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Abstract

Parkinson's disease (PD) is a neurodegenerative disorder marked by dopamine deficiency in the brain. Catechol-O-methyltransferase (COMT) is an enzyme responsible for the degradation of dopamine, and its inhibition can prolong dopamine activity. The current study focuses on designing and evaluating derivatives of 3,5-dinitrocatechol (DNC) through in silico methods for potential COMT inhibition. The crystal structure of the COMT enzyme (PDB: 4XUC) was used as the receptor for molecular docking studies. Entacapone, a standard COMT inhibitor, was used as a reference drug. Six derivatives of DNC were designed and analyzed using PyRx for docking, SwissADME for pharmacokinetic analysis, and ProTox-II for toxicity prediction. Among the derivatives, D6 showed the highest binding affinity (-6.6 kcal/mol) compared to Entacapone (-5.5 kcal/mol), with good ADME properties and minimal toxicity. The results indicate D6 as a promising COMT inhibitor candidate for Parkinson’s therapy.

Keywords

Parkinson’s Disease, COMT Inhibitor, 3,5-Dinitrocatechol, Molecular Docking, In Silico, ADME, ProTox.

Introduction

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function due to the depletion of dopamine in the brain. Among the enzymes involved in dopamine metabolism, Catechol-O-Methyltransferase (COMT) plays a significant role by methylating catecholamines such as dopamine, thereby decreasing their availability. The inhibition of COMT has emerged as an effective therapeutic strategy to prolong dopamine activity in PD patients. Currently available COMT inhibitors like Entacapone exhibit limited CNS penetration, which compromises their therapeutic efficacy. Therefore, the development of novel COMT inhibitors with better pharmacokinetic and pharmacodynamic profiles is of great interest. Recent advancements in computer-aided drug design (CADD) have enabled the use of in silico approaches such as molecular docking, ADMET prediction, and toxicity screening to identify promising drug candidates. The present study aims to design, screen, and evaluate derivatives of 3,5-dinitrocatechol (DNC), a known COMT-inhibiting scaffold, for their potential to serve as effective and safer alternatives to existing COMT inhibitors, using structure-based virtual screening methodologies involving the crystal structure of human COMT (PDB ID: 4XUC).

2. MATERIALS AND METHODS

Software Tools Used:

  • ChemSketch: Used to draw 2D structures, generate SMILES, and IUPAC names.
  • Avogadro: For geometry optimization and conversion to 3D formats.
  • PyRx: For molecular docking and virtual screening.
  • Discovery Studio Visualizer: For docking result analysis.
  • SwissADME & pkCSM: For physicochemical and ADME property predictions.
  • ProTox-II: For toxicity analysis.

Ligand Preparation: The ligands (DNC and its derivatives) were drawn using ChemSketch, optimized using Avogadro, and converted into PDB format. SMILES notations were generated and used for ADME and toxicity studies.

Fig no.1 Structure of Ligand

Receptor Preparation: The COMT enzyme (PDB ID: 4XUC) was retrieved from the RCSB Protein Data Bank. Preprocessing involved removal of water molecules, heteroatoms, and charge stabilization using AutoDock tools within PyRx.

Fig no.2- Structure of receptor 4xuc

Docking Methodology: The docking was performed using PyRx (Vina Wizard). All ligands were converted to PDBQT format. Binding affinities were recorded and interactions analyzed using Discovery Studio Visualizer.

Fig no.3-Receptor (4xuc)- Ligand (standard drug- Entacapone) visualisation

Fig no.4-Receptor (4xuc)- Ligand(D6) visualisation

Interpretation:

  • Both Entacapone and D6 exhibit strong binding interactions with COMT, confirming their inhibitory potential.
  • D6 forms additional hydrogen bonds compared to Entacapone, indicating potentially higher affinity and stability in the active site.
  • This suggests that D6 could serve as an improved COMT inhibitor for Parkinson’s disease treatment, pending further validation through molecular dynamics simulations and ADMET analysis.
  1. RESULTS AND DISCUSSION

Standard drug:

 

Name

Structure

IUPAC Name

Smile Notation

Binding Affinity

Entacapone

 

 

 

(2E)-2-cyano-3-(3,4-dihydroxy-5-nitrophenyl)-N,N-diethylprop-2-enamide

 

 

 

[O][N+](=O)c1cc(cc(O)c1O)\C=C(/C#N)C(=O)N(CC)CC

 

-5.5

Ligand and it’s derivatives:

A structure-based virtual screening approach was conducted to identify potential COMT inhibitors using Entacapone (a marketed drug) as the active ligand. Various derivatives were screened to evaluate their binding affinity to Catechol-O-Methyltransferase (COMT, PDB: 4XUC), a key target in Parkinson’s disease therapy.  Among all the modified derivatives, Derivative 6 exhibited the best binding affinity, outperforming Entacapone. The binding affinity of Entacapone was -5.5 kcal/mol, while D6 demonstrated a stronger binding affinity of -6.6 kcal/mol, indicating enhanced stability and potential efficacy as a COMT inhibitor.

 

Sr No.

Structure

IUPAC Name

Smile Notation

Binding Affinity

D1

 

 

 

 

3,5-dinitrobenzene-1,2-diol

 

 

 

Oc1cc(cc([N+](=O)[O])c1O)[N+](=O)[O-]

 

 

 

-5.0

D2

 

 

 

 

 

3,4-dihydroxy-5-nitrobenzaldehyde

 

 

 

 

O=Cc1cc(O)c(O)c(c1)[N+](=O)[O-]

 

 

 

 

-5.1

 

 

D3

 

 

 

 

 

3,4-dihydroxy-5-nitrobenzoyl fluoride

 

 

FC(=O)c1cc(O)c(O)c(c1)[N+](=O)[O-]

 

 

 

-5.0

D4

 

 

 

 

(3,4-dihydroxy-5-nitrophenyl)(2-hydroxyphenyl)methanone

 

 

O=[N+]([O-])c1cc(cc(O)c1O)C(=O)c1ccccc1O

 

 

 

-6.0

D5

 

 

 

 

(3,4-dihydroxy-5-nitrophenyl)(3-fluoro-2-hydroxyphenyl)methanone

 

 

O=[N+]([O])c1cc(cc(O)c1O)C(=O)c1cccc(F)c1O

 

 

 

-6.0

D6

 

 

(3,4-dihydroxy-5-nitrophenyl)[3-(fluoromethyl)-2-hydroxyphenyl]methanone

 

O=[N+]([O-])c1cc(cc(O)c1O)C(=O)c1cccc(CF)c1O

 

-6.6

1)Standard drug Entacapone

    1. SWISS ADME (Physicochemical Properties & Pharmacokinetics)

Entacapone is highly absorbable, does not cross the BBB, and has minimal CYP-mediated interactions, making it a reliable peripheral COMT inhibitor.

B) Toxicity Prediction (PRO TOX III)

The toxicity profile shows that Entacapone has potential respiratory toxicity (active, 0.89), mutagenicity (active, 0.65), and stress response activation (Nrf2/ARE & HSE pathways active, 0.92)

  1. 3,5 dinitrocatechol Derivative 6 which have more binding affinity
  1. SWISS ADME (Physicochemical Properties & Pharmacokinetics)

High GI Absorption → Good oral bioavailability.

  1. Lipinski's Rule of 5 → No violations, indicating good drug-likeness.
  2. CYP2C9 & CYP3A4 Inhibition → Possible metabolic interactions (can be an advantage for sustained drug action).
  3. Lead-likeness & Bioavailability Score (0.55) → Good potential as a drug candidate.
  4. Moderate Solubility → Sufficient for bioavailability.

B) Toxicity Prediction (PRO TOX III)

D6 has a better toxicity profile,than entacapone with nutritional toxicity & immunotoxicity. This makes it a potentially safer candidate with only mild risks.

6. CONCLUSION

The D6 derivative of 3,5-dinitrocatechol shows strong potential as a COMT inhibitor for Parkinson’s disease, with a better binding affinity (-6.6 kcal/mol) compared to Entacapone. Its pharmacokinetic profile is promising, with high GI absorption. Additionally, it exhibits minimal CYP enzyme inhibition, reducing the risk of drug-drug interactions. Toxicity analysis suggests D6 is generally safe, with no neurotoxicity, carcinogenicity, or BBB barrier concerns, though mild risks exist for nutitional & immunotoxicity.

Overall, D6 is a strong candidate for further development as a Parkinson’s therapy, offering improved efficacy and a favorable safety profile.

REFERENCES

  1. Silva R, Silva C, Soares-da-Silva P, et al. Discovery of small molecules as membrane-bound catechol-O-methyltransferase inhibitors using a pharmacophore-based virtual screening approach. Pharmaceuticals. 2022;15(1):51.
  2. Kumar A, Gupta V, Rana M, et al. Evaluation of nitrocatechol chalcone and pyrazoline derivatives as catechol-O-methyltransferase inhibitors: synthesis, biological evaluation, and molecular docking studies. Bioorganic & Medicinal Chemistry Letters. 2020;30(13):127201.
  3. Patel CN, Georrge JJ, Modi KM, et al. Pharmacophore-based virtual screening of catechol-O-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease. Journal of Biomolecular Structure and Dynamics. 2018;36(15):3938-3957. Taylor & Francis Online
  4. Engelbrecht I, Petzer JP, Petzer A. Nitrocatechol derivatives of chalcone as inhibitors of monoamine oxidase and catechol-O-methyltransferase. Central Nervous System Agents in Medicinal Chemistry. 2018;18(2):136-146. EurekaSelect
  5. Männistö PT, Kaakkola S. Catechol-O-methyltransferase (COMT): biochemistry, molecular biology, pharmacology, and clinical efficacy of the new selective COMT inhibitors. Pharmacological Reviews. 1999;51(4):593-628.
  6. Lotta T, Vidgren J, Tilgmann C, et al. Kinetics of human soluble and membrane-bound catechol-O-methyltransferase: a revised mechanism and description of the thermolabile variant of the enzyme. Biochemistry. 1995;34(13):4202-4210.
  7. Cheng YC, Prusoff WH. Relationship between the inhibition constant (Ki) and the concentration of inhibitor which causes 50 per cent inhibition (IC50) of an enzymatic reaction. Biochemical Pharmacology. 1973;22(23):3099-3108.
  8. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies. 2004;1(4):337-341.
  9. Gomes MB, et al. COMT polymorphisms and their role in the development of neurodegenerative diseases. Neuroscience Letters. 2021;740:135427.
  10. Salminen M, et al. Catechol-O-methyltransferase inhibitors in Parkinson's disease. Current Neuropharmacology. 2005;3(3):217-229.
  11. Martinez A, et al. Harmine derivatives as potential inhibitors for neurological disorders. Bioorganic & Medicinal Chemistry. 2012;20(23):7034-7042.
  12. Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry. 2010;53(7):2719-2740.
  13. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 2010;31(2):455-461.
  14. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017;7:42717.
  15. Adasme MF, et al. BindingDB and its role in drug design. Nucleic Acids Research. 2021;49(D1):D1082-D1089.
  16. Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. Journal of Combinatorial Chemistry. 1999;1(1):55-68.
  17. Guner OF. Pharmacophore perception, development, and use in drug design. International University Line. 2000.
  18. Singh J, Petter RC, Baillie TA, Whitty A. The resurgence of covalent drugs. Nature Reviews Drug Discovery. 2011;10:307-317.
  19. Weinshilboum R, Otterness D, Szumlanski C. Methylation pharmacogenetics: catechol O-methyltransferase, thiopurine methyltransferase, and histamine N-methyltransferase. Annual Review of Pharmacology and Toxicology. 1999;39(1):19-52.
  20. Reilly MT, et al. COMT Val158Met and cognition: an integrative analysis of neuroimaging and behavioral studies. Neuroscience & Biobehavioral Reviews. 2020;108:27-45.
  21. Lewis DA, et al. Altered cortical glutamate neurotransmission in schizophrenia: evidence from postmortem studies. Biological Psychiatry. 2012;71(12):978-987.
  22. Sitaram BR, et al. Antiparkinson activity of harmine and related beta-carbolines. Journal of Pharmacy and Pharmacology. 1982;34(7):473-475.
  23. Nair AB, Jacob S. A simple practice guide for dose conversion between animals and human. J Basic Clin Pharm. 2016;7(2):27–31.
  24. Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem. 2008;8(18):1555–1572.
  25. Jain AN. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel. 2004;7(4):396–403.
  26. Zhou Y, et al. A systematic review on drug-likeness evaluation of natural products. Front Pharmacol. 2020;11:579536.
  27. Jain R, et al. Pharmacophore modeling and virtual screening for identification of catechol-O-methyltransferase inhibitors. Mol Divers. 2021;25:1805–1819.
  28. Muralidharan B, et al. Computational screening and ADMET prediction of COMT inhibitors from plant-derived flavonoids. J Mol Graph Model. 2022;111:108079.
  29. Zhang H, et al. Recent advances in the discovery and development of catechol-O-methyltransferase inhibitors. Expert Opin Ther Pat. 2020;30(3):173–186.
  30. Huang WJ, et al. Parkinson’s disease: drug therapies in the clinical trial pipeline. J Neurol Neurophysiol. 2016;7(6):1000401.
  31. Obach RS, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther. 1997;283(1):46–58.
  32. Shen J, et al. Structure-based virtual screening and molecular dynamics simulation studies for novel COMT inhibitors. Int J Mol Sci. 2020;21(19):7010.
  33. Wang Q, et al. The mechanism of COMT inhibition and the effect of nitro-substituted catechols: an in silico investigation. Comput Biol Chem. 2019;83:107105.
  34. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3(8):673–683.
  35. Varma MV, et al. Predicting clearance mechanisms in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):3785–3802.
  36. PreADMET Web Tool. Online platform for ADMET prediction. Available from: http://preadmet.bmdrc.kr
  37. SwissADME Web Tool. Swiss Institute of Bioinformatics. Available from: http://www.swissadme.ch
  38. Molinspiration. Molecular properties and bioactivity prediction tool. Available from: https://www.molinspiration.com
  39. ChemSpider. A free chemical structure database. Available from: http://www.chemspider.com
  40. Protein Data Bank. Repository for 3D structural data of large biological molecules. Available from: https://www.rcsb.org/structure/4XUC
  41. PubChem. Open chemistry database. Available from: https://pubchem.ncbi.nlm.nih.gov
  42. ZINC Database. Free database of commercially available compounds for virtual screening. Available from: http://zinc.docking.org
  43. UCSF Chimera. Visualization system for exploratory research and analysis. Available from: https://www.cgl.ucsf.edu/chimera
  44. Discovery Studio Visualizer. Visualization tool for protein-ligand interaction studies. BIOVIA, Dassault Systèmes.
  45. Molecular Operating Environment (MOE). Chemical computing group software suite. Available from: https://www.chemcomp.com
  46. AutoDock Tools. Molecular modeling software for docking studies. Available from: https://autodock.scripps.edu
  47. Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron. 1980;36(22):3219–3228.
  48. Pires DE, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066–4072.
  49. Pang YP, et al. Inhibition of catechol-O-methyltransferase by nitrocatechols: a molecular insight. Proteins. 2008;71(2):818–827.
  50. Lövheim H, et al. COMT genotype is associated with cerebrospinal fluid dopamine metabolite levels. PLoS ONE. 2011;6(1):e15291.
  51. Ellis JM, Fell MJ. Current approaches to the treatment of Parkinson’s Disease. Bioorg Med Chem Lett. 2017;27(17):4247–4255.
  52. Chen JJ. Parkinson’s disease pharmacotherapy: past, present, and future. Drugs. 2021;81(2):201–213.
  53. Feng Z, et al. Role of nitro and hydroxyl groups in catechol derivatives for COMT inhibition: insights from docking and molecular dynamics. J Mol Model. 2021;27(8):210.
  54. Prasanna S, Doerksen RJ. Topological polar surface area: a useful descriptor in 2D-QSAR. Curr Med Chem. 2009;16(1):21–41..

Reference

  1. Silva R, Silva C, Soares-da-Silva P, et al. Discovery of small molecules as membrane-bound catechol-O-methyltransferase inhibitors using a pharmacophore-based virtual screening approach. Pharmaceuticals. 2022;15(1):51.
  2. Kumar A, Gupta V, Rana M, et al. Evaluation of nitrocatechol chalcone and pyrazoline derivatives as catechol-O-methyltransferase inhibitors: synthesis, biological evaluation, and molecular docking studies. Bioorganic & Medicinal Chemistry Letters. 2020;30(13):127201.
  3. Patel CN, Georrge JJ, Modi KM, et al. Pharmacophore-based virtual screening of catechol-O-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease. Journal of Biomolecular Structure and Dynamics. 2018;36(15):3938-3957. Taylor & Francis Online
  4. Engelbrecht I, Petzer JP, Petzer A. Nitrocatechol derivatives of chalcone as inhibitors of monoamine oxidase and catechol-O-methyltransferase. Central Nervous System Agents in Medicinal Chemistry. 2018;18(2):136-146. EurekaSelect
  5. Männistö PT, Kaakkola S. Catechol-O-methyltransferase (COMT): biochemistry, molecular biology, pharmacology, and clinical efficacy of the new selective COMT inhibitors. Pharmacological Reviews. 1999;51(4):593-628.
  6. Lotta T, Vidgren J, Tilgmann C, et al. Kinetics of human soluble and membrane-bound catechol-O-methyltransferase: a revised mechanism and description of the thermolabile variant of the enzyme. Biochemistry. 1995;34(13):4202-4210.
  7. Cheng YC, Prusoff WH. Relationship between the inhibition constant (Ki) and the concentration of inhibitor which causes 50 per cent inhibition (IC50) of an enzymatic reaction. Biochemical Pharmacology. 1973;22(23):3099-3108.
  8. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies. 2004;1(4):337-341.
  9. Gomes MB, et al. COMT polymorphisms and their role in the development of neurodegenerative diseases. Neuroscience Letters. 2021;740:135427.
  10. Salminen M, et al. Catechol-O-methyltransferase inhibitors in Parkinson's disease. Current Neuropharmacology. 2005;3(3):217-229.
  11. Martinez A, et al. Harmine derivatives as potential inhibitors for neurological disorders. Bioorganic & Medicinal Chemistry. 2012;20(23):7034-7042.
  12. Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry. 2010;53(7):2719-2740.
  13. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 2010;31(2):455-461.
  14. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017;7:42717.
  15. Adasme MF, et al. BindingDB and its role in drug design. Nucleic Acids Research. 2021;49(D1):D1082-D1089.
  16. Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. Journal of Combinatorial Chemistry. 1999;1(1):55-68.
  17. Guner OF. Pharmacophore perception, development, and use in drug design. International University Line. 2000.
  18. Singh J, Petter RC, Baillie TA, Whitty A. The resurgence of covalent drugs. Nature Reviews Drug Discovery. 2011;10:307-317.
  19. Weinshilboum R, Otterness D, Szumlanski C. Methylation pharmacogenetics: catechol O-methyltransferase, thiopurine methyltransferase, and histamine N-methyltransferase. Annual Review of Pharmacology and Toxicology. 1999;39(1):19-52.
  20. Reilly MT, et al. COMT Val158Met and cognition: an integrative analysis of neuroimaging and behavioral studies. Neuroscience & Biobehavioral Reviews. 2020;108:27-45.
  21. Lewis DA, et al. Altered cortical glutamate neurotransmission in schizophrenia: evidence from postmortem studies. Biological Psychiatry. 2012;71(12):978-987.
  22. Sitaram BR, et al. Antiparkinson activity of harmine and related beta-carbolines. Journal of Pharmacy and Pharmacology. 1982;34(7):473-475.
  23. Nair AB, Jacob S. A simple practice guide for dose conversion between animals and human. J Basic Clin Pharm. 2016;7(2):27–31.
  24. Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem. 2008;8(18):1555–1572.
  25. Jain AN. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel. 2004;7(4):396–403.
  26. Zhou Y, et al. A systematic review on drug-likeness evaluation of natural products. Front Pharmacol. 2020;11:579536.
  27. Jain R, et al. Pharmacophore modeling and virtual screening for identification of catechol-O-methyltransferase inhibitors. Mol Divers. 2021;25:1805–1819.
  28. Muralidharan B, et al. Computational screening and ADMET prediction of COMT inhibitors from plant-derived flavonoids. J Mol Graph Model. 2022;111:108079.
  29. Zhang H, et al. Recent advances in the discovery and development of catechol-O-methyltransferase inhibitors. Expert Opin Ther Pat. 2020;30(3):173–186.
  30. Huang WJ, et al. Parkinson’s disease: drug therapies in the clinical trial pipeline. J Neurol Neurophysiol. 2016;7(6):1000401.
  31. Obach RS, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther. 1997;283(1):46–58.
  32. Shen J, et al. Structure-based virtual screening and molecular dynamics simulation studies for novel COMT inhibitors. Int J Mol Sci. 2020;21(19):7010.
  33. Wang Q, et al. The mechanism of COMT inhibition and the effect of nitro-substituted catechols: an in silico investigation. Comput Biol Chem. 2019;83:107105.
  34. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3(8):673–683.
  35. Varma MV, et al. Predicting clearance mechanisms in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):3785–3802.
  36. PreADMET Web Tool. Online platform for ADMET prediction. Available from: http://preadmet.bmdrc.kr
  37. SwissADME Web Tool. Swiss Institute of Bioinformatics. Available from: http://www.swissadme.ch
  38. Molinspiration. Molecular properties and bioactivity prediction tool. Available from: https://www.molinspiration.com
  39. ChemSpider. A free chemical structure database. Available from: http://www.chemspider.com
  40. Protein Data Bank. Repository for 3D structural data of large biological molecules. Available from: https://www.rcsb.org/structure/4XUC
  41. PubChem. Open chemistry database. Available from: https://pubchem.ncbi.nlm.nih.gov
  42. ZINC Database. Free database of commercially available compounds for virtual screening. Available from: http://zinc.docking.org
  43. UCSF Chimera. Visualization system for exploratory research and analysis. Available from: https://www.cgl.ucsf.edu/chimera
  44. Discovery Studio Visualizer. Visualization tool for protein-ligand interaction studies. BIOVIA, Dassault Systèmes.
  45. Molecular Operating Environment (MOE). Chemical computing group software suite. Available from: https://www.chemcomp.com
  46. AutoDock Tools. Molecular modeling software for docking studies. Available from: https://autodock.scripps.edu
  47. Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron. 1980;36(22):3219–3228.
  48. Pires DE, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066–4072.
  49. Pang YP, et al. Inhibition of catechol-O-methyltransferase by nitrocatechols: a molecular insight. Proteins. 2008;71(2):818–827.
  50. Lövheim H, et al. COMT genotype is associated with cerebrospinal fluid dopamine metabolite levels. PLoS ONE. 2011;6(1):e15291.
  51. Ellis JM, Fell MJ. Current approaches to the treatment of Parkinson’s Disease. Bioorg Med Chem Lett. 2017;27(17):4247–4255.
  52. Chen JJ. Parkinson’s disease pharmacotherapy: past, present, and future. Drugs. 2021;81(2):201–213.
  53. Feng Z, et al. Role of nitro and hydroxyl groups in catechol derivatives for COMT inhibition: insights from docking and molecular dynamics. J Mol Model. 2021;27(8):210.
  54. Prasanna S, Doerksen RJ. Topological polar surface area: a useful descriptor in 2D-QSAR. Curr Med Chem. 2009;16(1):21–41.

Photo
D. P. Kawade
Corresponding author

Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, MS, India.

Photo
Y. S. Bhattad
Co-author

Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, MS, India.

Photo
S. R. Shahu
Co-author

Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, MS, India.

Photo
S. G. Kshirsagar
Co-author

Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, MS, India.

Photo
S.R. Bagh
Co-author

Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, MS, India.

D. P. Kawade*, Y. S. Bhattad, S. R. Shahu, S. G. Kshirsagar, S.R. Bagh, Design and In Silico Study of 3,5-Dinitrocatechol and Its Derivatives for Antiparkinsonian Activity, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 4, 2918-2931 https://doi.org/10.5281/zenodo.15273158

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Computer Aided and AI based Drug Design ...
Chaitali Ingawale , Sandhya Khomane , Rupali Kharat , Shrushti Uchale , ...
Evaluation Of Anti-Parkinsonian Effect Of Ethanolic Leaf Extract Of Allium, Jacq...
Adiba Afreen, Shaik Mohd Khasim, Saniya Zainab, Shaik Shahnaz Begum, Aman Khan, Mohd Ajaz Kaiser, Ka...
In Silico Design And ADME Study Of Novel Benzimidazole Containing Derivatives As...
Sandip S. Chaudhari , Megha R. Mahajan , Puja R. Khodape, Diksha N. Koli, Tarannum R. Sayyyad , Pras...