College of Pharmacy, Madras Medical College, affiliated to The Tamil Nadu Dr. M.G.R. Medical University, Chennai, Tamil Nadu, India
Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disorder characterized by airflow limitation and inflammation. Phosphodiesterase 4D (PDE4D) inhibitors have emerged as promising therapeutic targets for COPD due to their ability to modulate cyclic AMP levels and reduce inflammation. In this study, an in-silico approach was employed to design and evaluate novel thiophene derivatives as potential PDE4D inhibitors. Molecular docking studies were conducted to predict the binding interactions of these derivatives within the active site of PDE4D. The docking results revealed strong binding affinities, indicating that these compounds could effectively inhibit PDE4D activity. The key interactions between the ligands and the amino acid residues within the PDE4D binding pocket were analysed to understand the structure-activity relationships (SAR). Furthermore, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling ensured that the designed derivatives possess favourable pharmacokinetic properties. This study highlights the potential of thiophene derivatives as lead compounds for the development of new COPD therapies.
Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition characterized by persistent airflow limitation and an inflammatory response in the lungs. It is a significant cause of morbidity and mortality worldwide, impacting the quality of life of millions of individuals and posing a substantial economic burden. [1] COPD develops gradually and is often diagnosed in middle-aged or older adults with a history of smoking or prolonged exposure to irritants. The disease progresses over time, with symptoms becoming more severe and leading to complications like respiratory failure, cardiovascular issues, and increased susceptibility to lung infections. Despite being preventable and treatable, COPD remains a leading cause of death globally due to underdiagnosis and late intervention. [1,3] Chronic Obstructive Pulmonary Disease (COPD) encompasses two primary conditions, often coexisting, that contribute to breathing difficulties:
1.Chronic Bronchitis
Figure No.1: Chronic Bronchitis
2. Emphysema
Stages of COPD:
The severity of COPD is classified into four stages based on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, which use forced expiratory volume in 1 second (FEV?) from spirometry testing. The GOLD staging helps guide treatment decisions and predict disease progression.
Stage 1: Mild COPD (GOLD 1)
Stage 2: Moderate COPD (GOLD 2)
Stage 3: Severe COPD (GOLD 3)
Stage 4: Very Severe COPD (GOLD 4)
Causes:
The primary cause of COPD is exposure to irritants that damage the lungs and airways. Key factors include:
Symptoms:
COPD symptoms often develop slowly and worsen over time. Common symptoms include:
As the disease advances, symptoms may include unintentional weight loss, swelling in the legs, and cyanosis (bluish discoloration of lips or fingers). [1]
Epidemiology:
COPD is a global health challenge:
Etiology:
COPD results from a combination of genetic, environmental and behavioural factors:
Treatment:
Although COPD cannot be cured, treatments aim to manage symptoms, slow disease progression, and improve quality of life.
Medications:
Non-Medication Therapies:
Prevention:
Preventing COPD involves addressing modifiable risk factors:
PDE4 Inhibitors in COPD: Mechanism, Role, and Therapeutic Potential
Phosphodiesterase 4 (PDE4) inhibitors represent a significant advancement in the management of chronic obstructive pulmonary disease (COPD), particularly for patients with severe disease and frequent exacerbations. By targeting the inflammatory pathways central to COPD pathogenesis, PDE4 inhibitors help reduce symptoms, prevent flare-ups, and improve overall lung function.[4]
Role of PDE4 in COPD Pathophysiology
Phosphodiesterases (PDEs) are a family of enzymes that degrade cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), key secondary messengers involved in cellular signaling. Among the PDE family, PDE4 is the dominant isoform in inflammatory and immune cells such as neutrophils, macrophages, T-cells, and airway epithelial cells.
In COPD, overactivation of PDE4 leads to:
By inhibiting PDE4, cAMP levels are restored, reducing inflammation and airway damage while improving respiratory outcomes. [5]
PDE4 Inhibitors: Mechanism of Action
PDE4 inhibitors act by blocking the breakdown of cAMP, leading to:
These mechanisms collectively reduce chronic inflammation, which is a hallmark of COPD progression. [6]
Thiophene Scaffold in COPD: A Promising Avenue for Drug Development
The thiophene scaffold, a five-membered sulfur-containing heterocycle, has emerged as a versatile framework in drug design due to its unique chemical and pharmacological properties. In the context of chronic obstructive pulmonary disease (COPD), thiophene-based compounds are being explored for their potential to modulate key inflammatory and pathological processes, particularly as PDE4 inhibitors and anti-inflammatory agents. [7]
Anti-Inflammatory Potential of Thiophene Derivatives
COPD is characterized by chronic airway inflammation involving neutrophils, macrophages, and pro-inflammatory cytokines. Thiophene-based compounds exhibit anti-inflammatory properties by targeting pathways such as:
MATERIALS AND METHODS:
Synthetic Scheme:
Selection Of Biological Target:
A Protein Data Bank is a crystallographic database for three-dimensional structural data of large biological molecules such as Protein, Nucleic acid and Complex assemblies. In this study, Phosphodiesterase 4 (PDE4) inhibitors was chosen as a target for the treatment of COPD. The efficient PDB enzyme target was selected with lower resolution (PDB ID: 6LRM) provides a significant target for COPD therapeutic drugs. [10]
Construction of virtual library of ligands and novelty checking:
The ligands were drawn using the Chem Sketch software based on the necessary pharmacophoric features. Zinc15 and the Pubchem database were used to verify the novelty of the compounds. The designed compounds were considered to be novel since there is no data available in the ZINC® database. [10]
In-silico screening of drug likeness property:
Swiss ADME is a free web tool that predicts the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of small molecules, along with other pharmacokinetic and physicochemical characteristics. It is widely used in drug discovery and development to evaluate a compound’s drug-likeness and optimize its pharmacological properties. [10]
In-silico screening of toxicity prediction:
The Osiris Property Explorer is a computational tool designed to predict the drug-relevant properties of chemical compounds. It provides a quick and intuitive way to estimate various molecular properties, including toxicity risks, physicochemical properties, and drug-likeness. It is widely used in the early stages of drug discovery and development for compound optimization. [10]
Molecular Docking:
Molecular docking is a computational method used to predict the interaction between a small molecule (ligand) and a target macromolecule (typically a protein or nucleic acid). It is a fundamental tool in structure-based drug design, used to identify potential drug candidates by modeling their binding pose and affinity. [10]
Post-Docking Analysis:
Molegro Molecular Viewer (MMV) is a user-friendly software tool for visualizing molecular structures and analyzing interactions between proteins and ligands. It is particularly effective for post-docking analysis and offers various visualization options to explore binding poses, interaction networks (Hydrogen Bonds, Hydrophobic Interactions, Electrostatic Interactions) and molecular features.[10]
RESULTS AND DISCUSSION:
Novelty assessment:
The designed 50 compounds were considered to be novel since there is no data available in the ZINC® and the Pubchem database.
In-silico screening of drug-likeness property:
As per Lipinski rule of 5, ADME properties of novel proposed analogues were assessed using Swiss ADME, a free web tool.
Table No.1: Drug-likeness property of novel proposed analogues
Compound code |
Log P |
Molecular weight |
No. of HBA |
No. of HBD |
No. of Rot. bonds |
Lipinski rule of 5 (n violation) |
SS1 |
2.44 |
214.29 |
1 |
2 |
2 |
0 |
SS2 |
2.67 |
228.31 |
1 |
1 |
2 |
0 |
SS3 |
2.72 |
244.31 |
2 |
1 |
3 |
0 |
SS4 |
2.23 |
230.29 |
2 |
2 |
2 |
0 |
SS5 |
2.87 |
274.34 |
3 |
1 |
4 |
0 |
SS6 |
2.57 |
260.31 |
3 |
2 |
3 |
0 |
SS7 |
1.83 |
259.28 |
3 |
1 |
3 |
0 |
SS8 |
2.76 |
240.32 |
1 |
1 |
3 |
0 |
SS9 |
2.25 |
204.25 |
2 |
1 |
2 |
0 |
SS10 |
2.67 |
282.28 |
4 |
1 |
3 |
0 |
SS11 |
2.58 |
264.73 |
2 |
2 |
2 |
0 |
SS12 |
2.69 |
248.73 |
1 |
1 |
2 |
0 |
SS13 |
1.82 |
249.25 |
4 |
1 |
3 |
0 |
SS14 |
2.35 |
220.31 |
1 |
1 |
2 |
0 |
SS15 |
2.05 |
215.27 |
2 |
1 |
2 |
0 |
SS16 |
2.15 |
166.24 |
1 |
1 |
2 |
0 |
SS17 |
2.38 |
180.27 |
1 |
1 |
3 |
0 |
SS18 |
2.35 |
178.25 |
1 |
1 |
2 |
0 |
SS19 |
2.64 |
194.30 |
1 |
1 |
4 |
0 |
SS20 |
2.08 |
164.23 |
1 |
1 |
2 |
0 |
SS21 |
2.77 |
228.31 |
1 |
1 |
3 |
0 |
SS22 |
3.03 |
242.34 |
1 |
1 |
3 |
0 |
SS23 |
3.03 |
258.34 |
2 |
1 |
4 |
0 |
SS24 |
2.52 |
244.31 |
2 |
2 |
3 |
0 |
SS25 |
3.19 |
288.36 |
3 |
1 |
5 |
0 |
SS26 |
2.91 |
274.34 |
3 |
2 |
4 |
0 |
SS27 |
2.36 |
273.31 |
3 |
1 |
4 |
0 |
SS28 |
3.11 |
254.35 |
1 |
1 |
4 |
0 |
SS29 |
2.53 |
218.27 |
2 |
1 |
3 |
0 |
SS30 |
3.00 |
296.31 |
4 |
1 |
4 |
0 |
SS31 |
2.67 |
278.76 |
2 |
2 |
3 |
0 |
SS32 |
3.02 |
262.76 |
1 |
1 |
3 |
0 |
SS33 |
1.99 |
263.27 |
4 |
1 |
4 |
0 |
SS34 |
2.69 |
234.34 |
1 |
1 |
3 |
0 |
SS35 |
2.33 |
229.30 |
2 |
1 |
3 |
0 |
SS36 |
2.48 |
180.27 |
1 |
1 |
3 |
0 |
SS37 |
2.65 |
194.30 |
1 |
1 |
4 |
0 |
SS38 |
2.67 |
192.28 |
1 |
1 |
3 |
0 |
SS39 |
2.94 |
208.32 |
1 |
1 |
5 |
0 |
SS40 |
2.40 |
178.25 |
1 |
1 |
3 |
0 |
SS41 |
3.16 |
290.38 |
1 |
1 |
4 |
0 |
SS42 |
3.50 |
304.41 |
1 |
1 |
4 |
0 |
SS43 |
3.46 |
320.41 |
2 |
1 |
5 |
0 |
SS44 |
3.02 |
306.38 |
2 |
2 |
4 |
0 |
SS45 |
3.66 |
350.43 |
3 |
1 |
6 |
0 |
SS46 |
3.32 |
336.41 |
3 |
2 |
5 |
0 |
SS47 |
2.28 |
335.38 |
3 |
1 |
5 |
0 |
SS48 |
3.44 |
316.42 |
1 |
1 |
5 |
0 |
SS49 |
2.89 |
280.34 |
2 |
1 |
4 |
0 |
SS50 |
3.39 |
358.38 |
4 |
1 |
5 |
0 |
In-silico screening of toxicity prediction:
Toxicity profile of novel proposed analogues was assessed using the Osiris Property Explorer.
Table No.2: Toxicity profile of novel proposed analogues
Compound code |
Toxicity Prediction |
|||
Mutagenic |
Tumorigenic |
Irritant |
Reproductive effect |
|
SS1 |
|
|
|
|
SS2 |
|
|
|
|
SS3 |
|
|
|
|
SS4 |
|
|
|
|
SS5 |
|
|
|
|
SS6 |
|
|
|
|
SS7 |
|
|
|
|
SS8 |
|
|
|
|
SS9 |
|
|
|
|
SS10 |
|
|
|
|
SS11 |
|
|
|
|
SS12 |
|
|
|
|
SS13 |
|
|
|
|
SS14 |
|
|
|
|
SS15 |
|
|
|
|
SS16 |
|
|
|
|
SS17 |
|
|
|
|
SS18 |
|
|
|
|
SS19 |
|
|
|
|
SS20 |
|
|
|
|
SS21 |
|
|
|
|
SS22 |
|
|
|
|
SS23 |
|
|
|
|
SS24 |
|
|
|
|
SS25 |
|
|
|
|
SS26 |
|
|
|
|
SS27 |
|
|
|
|
SS28 |
|
|
|
|
SS29 |
|
|
|
|
SS30 |
|
|
|
|
SS31 |
|
|
|
|
SS32 |
|
|
|
|
SS33 |
|
|
|
|
SS34 |
|
|
|
|
SS35 |
|
|
|
|
SS36 |
|
|
|
|
SS37 |
|
|
|
|
SS38 |
|
|
|
|
SS39 |
|
|
|
|
SS40 |
|
|
|
|
SS41 |
|
|
|
|
SS42 |
|
|
|
|
SS43 |
|
|
|
|
SS44 |
|
|
|
|
SS45 |
|
|
|
|
SS46 |
|
|
|
|
SS47 |
|
|
|
|
SS48 |
|
|
|
|
SS49 |
|
|
|
|
SS50 |
|
|
|
|
Docking studies:
The ligands with novelty (0 identity), good drug-likeness properties and no toxicity were selected for molecular docking studies against PDE4D inhibitors (PDB ID: 6LRM)
Table No.3: Binding score of novel proposed analogues
Compound code |
PDE4D inhibitors (PDB ID: 6LRM) |
Compound code |
PDE4D inhibitors (PDB ID: 6LRM) |
SS1 |
-6.37 |
SS26 |
-6.57 |
SS2 |
-6.97 |
SS27 |
-7.68 |
SS3 |
-6.76 |
SS28 |
-6.74 |
SS4 |
-6.82 |
SS29 |
-7.03 |
SS5 |
-7.08 |
SS30 |
-7.05 |
SS6 |
-6.88 |
SS31 |
-7.03 |
SS7 |
-7.92 |
SS32 |
-6.93 |
SS8 |
-6.69 |
SS33 |
-7.73 |
SS9 |
-7.18 |
SS34 |
-6.65 |
SS10 |
-6.77 |
SS35 |
-6.77 |
SS11 |
-6.82 |
SS36 |
-5.88 |
SS12 |
-6.92 |
SS37 |
-5.64 |
SS13 |
-7.69 |
SS38 |
-6.01 |
SS14 |
-6.65 |
SS39 |
-5.66 |
SS15 |
-6.77 |
SS40 |
-5.71 |
SS16 |
-6.0 |
SS41 |
-7.28 |
SS17 |
-6.18 |
SS42 |
-6.86 |
SS18 |
-6.38 |
SS43 |
-7.43 |
SS19 |
-6.19 |
SS44 |
-7.04 |
SS20 |
-6.06 |
SS45 |
-8.67 |
SS21 |
-6.65 |
SS46 |
-7.83 |
SS22 |
-6.86 |
SS47 |
-7.1 |
SS23 |
-6.77 |
SS48 |
-6.99 |
SS24 |
-6.62 |
SS49 |
-7.57 |
SS25 |
-7.71 |
SS50 |
-7.5 |
Table No.4: Structure / IUPAC name of top 5 ligands based on docking scores
To visualize molecular structures and analyze interactions between protein and ligands, Molegro Molecular Viewer (MMV) software is used.
Table No.5: Docking interaction of top 5 ligands based on docking scores
Table No.6: Ligand-Receptor interaction
Compound code |
Hydrogen bond interaction |
SS7 |
Gln 369 |
SS25 |
Gln 369 |
SS33 |
Gln 369 |
SS45 |
Tyr 159, His 160 |
SS46 |
Gln 369 |
CONCLUSION:
This study demonstrates the utility of in-silico drug design and molecular docking techniques in identifying novel thiophene derivatives as potential PDE4D inhibitors for COPD treatment. The computational findings suggest that these compounds exhibit strong binding affinities and favorable pharmacokinetic profiles, making them promising candidates for further experimental validation. The insights gained from the docking analysis and SAR exploration pave the way for the development of effective and selective PDE4D-targeted therapies, potentially improving outcomes for COPD patients.
ACKNOWLEDGEMENT
We express our sincere thanks to the Department of Pharmaceutical Chemistry, College of Pharmacy, Madras Medical College (MMC), Chennai for providing necessary facilities for the research work.
Conflicts Of Interest
The author declares there is no conflict of interest.
REFERENCES
Neelambari S.*, Priyadharshini R., Mohammed Idrees H., Jawaharsamuvel R., In-Silico Drug Design and Molecular Docking Studies of Some Novel Thiophene Derivatives Targeting Pde4d Inhibitors as Chronic Obstructive Pulmonary Disease Agents, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 1, 444-454. https://doi.org/10.5281/zenodo.14609519