Login Register Cart 0
Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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

QSAR Analysis, Molecular Docking and ADME Studies of Thiobarbituric Acid Derivatives as Thymidine Phosphorylase Inhibitors: A Rational Approach to Anticancer Drug Design by in silico Modelling

Author(s): Pooja S. Meher, Janhavi R. Rao* and Dileep Kumar

Volume 20, Issue 2, 2023

Published on: 22 June, 2022

Page: [192 - 200] Pages: 9

DOI: 10.2174/1570180819666220509103648

Price: $65

conference banner
Abstract

Background: Thymidine Phosphorylase (TP) is an imperative target for cancer researchers. In the current research, quantitative structure-activity relationship (QSAR) models were demonstrated to identify new TP inhibitors.

Objective: The main objective is to perform a QSAR study on a series of 19 derivatives of thiobarbituric acid and new molecules designed and dock to check potency and efficacy for anticancer activity.

Methods: Multiple linear regression analysis (MLR) was used to establish a two-dimensional quantitative structure-activity relationship (2D-QSAR) with regression coefficient values of 0.9781, 0.9513, and 0.9819 for the training set (r2), leave-one-out (LOO) dependent internal regression (q2), and external test set regression (r2 _pred), respectively. Three-dimensional quantitative structure-activity relationship (3DQSAR) model, obtained by using the simulated annealing k nearest neighbour (SA-KNN) method (q2 = 0.7880). Newly designed molecules were subjected to docking studies with 7-deazaxanthine taken as standard.

Results: Molecular modelling, structure-based drug design and docking study analysis were performed. The new chemical entities (NCE’s) designed, docked towards targeted receptor and show good results as compared to the standard 7-deazaxanthine. It was found that these molecules bind similar amino acid pocket regions as that of standard. Molecules bind at the active site of TP enzyme involving H bond interactions with shorter distances showed greater affinity. At last, the oral bioavailability and toxic effect were evaluated as absorption, distribution, metabolism, and elimination (ADME) studies by computational means of the Qikprop tool of Schrodinger.

Conclusion: One of the most successful and fast-increasing methodologies is molecular modelling. It not only aids in the prediction of specific target compounds but also aids in the cost reduction of valuable substances. QSAR and docking study was performed, and most of the molecules have shown good dock scores. Based on these results, NCE’s for anticancer activity were successfully designed and analysed in this research work which will be helpful for effective drug synthesis with less toxicity in the future.

Others: 2D QSAR model was generated by three methods, and the best one was selected for further study. NCEs were planned based on descriptors such as topological, electrostatic, steric, and hydrophobic substitutions around the core.

Keywords: Thymidine phosphorylase inhibitors, quantitative structure-activity relationship, thiobarbituric acid, docking, ADME, anticancer.

« Previous Next »
Graphical Abstract

[1]
Hanf, V.; Kreienberg, R. Corpus uteri. In: Die Gynäkologie; Kaufmann, M.; Costa, S.D.; Scharl, A., Eds.; Springer: Berlin, Heidelberg, 2003.
[http://dx.doi.org/10.1007/978-3-662-11496-4_24]
[2]
Focher, F.; Spadari, S. Thymidine phosphorylase: a two-face Janus in anticancer chemotherapy. Curr. Cancer Drug Targets, 2001, 1(2), 141-153.
[http://dx.doi.org/10.2174/1568009013334232] [PMID: 12188887]
[3]
Takebayashi, Y.; Yamada, K.; Miyadera, K.; Sumizawa, T.; Furukawa, T.; Kinoshita, F.; Aoki, D.; Okumura, H.; Yamada, Y.; Akiyama, S.; Aikou, T. The activity and expression of thymidine phosphorylase in human solid tumours. Eur. J. Cancer, 1996, 32A(7), 1227-1232.
[http://dx.doi.org/10.1016/0959-8049(96)00061-5] [PMID: 8758258]
[4]
Balzarini, J.; Gamboa, A.E.; Esnouf, R.; Liekens, S.; Neyts, J.; De Clercq, E.; Camarasa, M.J.; Pérez-Pérez, M.J. 7-Deazaxanthine, a novel prototype inhibitor of thymidine phosphorylase. FEBS Lett., 1998, 438(1-2), 91-95.
[http://dx.doi.org/10.1016/S0014-5793(98)01271-X] [PMID: 9821965]
[5]
Gbaj, A.; Edwards, P.N.; Reigan, P.; Freeman, S.; Jaffar, M.; Douglas, K.T. Thymidine phosphorylase from Escherichia coli: tight-binding inhibitors as enzyme active-site titrants. J. Enzyme Inhib. Med. Chem., 2006, 21(1), 69-73.
[http://dx.doi.org/10.1080/14756360500424010] [PMID: 16570508]
[6]
Langen, P.; Etzold, G.; Bärwolff, D.; Preussel, B. Inhibition of thymidine phosphorylase by 6-aminothymine and derivatives of 6-aminouracil. Biochem. Pharmacol., 1967, 16(9), 1833-1837.
[http://dx.doi.org/10.1016/0006-2952(67)90260-2] [PMID: 6053224]
[7]
Casanova, E.; Herna, A. 5 ′ - O -Tritylinosine and Analogues as Allosteric Inhibitors of Human Thymidine Phosphorylase, 2006, 5562-5570.
[8]
Patel, H.M.; Noolvi, M.N.; Sharma, P.; Jaiswal, V.; Bansal, S.; Lohan, S.; Kumar, S.S.; Abbot, V.; Dhiman, S.; Bhardwaj, V. Quantitative structure-activity relationship (QSAR) studies as strategic approach in drug discovery. Med. Chem. Res., 2014, 23(12), 4991-5007.
[http://dx.doi.org/10.1007/s00044-014-1072-3]
[9]
Dudek, A.Z.; Arodz, T.; Gálvez, J. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Throughput Screen., 2006, 9(3), 213-228.
[http://dx.doi.org/10.2174/138620706776055539] [PMID: 16533155]
[10]
Khan, P.M.; Roy, K. Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR). Expert Opin. Drug Discov., 2018, 13(12), 1075-1089.
[http://dx.doi.org/10.1080/17460441.2018.1542428] [PMID: 30372648]
[11]
Richon, A.; Young, S. An introduction to QSAR methodology. Netw. Sci., 1997, 1-20.
[12]
Ullah, H.; Khan, F.; Taha, M.; Ahmad, F.; Hussain, S.; Ullah, H. Phosphorylase, lipoxygenase inhibitory potentials of thiobarbituric acid analogues and their molecular docking studies., 2020, 53-60.
[13]
A.F. Methods Optimizing Molecules using VLifeMDS, , 1-9.
[14]
Oprea, T.I.; Waller, C.L.; Marshall, G.R. Three-dimensional quantitative structure-activity relationship of human immunodeficiency virus (I) protease inhibitors. 2. Predictive power using limited exploration of alternate binding modes. J. Med. Chem., 1994, 37(14), 2206-2215.
[http://dx.doi.org/10.1021/jm00040a013] [PMID: 8035428]
[15]
Golbraikh, A.; Tropsha, A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput. Aided Mol. Des., 2002, 16(5-6), 357-369.
[http://dx.doi.org/10.1023/A:1020869118689] [PMID: 12489684]
[16]
Chitre, T.S.; Kathiravan, M.K.; Bothara, K.G.; Bhandari, S.V.; Jalnapurkar, R.R. Pharmacophore optimization and design of competitive inhibitors of thymidine monophosphate kinase through molecular modeling studies. Chem. Biol. Drug Des., 2011, 78(5), 826-834.
[http://dx.doi.org/10.1111/j.1747-0285.2011.01200.x] [PMID: 21801308]
[17]
Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22(1), 69-77.
[http://dx.doi.org/10.1002/qsar.200390007]
[18]
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative Molecular Field Analysis (CoMFA). 1. Effect of Shape on Binding of Steroids to Carrier Proteins 1988, 5959-5967.
[19]
Shen, M.; LeTiran, A.; Xiao, Y.; Golbraikh, A.; Kohn, H.; Tropsha, A. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J. Med. Chem., 2002, 45(13), 2811-2823.
[http://dx.doi.org/10.1021/jm010488u] [PMID: 12061883]
[20]
Zheng, W.; Tropsha, A. Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40(1), 185-194.
[http://dx.doi.org/10.1021/ci980033m] [PMID: 10661566]

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy