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Drug Res (Stuttg) 2025; 75(01): 21-33
DOI: 10.1055/a-2435-4709
Original Article

In Silico Identification of Promising PDE5 Inhibitors Against Hepatocellular Carcinoma Among Natural Derivatives: A Study Involving Docking and ADMET Analysis

Anil Kumar
1   Patel College of Pharmacy, Madhyanchal Professional University, Ratibad, Bhopal, M.P, India
,
Dharmendra Rajput
1   Patel College of Pharmacy, Madhyanchal Professional University, Ratibad, Bhopal, M.P, India
,
Naveen Gupta
1   Patel College of Pharmacy, Madhyanchal Professional University, Ratibad, Bhopal, M.P, India
,
Harpreet Singh
2   School of Pharmaceutical Sciences, IFTM University, Moradabad, Uttar Pradesh, India
,
Shivani Chopra
3   Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
,
Hitesh Chopra
4   Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
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Abstract

Hepatocellular carcinoma (HCC) represents a significant worldwide health challenge due to its high mortality rate, underscoring the need for advanced therapeutic strategies. This study employs a computer-based method to identify potential phosphodiesterase 5 (PDE5) inhibitors from a library of approved IBS_Scaff 532 natural compounds. PDE5 inhibitors have gained attention for their potential anti-tumor effects. Using molecular docking simulations, the researchers assessed how well these compounds bind to the PDE5 enzyme, which regulates cellular cGMP pathways. Additionally, ADMET profiling predicted the pharmacological and safety properties of candidate inhibitors. Notably, compounds like IBS_NC-0322 and IBS_NC-0320 exhibited favorable ADMET properties and strong binding affinities. These findings suggest their potential as therapeutic agents for treating HCC. While in silico methods serve as valuable screening tools, subsequent experimental validation and clinical trials are essential for confirmation.

Keywords

ADMET - cGMP - hepatocellular carcinoma (HCC) - molecular docking - phosphodiesterase 5


Publication History

Received: 11 June 2024

Accepted: 30 September 2024

Article published online:
12 November 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Kumar A, Rajput DS, Gupta N. Phosphodiesterase (PDE) as a novel target to suppress Carcinoma: a future perspective. YMER Dig 2024; 23: 833-854
    PubMedSearch in Google Scholar
  • 2 Cruz-Burgos M, Losada-Garcia A, Cruz-Hernández CD. et al. New Approaches in Oncology for Repositioning Drugs: The Case of PDE5 Inhibitor Sildenafil. Front Oncol 2021; 11: 627229
    CrossrefPubMedSearch in Google Scholar
  • 3 Tabori NE, Sivananthan G. Seminars in IR Liver Oncology: Treatment Options for Early-Stage Hepatocellular Carcinoma. Semin Inter Rad 2020; 37: 448
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Barone I, Giordano C, Bonofiglio D. et al. Phosphodiesterase type 5 and cancers: progress and challenges. Oncotarg 2017; 8: 99179
    CrossrefPubMedSearch in Google Scholar
  • 5 ElHady AK, El-Gamil DS, Abdel-Halim M. et al. Advancements in Phosphodiesterase 5 Inhibitors: Unveiling Present and Future Perspectives. Pharmaceu 2023; 16: 1266
    PubMedSearch in Google Scholar
  • 6 Terrett NK, Bell AS, Brown D. et al. Sildenafil (Viagra (TM)), a potent and selective inhibitor of type 5 CGMP phosphodiesterase with utility for the treatment of male erectile dysfunction. Bioorg Med Chem Lett 1996; 6: 1819-1824
    CrossrefPubMedSearch in Google Scholar
  • 7 Ghofrani HA, Osterloh IH, Grimminger F. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat Rev Drug Dis 2006; 5: 689
    CrossrefPubMedSearch in Google Scholar
  • 8 Corbin JD, Francis SH. Cyclic GMP phosphodiesterase-5: Target of sildenafil. J Bio Chem 1999; 274: 13729-13732
    CrossrefPubMedSearch in Google Scholar
  • 9 Huang W, Sundquist J, Sundquist K. et al. Phosphodiesterase-5 inhibitors use and risk for mortality and metastases among male patients with colorectal cancer. Nat Comm 2020; 11: 3191
    CrossrefPubMedSearch in Google Scholar
  • 10 Xu L, Sun L, Su P. et al. Identification of Potential Inhibitors of PDE5 based on Structure-based Virtual Screening Approaches. Curr Comp Aid Drug Des 2023; 19: 234-242
    CrossrefPubMedSearch in Google Scholar
  • 11 Yalamarty SSK, Filipczak N, Li X. et al. Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM). Can (Bas) 2023; 15: 2116
    CrossrefPubMedSearch in Google Scholar
  • 12 Agamah FE, Mazandu GK, Hassan R. et al. Computational/in silico methods in drug target and lead prediction. Brief Bio 2020; 21: 1663
    CrossrefPubMedSearch in Google Scholar
  • 13 Guan L, Yang H, Cai Y. et al. ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm 2019; 10: 148
    CrossrefPubMedSearch in Google Scholar
  • 14 Wan H. What ADME tests should be conducted for preclinical studies?. ADMET DMPK 2013; 1: 19-28
    PubMedSearch in Google Scholar
  • 15 Patil VR, Dhote AM, Patil R. et al. Identification of structural scaffold from interbioscreen (IBS) database to inhibit 3CLpro, PLpro, and RdRp of SARS-CoV-2 using molecular docking and dynamic simulation studies. J Biomol Struct Dyn 2023; 41: 13168-13179
    CrossrefPubMedSearch in Google Scholar
  • 16 Franceschin M, Cianni L, Pitorri M. et al. Natural Aromatic Compounds as Scaffolds to Develop Selective G-Quadruplex Ligands: From Previously Reported Berberine Derivatives to New Palmatine Analogues. Mol 2018; 23: 1423
    CrossrefPubMedSearch in Google Scholar
  • 17 Szklarczyk D, Kirsch R, Koutrouli M. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 2023; 51: D638-D646
    CrossrefPubMedSearch in Google Scholar
  • 18 Szklarczyk D, Gable AL, Nastou KC. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021; 49: D605-D612
    CrossrefPubMedSearch in Google Scholar
  • 19 Basar MA, Hosen MF, Kumar Paul B. et al. Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Inform Med Unlocked 2023; 37: 101174
    CrossrefPubMedSearch in Google Scholar
  • 20 Ding Z, Kihara D. Computational identification of protein-protein interactions in model plant proteomes. Sci Rep 2019; 9: 8740
    CrossrefPubMedSearch in Google Scholar
  • 21 Tjitda PJP, Nıtbanı FO, Parıkesıt AA. et al. In Silico Investigation of Tropical Natural Product for Wild-Type and Quadrupole Mutant PfDHFR Inhibitors as Antimalarial Candidates. Trop J Nat Prod Res 2024; 8: 6208-6217
    PubMedSearch in Google Scholar
  • 22 Hsieh CM, Chen CY, Chern JW. et al. Structure of Human Phosphodiesterase 5A1 Complexed with Avanafil Reveals Molecular Basis of Isoform Selectivity and Guidelines for Targeting α-Helix Backbone Oxygen by Halogen Bonding. J Med Chem 2020; 63: 8485-8494
    CrossrefPubMedSearch in Google Scholar
  • 23 Killari KN, Polimati H, Prasanth DSNBK. et al. Salazinic acid attenuates male sexual dysfunction and testicular oxidative damage in streptozotocin-induced diabetic albino rats. RSC Adv 2023; 13: 12991-13005
    CrossrefPubMedSearch in Google Scholar
  • 24 Chen CY, Chang YH, Bau DT. et al. Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis. Acta Pharmacol Sin 2009; 30: 1186
    CrossrefPubMedSearch in Google Scholar
  • 25 Varma AK, Patil R, Das S. et al. Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing. PLoS One 2010; 5: e12029
    CrossrefPubMedSearch in Google Scholar
  • 26 Korb O, Stützle T, Exner TE. PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2006; 4150: 247-258
    PubMedSearch in Google Scholar
  • 27 Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem 2015; 58: 4066-4072
    CrossrefPubMedSearch in Google Scholar
  • 28 Azzam KAL. SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives. Kompleksnoe Ispol′zovanie Mineral′nogo syr′â/Complex Use of Mineral Resources/Mineraldik Shikisattardy Keshendi Paidalanu 2023; 325: 14-21
    CrossrefPubMedSearch in Google Scholar
  • 29 Hutchings DC, Anderson SG, Caldwell JL. et al. Phosphodiesterase-5 inhibitors and the heart: compound cardioprotection?. Heart 2018; 104: 1244-1250
    CrossrefPubMedSearch in Google Scholar
  • 30 Kitta T, Shmohara N. PDE5 Inhibitors. Japanese Journal of Clinical Urology 2023; 70: 404-410
    PubMedSearch in Google Scholar
  • 31 Kayık G, Tüzün N, Durdagi S. Investigation of PDE5/PDE6 and PDE5/PDE11 selective potent tadalafil-like PDE5 inhibitors using combination of molecular modeling approaches, molecular fingerprint-based virtual screening protocols and structure-based pharmacophore development. J Enzyme Inhib Med Chem 2017; 32: 311
    CrossrefPubMedSearch in Google Scholar
  • 32 Torres PHM, Sodero ACR, Jofily P. et al. Key Topics in Molecular Docking for Drug Design. International Journal of Molecular Sciences 2019; 20: 4574
    CrossrefPubMedSearch in Google Scholar
  • 33 Khan MS, Mohammad HA, Shahwan M. et al. Identifying Phosphodiesterase-5 Inhibitors with Drug Repurposing Approach: Implications in Vasodysfunctional Disorders. ChemistryOpen 2024; 13: e202300196
    CrossrefPubMedSearch in Google Scholar
  • 34 ElHady AK, El-Gamil DS, Abdel-Halim M. et al. Advancements in Phosphodiesterase 5 Inhibitors: Unveiling Present and Future Perspectives. Pharmaceuticals 2023; 16: 1266
    CrossrefPubMedSearch in Google Scholar
  • 35 Sethi A, Joshi K, Sasikala K. et al. Molecular Docking in Modern Drug Discovery: Principles and Recent Applications. Drug Discovery and Development – New Advances. IntechOpen 2020; 1-21
    PubMedSearch in Google Scholar
  • 36 Srivastava V, Yadav A, Sarkar P. Molecular docking and ADMET study of bioactive compounds of Glycyrrhiza glabra against main protease of SARS-CoV2. Mater Today Proc 2022; 49: 2999
    CrossrefPubMedSearch in Google Scholar
  • 37 Perkin VO, Antonyan GV, Radchenko EV. et al. Web Services for the Prediction of ADMET Parameters Relevant to the Design of Neuroprotective Drugs. Neuromethods 2023; 203: 465-485
    CrossrefPubMedSearch in Google Scholar
  • 38 Carlo D, Ronchi A, De Carlo A. et al. Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks. Pharmaceutics 2024; 16: 776
    CrossrefPubMedSearch in Google Scholar
  • 39 Kloner RA, Burnett AL, Miner M. et al. Princeton IV consensus guidelines: PDE5 inhibitors and cardiac health. J Sex Med 2024; 21: 90-116
    CrossrefPubMedSearch in Google Scholar
  • 40 Peak TC, Richman A, Gur S. et al. The Role of PDE5 Inhibitors and the NO/cGMP Pathway in Cancer. Sex Med Rev 2016; 4: 74-84
    CrossrefPubMedSearch in Google Scholar
  • 41 Nhat Phuong D, Flower DR, Chattopadhyay S. et al. Towards Effective Consensus Scoring in Structure-Based Virtual Screening. Interdiscip Sci 2023; 15: 131-145
    PubMedSearch in Google Scholar