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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-adays, 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 T.E.S.T. 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.
Background: Computational (in silico) methods, such as quantitative structure-activity relationships (QSARs) are already well recognized and used in many screening programs related to environmental, industrial and medical chemistry. The main idea of the QSAR is that there is a relationship between molecular structure and ultimate biological effect caused by a chemical compound. In this respect the approach could be used successfully for prediction of various biological endpoints caused by chemical compounds including receptor binding affinity. Aim of the study: In the current study the capabilities for structure-activity modelling incorporated in noncommercial software tool have been employed for investigating the binding effect of xenobiotics toward estrogen and human pregnane X receptor. Material and methods: The analysis was performed by making use of the non-commercial software platform QSAR Toolbox. This system allows application of a set of built-in models for different biological effects, and also allows incorporation of new models for other endpoints. Results: Two models have been applied for predicting the binding effect toward estrogen and human pregnane X receptors of a large number of chemicals collected in a single database of high practical concern. The results show that there are many chemicals which are able to bind the investigated receptors. Since those chemicals are encountered in the environment, they could be considered as potential threat for society. Conclusions: The obtained results could be used as initial step for further experimental testing of those chemicals in order to confirm their potential to harm biological systems in the body.
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