QSAR Modeling for Acute Toxicity Prediction in Rat by Common Painkiller Drugs

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Abstract:

Painkiller drugs or analgesics are potent pain reliever chemical agents, which are commonly used in pain therapy. Mathematical modeling by QSAR (quantitative structure activity relationship) methods are well known practices to determine predictive toxicity in biota. Now-a-days, an easy screening of chemicals, QSAR can be done by using several recommended softwares. The present study was carried out by using software namely T.E.S.T. (Toxicity estimation software tool) for rat oral LD50 (median lethal dose) predictive toxicity for common painkiller drugs. These painkiller drugs were selected as 35 compounds and tabulated on the basis characteristics of one non-narcotic viz. acetaminophen, twenty non-steroidal anti-inflammatory such as bromofenac, diclofenac, diflunsial, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, maclofenamate sodium, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, phenylbutazone, piroxicam, sulindac and tolmetin as well as fourteen narcotic viz. buprenorphine, butorphanol, codeine, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, nalbuphine, oxycodone, pentazocine, dextropropoxyphene and tapentadol. The data were tabulated on experimental (bioassay) from ChemIDPlus and predictive toxicity of 30 compounds out of 35 compounds by using T.E.S.T. The predictive data were found by T.E.S.T. that 20 and 10 compounds were very toxic and moderately toxic respectively but not extremely, super toxic and non-toxic in rat model after acute oral exposure. It is suggested to evaluate the predicted data further with other available recommended softwares with different test models like daphnia, fish etc. to know aquatic toxicity when these compounds may discharge into waterbodies.

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[1] W.O. Foye, Principles of Medicinal Chemistry, 3rd edition, Bombay: Varghese Publishing House, (1989) p.240.

Google Scholar

[2] S. Arora, and Saurabhvija, QSAR study on some newly synthesized pyrimido-benzimidazole derivatives as analgesic agents, Int. J. Pharm. Pharm. Sci. 3(5suppl), (2011) 457-461.

Google Scholar

[3] USEPA (United States Environmental Protection Agency) T. E. S. T Tool, User's Guide for T.E.S.T, Version 4.1, A Program to Estimate Toxicity from Molecular Structure, Cincinnati, OH, USA, (2012).

Google Scholar

[4] Accelrys Inc., TOPKAT software, Accelrys Inc., San Diego, CA, (2003).

Google Scholar

[5] Talete, Dragon Version 5.4, (2006) (http://www.talete.mi.it/dragon_net.htm)

Google Scholar

[6] User Manual ADMET, Version 5.5., Simulation Plus Inc, S.P.: Lancaster, CA, USA, (2011).

Google Scholar

[7] F. Choplin, Comprehensive medicinal chemistry, Corwin Hansch, Vol 4, Elsevier Pergamon, Oxford, (2005) 33-57.

Google Scholar

[8] C.W. Yap, Y. Xue, Z.R. Li, Y.Z. Chen, Application of support vector machines to in silico prediction of cytochrome P450 enzyme substrates and inhibitors, Curr. Topics Med. Chem. 6 (15) (2006) 1593-1607.

DOI: 10.2174/156802606778108942

Google Scholar

[9] R.V. Guido, G. Oliva, A.D. Andricopulo, Virtual screening and its integration with modern drug design technologies, Curr. Med. Chem. 15 (1) (2008) 37-46.

DOI: 10.2174/092986708783330683

Google Scholar

[10] A. Schwaighofer, T. Schroeter, S. Mika, G. Blanchard, How wrong can we get? A review of machine learning approaches and error bars, Comb. Chem. High Throughput Screen. 12 (5) (2009) 453-468.

DOI: 10.2174/138620709788489064

Google Scholar

[11] L.G. Valerio Jr., In silico toxicology for the pharmaceutical sciences, Toxicol. Appl. Pharmacol. 241 (2009) 356-370.

Google Scholar

[12] S.N. Talapatra, D. Misra, K. Banerjee, P. Banerjee, S. Swarnakar, QSAR modeling for acute toxicity prediction of fluroquinolone antibiotics by using software, Int. J. Adv. Res. 3 (6) (2015) 225-240.

Google Scholar

[13] V. Kovalishyn, I. Kopernyk, S. Chumachenko, O. Shablykin, F. Kondratyuk, S. Pil'o, V. Prokopenko, V. Brovarets, L. Metelytsia, QSAR studies, design, synthesis and antimicrobial evaluation of azole derivatives, Comput. Biol. Bioinfor. 2(2) (2014) 25-32.

DOI: 10.2174/1570163813666161108125227

Google Scholar

[14] P. Ruiz, G. Begluitti, T. Tincher, J. Wheeler, M. Mumtaz, Prediction of acute mammalian toxicity using QSAR methods: A case study of sulfur mustard and its breakdown products, Molecules 17 (2012) 8982-9001.

DOI: 10.3390/molecules17088982

Google Scholar

[15] P. Banerjee, S.N. Talapatra, Assessment of medicinal tree diversity in the Chintamoni Kar Bird Sanctuary (CKBS), Kolkata, India and prediction of antimutagenic phytochemicals by using software, Int. J. Adv. Res. 3 (7) (2015) 225-243.

Google Scholar

[16] S.N. Talapatra, A. Sarkar, Acute toxicity prediction of synthetic and natural preservatives in rat by using QSAR modeling software, Int. J. Adv. Res. 3 (7) (2015) 1424-1438.

Google Scholar

[17] ChemIDplus, A Toxnet Database, U.S. National Library of Medicine, Available from: www.chem.sis.nlm.nih.gov/chemidplus.

Google Scholar

[18] T.M. Martin, P. Harten, R. Venkatapathy, S. Das, D.M. Young, A hierarchical clustering methodology for the estimation of toxicity, Toxicol. Mech. Methods 18 (2008) 251-266.

DOI: 10.1080/15376510701857353

Google Scholar

[19] M. Cleuvers, Mixture toxicity of the anti-inflammatory drugs diclofenac, ibuprofen, naproxen, and acetylsalicylic acid. Ecotoxicol. Environ. Saf. 59 (2004) 309-315.

DOI: 10.1016/s0147-6513(03)00141-6

Google Scholar

[20] V.K. Gombar, D.V.S. Jain, Quantification of molecular shape and its correlation with physico-chemical properties, Indian J. Chem. 24A (1987) 554-555.

Google Scholar

[21] V.K. Gombar, K. Enslein, Quantitative structure-activity relationship (QSAR) studies using electronic descriptors calculated from topological and molecular orbital (MO) methods, QSAR 9 (1990) 321-325.

DOI: 10.1002/qsar.19900090405

Google Scholar

[22] L.H. Hall, B. Mohney, L.B. Kier, The electrotopological state: Structure information at the atomic level for molecular graphs, J. Chem. Inf. Comput. Sci. 31 (1991) 76-82.

DOI: 10.1021/ci00001a012

Google Scholar

[23] S.J. Xu, Computer-assisted drug molecular design, Chemical Industry Press, Beijing, China (2004).

Google Scholar

[24] Canadian Center for Occupational Health & Safety, What is an LD50 and LC50, (2012), Available from: http://www.ccohs.ca/oshanswers/chemicals/LD50.html#_1_6.

Google Scholar