In Silico

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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 for your active substance

A comprehensive suite of computational modeling tools employing expert rule-based and statistical-based approaches to maximum effect

In silico predictions are integrated into the wider testing approach for your active substance –optimizing your study spend while still building compelling arguments to regulators

In silico assessment predicts the potential toxicity of an agrochemical active substance using advanced computation modeling. The reliability and acceptance of New Approach Methods (NAMs) or non-animal approaches, such as in silico is increasing as scientific knowledge and information on active substances grows. Importantly, in silico modeling can provide toxicity predictions rapidly, without animal testing. By working with Labcorp, you can access expertise in in silico predictions crucial to answering safety questions about your agrochemical and informing the design of in vitro studies.

Without data and with constrained budgets, how do you predict the toxicity of your agrochemical?

Budgets are always constrained and in vitro and in vivo testing can be expensive and takes time. While in silico predictions combined with read-across can build weight-of-evidence arguments for your agrochemical, high levels of experience, skill and insight are needed to use QSARs to build predictions that can withstand regulatory scrutiny.

Compelling in silico predictions for your agrochemical built by combining expertise in computational biology, statistics, chemistry and toxicology

You need a combination of scientific acumen and practical expertise to develop compelling in silico predictions. That needs a team of scientists who understand the chemistry, toxicology and the statistical basis behind QSAR modeling. That is why, as our partner, you will have access to a team of experts with vast experience in conducting QSARs and read-across to build weight-of-evidence arguments appropriate for agrochemical regulators.

Optimizing prediction quality and confidence with a range of QSAR modeling tools

A comprehensive suite of QSAR modeling tools are used to build a reliable prediction for any endpoint requested for your agrochemical. Both expert rule-based systems and statistical-based models will be used to enhance the confidence of the endpoint predictions.

QSAR models used include:

  • Biovia Discovery Studio (TOPKAT) extensible
  • OECD QSAR Toolbox
  • ACD/Percepta
  • DEREK Nexus
  • US EPA T.E.S.T.
  • US EPA EPI Suite
  • ToxRead
  • ToxTree

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