Subscribe

Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILNS > Volume 52 > QSAR Modeling for Acute Toxicity Prediction in Rat...
< Back to Volume

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

Full Text PDF

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.

Info:

Periodical:
International Letters of Natural Sciences (Volume 52)
Pages:
9-18
DOI:
10.18052/www.scipress.com/ILNS.52.9
Citation:
J. S. Roy et al., "QSAR Modeling for Acute Toxicity Prediction in Rat by Common Painkiller Drugs", International Letters of Natural Sciences, Vol. 52, pp. 9-18, 2016
Online since:
Mar 2016
Export:
Distribution:
References:

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

[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.

[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).

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

[5] Talete, Dragon Version 5. 4, (2006) (http: /www. talete. mi. it/dragon_net. htm).

[6] User Manual ADMET, Version 5. 5., Simulation Plus Inc, S.P.: Lancaster, CA, USA, (2011).

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

[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

[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

[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

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

[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.

[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).

[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

[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.

[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.

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

[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.

[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

[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.

[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

[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

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

[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.

Show More Hide