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A dedicated team of computational biologists, statisticians, chemists, toxicologists and regulatory experts ensure robust in silico toxicology predictions to support weight of evidence arguments supporting your chemical
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A wide suite of computational modeling tools using expert rule-based and statistical-based approaches employed to optimum effect
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In silico predictions are integrated into the wider testing approach for your chemical to optimize your study spend and help you make compelling arguments to regulators
In silico assessment uses advanced computation modelling to predict the potential toxicity of a chemical rapidly and without the need for animal testing. New Approach Methods (NAMs) or non-animal approaches, such as in silico, are improving as computation power, scientific knowledge and information on chemicals grow. By working with us, you can gain expertise for in silico predictions crucial to answering safety questions about your chemical and informing the design of any in vitro study program.
How can you predict the toxicity of your chemical without testing if there is little or no experimental data available?
Avoiding both in vitro and in vivo testing may be necessary if budgets and regulatory timelines are tight. In silico predictions, based on computer modelling, combined with read-across approaches can build a weight of evidence argument for regulators. However, running the models and interpreting the predictions as part of a WOE approach requires high levels of experience, skill and regulatory insight.
How do you optimize and target your spend on toxicity testing?
To optimize your budget for toxicology testing, you must take a strategic approach to study planning. In silico approaches can help you establish the optimum focus for your study program and the tests that will best deliver reliable outcomes in a cost and time efficient manner.
Combined expertise in computational biology, statistics, chemistry and toxicology to create compelling in silico toxicology predictions for your chemical
Meaningful in silico predictions can only be made by people who understand the chemistry, toxicology and the statistical basis behind QSAR models. By working with us, you will have access to a team of experts with many years’ experience in conducting QSARs and read-across and with high levels of regulatory insight. Our diverse team of computational biologists, statisticians, chemists and toxicologists is flexed to match your needs.
The best approach to optimize prediction quality and confidence
A suite of QSAR modeling tools are employed to ensure a reliable prediction for any endpoint requested for your chemical. This may involve the use of both expert rule-based systems and statistical-based models to improve the confidence of endpoint predictions.
QSAR models used include:
- OECD QSAR Toolbox
- CASE Ultra
- DEREK Nexus
- VEGA NIC
- US EPA T.E.S.T.
- US EPA EPI Suite
- ToxRead
- ToxTree