Login Register Cart 0
Generic placeholder image

Current Enzyme Inhibition

Editor-in-Chief

ISSN (Print): 1573-4080
ISSN (Online): 1875-6662

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

3D-QSAR, Molecular Docking and Pharmacokinetic Studies: In-Silico Approach to Search Novel Inhibitors of 5-Alpha Reductase for Treatment of Benign Prostatic Hyperplasia

Author(s): Harnoor Kaur, Neelima Dhingra*, Alka Kumari, Priyanka Rana and Tanzeer Kaur

Volume 18, Issue 3, 2022

Published on: 17 October, 2022

Page: [226 - 244] Pages: 19

DOI: 10.2174/1573408018666220914102231

Price: $65

Abstract

Aim: This study aims to identify novel steroidal 5-alpha reductase (5AR) inhibitors using computational approaches.

Objectives: The objective of this study is to exploit the steroidal nuclei for possible modifications by creating a library of 17-oximino-5-androsten-3-carboxamide derivatives and identify potent 5AR inhibitors based on docking and pharmacokinetic parameters.

Background: Benign prostatic hyperplasia (BPH) is a condition of aged men that is characterized by lower urinary tract symptoms. Excessive production of dihydrotestosterone (DHT) from testosterone has been found to play a major role in its pathophysiology. Studies targeting the 5AR enzyme have so far resulted in the development of two clinically approved 5AR inhibitors.

Methods: Atom-based three-dimensional-quantitative structure-activity relationship (3D-QSAR) models have been developed using a selected series of steroidal derivatives as 5AR inhibitors to elucidate the structural properties required for 5AR inhibitory activities. Further in‒silico studies (molecular docking and pharmacokinetic properties like adsorption, distribution, metabolism, and excretion) of 17- oximino-5-androsten-3-carboxamide derivatives have also been carried out to identify the binding orientation and protein-ligand interactions responsible for the exhibited activity and drug like properties.

Results: The best 3D-QSAR model was generated using Partial Least Square method with an excellent correlation coefficient (R², training set) of 0.882, standard deviation (SD) of 0.09, and a predicted coefficient (Q², test set) of 0.814. Docking analysis indicated that the designed series of compounds have comparable binding affinity from -8.961 to -8.017 to the protein and suggested that hydrophobic and electrostatic moieties can have a key role in the inhibition mechanism.

Conclusion: 3D-QSAR, molecular docking and pharmacokinetic studies indicated that 17-oximino-5- androsten-3-carboxamide derivatives could be studied further to provide a new strategy for the treatment of BPH.

Keywords: Benign prostatic hyperplasia, 17-oximino-5-androsten-3-carboxamide derivatives, molecular docking, quantitative structure activity relationship, pharmacokinetic studies, testosterone, dihydrotestosterone.

« Previous Next »
Graphical Abstract

[1]
Dhingra R, Malhotra M, Sharma V, Bhardwaj TR, Dhingra N. Exploration of novel 5α-reductase inhibitors for benign prostatic hyperplasia by 2D/3D QSAR, cytotoxicity pre-ADME and docking studies. Curr Top Med Chem 2019; 18(32): 2816-34.
[http://dx.doi.org/10.2174/1568026619666190119145959] [PMID: 30659542]
[2]
Aggarwal S, Mahapatra MK, Kumar R. et al. Synthesis and biological evaluation of 3-tetrazolo steroidal analogs: Novel class of 5α-reductase inhibitors. Bioorg Med Chem 2016; 24(4): 779-88.
[http://dx.doi.org/10.1016/j.bmc.2015.12.048] [PMID: 26780831]
[3]
Rasmusson GH, Reynolds GF, Utne T. et al. Azasteroids as inhibitors of rat prostatic 5.alpha.-reductase. J Med Chem 1984; 27(12): 1690-701.
[http://dx.doi.org/10.1021/jm00378a028] [PMID: 6502599]
[4]
Li J, Ding Z, Wang Z. et al. Androgen regulation of 5α-reductase isoenzymes in prostate cancer: implications for prostate cancer preven-tion. PLoS One 2011; 6(12): e28840.
[http://dx.doi.org/10.1371/journal.pone.0028840]
[5]
Uemura M, Tamura K, Chung S. et al. Novel 5 α-steroid reductase (SRD5A3, type-3) is overexpressed in hormone-refractory prostate cancer. Cancer Sci 2008; 99(1): 81-6.
[PMID: 17986282]
[6]
Wilson JD. The pathogenesis of benign prostatic hyperplasia. Am J Med 1980; 68(5): 745-56.
[http://dx.doi.org/10.1016/0002-9343(80)90267-3] [PMID: 6155068]
[7]
Davison S, Bell R. Androgen Physiology. Semin Reprod Med 2006; 24(2): 071-7. http://dx.doi.org/10.1055/s-2006-939565 PMID: 16633980
[8]
Bruchovsky N, Sadar MD, Akakura K, Goldenberg SL, Matsuoka K, Rennie PS. Characterization of 5α-reductase gene expression in stroma and epithelium of human prostate. J Steroid Biochem Mol Biol 1996; 59(5-6): 397-404.
[http://dx.doi.org/10.1016/S0960-0760(96)00125-2] [PMID: 9010345]
[9]
Aggarwal S, Thareja S, Verma A, Bhardwaj TR, Kumar M. An overview on 5α-reductase inhibitors. Steroids 2010; 75(2): 109-53.
[http://dx.doi.org/10.1016/j.steroids.2009.10.005] [PMID: 19879888]
[10]
Kenny B, Ballard S, Blagg J, Fox D. Pharmacological options in the treatment of benign prostatic hyperplasia. J Med Chem 1997; 40(9): 1293-315.
[http://dx.doi.org/10.1021/jm960697s] [PMID: 9135028]
[11]
Veselovsky A, Ivanov A. Strategy of computer-aided drug design. Curr Drug Targets Infect Disord 2003; 3(1): 33-40.
[http://dx.doi.org/10.2174/1568005033342145] [PMID: 12570731]
[12]
Sarithamol S, Pushpa VL, Divya V, Manoj KB. Comparative QSAR model generation using pyrazole derivatives for screening Janus ki-nase‐1 inhibitors. Chem Biol Drug Des 2020; 95(5): 503-19.
[http://dx.doi.org/10.1111/cbdd.13667] [PMID: 32022397]
[13]
Costa PCS, Evangelista JS, Leal I, Miranda PCML. Chemical graph theory for property modeling in QSAR and QSPR—Charming QSAR & QSPR. Mathematics 2020; 9(1): 60-89.
[http://dx.doi.org/10.3390/math9010060]
[14]
Ooms F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr Med Chem 2000; 7(2): 141-58.
[http://dx.doi.org/10.2174/0929867003375317] [PMID: 10637360]
[15]
Amzel LM. Structure-based drug design. Curr Opin Biotechnol 1998; 9(4): 366-9.
[http://dx.doi.org/10.1016/S0958-1669(98)80009-8] [PMID: 9720263]
[16]
Das PS. A review on computer aided drug design in drug discovery. World J Pharm Pharm Sci 2017; 6(7): 279-91.
[http://dx.doi.org/10.20959/wjpps20177-9450]
[17]
Richards WG. Computer-aided drug design. Pure Appl Chem 1994; 66(8): 1589-96.
[http://dx.doi.org/10.1351/pac199466081589]
[18]
Guedes IA, de Magalhães CS, Dardenne LE. Receptor–ligand molecular docking. Biophys Rev 2014; 6(1): 75-87.
[http://dx.doi.org/10.1007/s12551-013-0130-2] [PMID: 28509958]
[19]
Gomeni R, Bani M, D’Angeli C, Corsi M, Bye A. Computer-assisted drug development (CADD): an emerging technology for designing first-time-in-man and proof-of-concept studies from preclinical experiments. Eur J Pharm Sci 2001; 13(3): 261-70.
[http://dx.doi.org/10.1016/S0928-0987(01)00111-7] [PMID: 11384848]
[20]
Mukesh R. Molecular docking: A review. IJRAP 2011; 2: 1746-51.
[21]
Steven L, Dixon AM, Smondyrev EH, Knoll SN, Rao DE, Shaw RA. PHASE: a new engine for pharmacophore perception, 3DQSAR model development, and 3D database screening: Methodology and preliminary results. J Comput Aided Mol Des 2066(20): 647-71.
[22]
Caldwell G, Yan Z, Tang W, Dasgupta M, Hasting B. ADME optimization and toxicity assessment in early- and late-phase drug discovery. Curr Top Med Chem 2009; 9(11): 965-80.
[http://dx.doi.org/10.2174/156802609789630929] [PMID: 19747120]
[23]
Shah A, Lobo R, Krishnadas N, Pai A. Pharmacophore and atom-based 3D QSAR studies on the novel 5-alpha-reductase inhibitors. Indi-an J Pharma Edu Res 2018; 52(S4): S296-302.
[http://dx.doi.org/10.5530/ijper.52.4s.110]
[24]
Varpe BD, Jadhav SB, Chatale BC, Mali AS, Jadhav SY, Kulkarni AA. 3D-QSAR and Pharmacophore modeling of 3,5-disubstituted indole derivatives as Pim kinase inhibitors. Struct Chem 2020; 31(5): 1675-90.
[http://dx.doi.org/10.1007/s11224-020-01503-1]
[25]
Hajduk PJ, Huth JR, Tse C. Predicting protein druggability. Drug Discov Today 2005; 10(23-24): 1675-82.
[http://dx.doi.org/10.1016/S1359-6446(05)03624-X] [PMID: 16376828]
[26]
Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 2004; 1(4): 337-41.
[http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID: 24981612]
[27]
Hop C, Cole M, Davidson R. et al. High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models. Curr Drug Metab 2008; 9(9): 847-53.
[http://dx.doi.org/10.2174/138920008786485092] [PMID: 18991580]
[28]
Singh B, Paul Y, Dhake A. In silico quantitative structure pharmacokinetic relationship modeling of quinolones: Apparent volume of dis-tribution. Asian J Pharm 2009; 3(3): 202-63.
[http://dx.doi.org/10.4103/0973-8398.56298]

Rights & Permissions Print Cite
Article Metrics
17
2
© 2024 Bentham Science Publishers | Privacy Policy