In silico prediction of drug toxicity

@article{Dearden2003InSP,
  title={In silico prediction of drug toxicity},
  author={John C. Dearden},
  journal={Journal of Computer-Aided Molecular Design},
  year={2003},
  volume={17},
  pages={119-127}
}
  • J. Dearden
  • Published 1 February 2003
  • Chemistry
  • Journal of Computer-Aided Molecular Design
It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity… 
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References

SHOWING 1-10 OF 36 REFERENCES
Computer systems for the prediction of toxicity: an update.
  • N. Greene
  • Biology
    Advanced drug delivery reviews
  • 2002
Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR.
TLDR
The number of correctly identified mutagens and the predictions compared to their Salmonella typhimurium mutagenicity data has increased and work on improving the predictive capabilities of DEREK, StAR and METEOR is in progress.
Development, characterization and application of predictive-toxicology models.
TLDR
Methods have been developed to combine SAR submodels and thereby improve predictive performance in CASE/MULTICASE, and the development of Good Laboratory Practices is a further priority.
A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.
  • K. Enslein, V. Gombar, D. Bagheri
  • Biology
    Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
  • 1997
A Possible Index of Fatal Drug Toxicity in Humans
TLDR
This index (T) has been obtained for 47 drugs commonly encountered in fatalities in England and Wales and it is shown that T is often closely related to the corresponding fatal toxicity in animals, and to physico-chemical factors which are known to be correlated with other measures of human drug toxicity.
A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software.
TLDR
A new quantitative structure-activity relational expert system (QSAR-ES) method for predicting the carcinogenic potential of pharmaceuticals and other organic chemicals in rodents, and a beta-test evaluation of its performance is described.
Structural determinants of developmental toxicity in hamsters.
TLDR
Results suggested that developmental toxicity in hamsters was not the result of a unique mechanism or attack on a specific molecular target, and indicated that the availability of experimental data on additional chemicals would improve the performance of the SAR model.
Structure-activity studies of chemical carcinogens: use of an electrophilic reactivity parameter in a new QSAR model.
TLDR
The addition of the estimated electrophilicity parameter increased the overall performance of the system and remarkably contributed to the identification of carcinogens (their correct classification increasing from the previous 86% to 97%).
...
...