Abstract:
:Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them.
journal_name
Hum Exp Toxicoljournal_title
Human & experimental toxicologyauthors
Naven RT,Louise-May Sdoi
10.1177/0960327115605440subject
Has Abstractpub_date
2015-12-01 00:00:00pages
1304-9issue
12eissn
0960-3271issn
1477-0903pii
34/12/1304journal_volume
34pub_type
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journal_title:Human & experimental toxicology
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