88. [CrossRef] | [Google Scholar]
  4. Cai Y, Zhang W, Lun S, Zhu T, Xu W, Yang F, et al. Design, synthesis and biological evaluation of N-phenylindole derivatives as Pks13 inhibitors against Mycobacterium tuberculosis. Molecules. 2022;27(9):2844 [PubMed] | [CrossRef] | [Google Scholar]
  5. Alsayed SS, Gunosewoyo H. Tuberculosis: pathogenesis, current treatment regimens and new drug targets. Int J Mol Sci. 2023;24(6):5202 [PubMed] | [CrossRef] | [Google Scholar]
  6. Gavalda S, Bardou F, Laval F, Bon C, Malaga W, Chalut C, et al. The polyketide synthase Pks13 catalyzes a novel mechanism of lipid transfer in mycobacteria. Chem Biol. 2014;21(12):1660-9. [PubMed] | [CrossRef] | [Google Scholar]
  7. Abrahams KA, Besra GS. Mycobacterial cell wall biosynthesis: a multifaceted antibiotic target. Parasitology. 2018;145(2):116-33. [PubMed] | [CrossRef] | [Google Scholar]
  8. Wilson R, Kumar P, Parashar V, Vilchèze C, Veyron-Churlet R, Freundlich JS, et al. Antituberculosis thiophenes define a requirement for Pks13 in mycolic acid biosynthesis. Nat Chem Biol. 2013;9(8):499-506. [PubMed] | [CrossRef] | [Google Scholar]
  9. Portevin D, de Sousa-D’Auria C, Houssin C, Grimaldi C, Chami M, Daffé M, et al. A polyketide synthase catalyzes the last condensation step of mycolic acid biosynthesis in mycobacteria and related organisms. Proc Natl Acad Sci U S A. 2004;101(1):314-9. [PubMed] | [CrossRef] | [Google Scholar]
  10. Bhatt A, Molle V, Besra GS, Jacobs WR, Kremer L. The Mycobacterium tuberculosis FAS-II condensing enzymes: their role in mycolic acid biosynthesis, acid-fastness, pathogenesis and in future drug development. Mol Microbiol. 2007;64(6):1442-54. [PubMed] | [CrossRef] | [Google Scholar]
  11. Shanthakumar B, Gopinath P, Chagaleti BK, Saravanan V, Palaniappan SK, Musaed Almutairi SM, et al. Imidazooxazine moiety as polyketide synthase 13 inhibitors targeting tuberculosis. J King Saud Univ Sci. 2024;36(6):103220 [CrossRef] | [Google Scholar]
  12. Jena A, Prakashraj C, Chagaleti BK, Kathiravan MK, Kumar BS. Array. Indian J Heterocycl Chem. 2023;33(1) [CrossRef] | [Google Scholar]
  13. B S, M K K. Insights into structures of imidazo oxazines as potent polyketide synthase XIII inhibitors using molecular modeling techniques. J Recept Signal Transduct Res. 2020;40(4):313-23. [PubMed] | [CrossRef] | [Google Scholar]
  14. Asiri YI, Muhsinah AB, Alsayari A, Venkatesan K, Al-Ghorbani M, Mabkhot YN, et al. Design, synthesis and antimicrobial activity of novel 2-aminothiophene containing cyclic and heterocyclic moieties. Bioorg Med Chem Lett. 2021;44:128117 [PubMed] | [CrossRef] | [Google Scholar]
  15. Mabkhot YN, Kaal NA, Alterary S, Mubarak MS, Alsayari A, Bin Muhsinah A, et al. New thiophene derivatives as antimicrobial agents. J Heterocycl Chem. 2019;56(10):2845-953. [CrossRef] | [Google Scholar]
  16. Mabkhot YN, Alatibi F, El-Sayed NN, Kheder NA, Al-Showiman SS. Synthesis and structure-activity relationship of some new thiophene-based heterocycles as potential antimicrobial agents. Molecules. 2016;21(8):1036 [PubMed] | [CrossRef] | [Google Scholar]
  17. Thanna S, Knudson SE, Grzegorzewicz A, Kapil S, Goins CM, Ronning DR, et al. Synthesis and evaluation of new 2-aminothiophenes against Mycobacterium tuberculosis. Org Biomol Chem. 2016;14(25):6119-33. [PubMed] | [CrossRef] | [Google Scholar]
  18. Rana K, Pandurangan A, Singh N, Tiwari AK. A systemic review of Schiff bases as an analgesic and anti-inflammatory. Int J Curr Pharm Res. 2012;4(2):5-11. [PubMed] | [CrossRef] | [Google Scholar]
  19. Dhedan RM, Alsahib SA, Ali RA. A brief review on Schiff base, synthesis and their antimicrobial activities. Russ J Bioorg Chem. 2023;49:S31-52. Suppl 1 [CrossRef] | [Google Scholar]
  20. More G, Bootwala S, Shenoy S, Mascarenhas J, Aruna K. Synthesis, characterization and antitubercular and antimicrobial activities of new aminothiophene Schiff bases and their Co (II). Orient J Chem. 2018;34(2):800-12. [CrossRef] | [Google Scholar]
  21. Surabhi S, Singh BK. Computer-aided drug design: an overview. J Drug Deliv Ther. 2018;8(5):504-9. [CrossRef] | [Google Scholar]
  22. Laamari Y, Bimoussa A, Mourad F, Chagaleti BK, Saravanan V, Alossaimi MA, et al. Multitargeted molecular Docking and dynamics simulation of thymol-based chalcones against cancer protein markers: synthesis, characterization and computational study. J Mol Struct. 2024;1317:139116 [CrossRef] | [Google Scholar]
  23. Chagaleti BK, Saravanan V, Vellapandian C, Kathiravan MK. Exploring cyclin-dependent kinase inhibitors: a comprehensive study in search of CDK-6 inhibitors using a pharmacophore modeling and dynamics approach. RSC Adv. 2023;13(48):33770-85. [PubMed] | [CrossRef] | [Google Scholar]
  24. Chagaleti BK, Rajagopal R, Alfarhan A, Arockiaraj J. Targeting cyclin-dependent kinase 2 CDK2: insights from Molecular Docking and Dynamics Simulation-A systematic computational approach to discover novel cancer therapeutics. Comp Biol Chem. 2024;112:108134 [PubMed] | [CrossRef] | [Google Scholar]
  25. Saravanan V, Chagaleti BK, Packiapalavesam SD, Kathiravan M. Ligand based pharmacophore modelling and integrated computational approaches in the quest for small molecule inhibitors against hCA IX. RSC Adv. 2024;14(5):3346-58. [PubMed] | [CrossRef] | [Google Scholar]
  26. Chagaleti BK, Reddy MB, Saravanan V, Senthil Kumar P. An overview of mechanism and chemical inhibitors of shikimate kinase. J Biomol Struct Dyn. 2023;41(23):14582-98. [PubMed] | [CrossRef] | [Google Scholar]
  27. Thanvi A, Chagaleti BK, Srimathi R S, Kathiravan M K K, B Shanthakumar S. 2, 4-substituted oxazolones: antioxidant potential exploration. J Young Pharm. 2024;16(2):244-51. [CrossRef] | [Google Scholar]
  28. Alqahtani S. Array. Expert Opin Drug Metab Toxicol. 2017;13(11):1147-58. [PubMed] | [CrossRef] | [Google Scholar]
  29. Shanthakumar B, Saravanan V, Chagaleti BK, Kathiravan MK. Design synthesis and biological evaluation of thiophene 2-pentafluoro benzamide derivatives as antitubercular agent. J Med Pharm Allied Sci. 2023;12 [PubMed] | [CrossRef] | [Google Scholar]
  30. Salake AB, Chothe AS, Nilewar SS. Design, synthesis, and evaluations of antifungal activity of novel phenyl(2H-tetrazol-5-yl)methanamine derivatives. J Chem Biol. 2014;1(7):29-35. [PubMed] | [CrossRef] | [Google Scholar]
  31. Jain KS, Bariwal JB, Phoujdar MS, Nagras MA, Amrutkar RD, Munde MK, et al. A novel microwave‐assisted green synthesis of condensed 2‐substituted‐pyrimidin‐4 (3H)‐ones under solvent‐free conditions. Journal of Heterocyclic Chemistry. 2009;46(2):178-85. [CrossRef] | [Google Scholar]
  32. Cho S, Lee HS, Franzblau S. Microplate alamar blue assay (MABA) and low oxygen recovery assay (LORA) for Mycobacterium tuberculosis. In: Mycobacteria protocols. 2015:281-92. [PubMed] | [CrossRef] | [Google Scholar]