Tools for evidence-based toxicology: computational-based strategies as a viable modality for decision support in chemical safety evaluation and risk assessment
Free accessResearch articleFirst published online October, 2008
Tools for evidence-based toxicology: computational-based strategies as a viable modality for decision support in chemical safety evaluation and risk assessment
1 Guzelian PS Victoroff MS Halmes NC James RC Guzelian CP . Evidence-based toxicology: a comprehensive framework for causation. Hum Exp Toxicol2005; 24: 161–201.
2.
2 Benz RD . Toxicological and clinical computational analysis and the US FDA/CDER. Expert Opin Drug Metab Toxicol2007; 3: 109–124.
3.
3 Kapetanovic IM . Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact2008; 30: 165–176.
4.
4 Yang C Hasselgren CH Boyer S Arvidson K Aveston S Dierkes P . Understanding genetic toxicity through data mining: the process of building knowledge by integrating multiple genetic toxicity databases. Toxicol Mech Methods2008; 18: 277–295.
5.
5 Contrera JF Matthews E Kruhlak N Benz RD . In silico screening of chemicals for genetic toxicity using MDL-QSAR, nonparametric discriminant analysis, E-State, connectivity, and molecular property descriptors. Toxicol Mech Methods2008; 18: 207–216.
6.
6 Matthews EJ Kruhlak NL Benz RD Contrera JF Marchant CA Yang C . Combined use of MC4PC, MDL-QSAR, BioePisteme, Leadscope PDM, and Derek for Windows software to achieve high-performance, high-confidence, mode of action-based predictions of chemical carcinogenesis in rodents. Toxicol Mech Methods2008; 18: 189–206.
7.
7 Matthews EJ Contrera JF . A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul Toxicol Pharmacol1998; 28: 242–264.
8.
8 Contrera JF Matthews EJ Kruhlak NL Benz RD . Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose. Regul Toxicol Pharmacol2004; 40: 185–206.
9.
9 Doweyko AM . QSAR: dead or alive. J Comput Aided Mol Des2008; 22: 81–89.
10.
10 Benigni R Bossa C . Predictivity of QSAR. J Chem Inf Model2008; 48: 971–980.
11.
11 Cartwright N . Nature's capacities and their measurement.Oxford: Clarendon Press; 1989
12.
12 Valerio LG Arvidson KB Chanderbhan RF Contrera JF . Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. Toxicol Appl Pharmacol2007; 222: 1–16.
13.
13 Matthews EJ Contrera JF . In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects. Expert Opin Drug Metab Toxicol2007; 3: 125–134.
14.
14 Saiakhov RD Klopman G . MultiCASE Expert Systems and the REACH Initiative. Toxicol Mech Methods2008; 18: 159–175.
15.
15 Kumar N Hendriks BS James KA de Graaf D Lauffenburger DA . Applying computational modeling to drug discovery and development. Drug Discov Today2006; 11: 806–811.
16.
16 Jain AN . Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel2004; 7: 396–403.
17.
17 Bailey AB Chanderbhan R Collazo-Braier N Cheeseman MA Twaroski ML . The use of structure-activity relationship analysis in the food contact notification program. Regul Toxicol Pharmacol2005; 42: 225–235.
18.
18 Valerio LG Dixit R . Computational toxicology. Toxicol Mech Methods2008; 18: 97–99.
