Two major alternatives to in vivo testing are in vitro and in silico techniques. In the last decade, numerous computer software have been developed in order to replace, reduce and refine the animal tests. These software packages include also mutagenicity and carcinogenicity predicting models of the chemical compounds.
1.17.1 VEGA Platform
The VEGA platform contains a number of (Q)SAR models for predicting mutagenicity and carcinogenicity such as CAESAR. Two new carcinogenicity models have been added to the VEGA platform by the author 78. CAESAR ((Q)SAR mutagenicity models) was specifically
developed for the REACH regulation in collaboration with the United States Environmental Protection Agency (http://www.caesar-project.eu/). The mutagenicity models in the VEGA platform are based on data obtained from the Ames bacterial test. Models on carcinogenicity, developmental toxicity and etc. are freely available from the VEGA platform.
1.17.2 DEREK Nexus
The DEREK Nexus 79 is a knowledge-based expert system, developed by LHASA Limited that
predicts the genotoxicity, mutagenicity and carcinogenicity of a chemical by highlighting the SAs present in its molecular structure. Derek Nexus toxicity predictions are a result of two processes: evaluating SAs and estimating the likelihood of toxicity.
The knowledge-based SAs for in vitro mutagenicity have been implemented by experts who have assessed relevant Ames data and supporting mechanistic data (e.g. DNA adduct formation experiments). If a query compound matches a SA, the alert will fire with an associated
reasoning level (e.g. plausible, probable or certain). The reasoning levels associated with the in vitro bacterial mutagenicity alerts in Derek Nexus gives an indication of the likelihood for compounds in that class to be active in the Ames test 80.
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Each bacterial, in vitro mutagenicity alert in the knowledge base was examined by a scientist with expertise in mutagenicity alert development. The patterns encoding the SAR for each alert were modified using the Derek Knowledge Editor if they contained features that were
implemented to prevent the pattern being activated by nonmutagenic compounds (so-called exclusion patterns). Such features were removed and the resultant ‘predictive space’ was stored within a modified knowledge base. Thus, each bacterial, in vitro mutagenicity alert in Derek had a corresponding region of predictive space 81.
1.17.3 TOPKAT
TOPKAT 82 is a (Q)SAR-based system, developed by Accelrys Inc. (http://accelrys.co m/).
Some of the TOPKAT toxicological endpoints are mutagenicity, developmental toxicity, rodent carcinogenicity, rat chronic LOAEL, rat MTD and rat oral LD50. TOPKAT models are
developed using two-dimensional molecular, electronic and spatial descriptors. The toxicity prediction is obtained from a chemical’s molecular structure. TOPKAT defines an applicability domain value which estimates the confidence in the prediction by applying the patented Optimal Predictive Space validation method. Any prediction generated for a query structure outside of the OPS space is considered unreliable.
1.17.4 MultiCASE
MultiCASE 83 (MultiCASE Inc., Cleveland, OH, USA) is a prediction model for genotoxicity
and carcinogenicity endpoints based on US FDA and US EPA. MultiCASE identifies SAs with a potential to initiate high biological activity, in addition, some statistical parameters are analysed to complete the predictions. The mutagenicity and genotoxicity models are based on the data obtained from Ames test, direct mutagenicity, base-pair mutagenicity, frameshift mutagenicity, chromosomal aberrations, and sister chromatid exchange data. The
carcinogenicity model includes different rodent assays (rate, mouse, male, female, and TD50 rats) and human epigenetic studies. All models use the statistical approach with the exception of the rule-based model for the Ames mutagenicity.
1.17.5 QSAR Toolbox
QSAR Toolbox 84 in cooperation with the ECHA is a read-across tool for grouping the
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(http://www.qsartoolbox.org/). QSAR Toolbox systematically groups chemicals into classes according to their molecular structure, physico-chemical and biological properties. This software extracts structural characteristics and modes of action based on experimental information for the target molecule. The common mechanisms of action and common toxicological behaviour or consistent trends among results related to regulatory endpoints are results for an evaluation in this prediction software.
1.17.6 Toxtree
Toxtree 52 is a free tool for the assessment of mutagenicity and carcinogenicity of the chemicals
using decision trees. Toxtree mutagenicity and carcinogenicity model is based on the SAs of the Benigni-Bossa rule set, SAs for identification of Michael acceptors, and SAs confirmed by positive in vivo micronucleus tests. The program identifies any SA present in the target
molecule structure and concludes about the mutagenic or carcinogenic property of the chemical compound under investigation. The result of the prediction can be class I (inactive), class II (weak activity), or class III (active).
1.17.7 LAZAR
LAZAR 85 is an open source tool for the prediction of carcinogenicity and Salmonella
mutagenicity. LAZAR creates local endpoint (Q)SAR models based on a training set (only nearest neighbours) for each chemical separately. It first calculated the descriptors and determines the molecular similarity and then it builds a local (Q)SAR model based on a database of experimental toxicity data. This program meets all five OECD principles. 1.17.8 ACD/Tox Suite
The ACD/Tox Suite 86 package contains predicting models for genotoxicity and carcinogenicity.
The assessments are made based on validated (Q)SAR models in combination with expert knowledge. The software highlights and identifies the SAs which are responsible for toxic properties and extracts some similar molecules from the training set. The training set is composed of compounds that are genotoxic in Ames test.
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The Model Applier developed by Leadscope 87 (Leadscope Inc., Colombia, OH, USA), uses
(Q)SAR models for Salmonella mutagenicity, E. coli mutagenicity, mouse lymphoma, in vitro chromosome aberrations, and in vivo micronuclei.
1.17.10 SARpy
SARpy 88 is a data mining tool which works in a SAR approach. This tool is able to identify a
list of active and inactive molecular fragments that act as SAs for biological activity from a learning set of chemical compounds with known binary classification toxicity property (e.g. carcinogenicity, mutagenicity, etc.). The entire process is designed to fit with human reasoning. In fact, SARpy is a computerized tool which helps the expert to extract significant molecular substructures with potential effect in toxicity or in nontoxicity in an automatic way with customized requirements to be set by the user. The extracted list of active and/or inactive SAs identified by SARpy are considered new prediction models with satisfactory prediction ability on an external test set, in particular in the case of mutagenicity 89.