Predictive toxicology using QSAR : A perspective

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700 032, India E-mail: kunalroy_in@yahoo.com Fax : 91-33-28371078 Manuscript received 27 August 2010, accepted 30 August 2010 The need of in silico techniques In predicting toxicological and hazardous properties of chemicals are taking the central stage of attention day by day among the scientific community and the public. In silico methods are capable of providing information about the physicochemical properties of chemicals, their environmental fate as well as their human health effects. Quantitative structure-activity relationship (QSAR) is one of the mostly used techniques among different in silico approaches. QSAR will complement the "3Rs" (replacement, refinement and reduction of animals in research) with a powerful new tool to minimize animal testing. Advanced QSAR predictive models are being designed and tested by different countries governing regulatory agencies to assess physical, chemical, and biological properties of individual chemical entities using applications specific for decision-making frameworks in safety assessments. The use of QSAR modelling for toxicological predictions would help to determine the potential adverse effects of chemical entities in risk assessment, chemical screening, and priority setting. Again, in pre-screening of new drug entities before preclinical trial, QSAR can be proved as a significant tool for predicting preclinical toxicological endpoints and clinical adverse effects. A major objective of employing these software programs is to facilitate industry scientists not only to improve the discovery process but also to ensure the sensible use of in silica tools to support risk assessments of chemicals and drug-induced toxicities and in safety evaluations. Though few expert systems are available, yet a large number of expert systems should be developed for the ease of toxicity screenings of chemicals and pharmaceuticals In less time employing less money and avoiding the sacrifice of large number of animals. In this review, representative examples or different recently reported QSAR models, expert systems and available databases and the current capabilities and limitations of in silico approaches are critically addressed