Computational Toxicological Approaches for Drug Profiling and Development of Online Clinical Repositories

Preview this chapter:

Computational Toxicological Approaches for Drug Profiling and Development of Online Clinical Repositories, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815196986/chap3-1.gif

One of the chief reasons for drug attrition and failure to become a marketed drug is the potential toxicity associated with its administration. Therefore, many drugs encountered in the past reached the last phase of drug development successfully but could not be marketed despite their potential drug-likeness due to their inevitable toxicity properties. This issue can be addressed considerably by employing computational toxicological approaches for predicting the toxicity parameters of a drug candidate before its practical synthesis. Pharmaceutical companies utilise computer-based toxicity predictions at the design stage for identifying lead compounds possessing the least toxic properties, and also at the optimization stage for selecting candidates as potential drugs. This integrative field has been exploited for various applications including hazard and risk prioritization of chemicals and safety screening of drug metabolites. The importance of QSTR models for the computational prediction of toxicity is also discussed in this chapter. Various important and predominant software for in silico toxicity prediction including ADMETox, OSIRIS Property Explorer, TopKat and admetSAR 2.0 are also covered herein. This chapter also discusses various freely accessible online clinical repositories such as BindingDB, PubChem, ChEMBL, DrugBank and ChemNavigator iResearch Library. Therefore, the present chapter focuses on the role played by computational toxicology in the procedure of drug profiling and in establishing freely accessible online clinical repositories.<br>