SAXOCON A/S

New Approach Methodologies (NAMs)

Join us in the future of toxicological evaluation today.

The EMA and FDA are developing and adopting new technologies in predictive toxicology. We have the expertise and tools to support your development processes with cutting-edge modelling and the best strategy for using NAMs.

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Computational Toxicology

Our in-silico capabilities are second to none. For over 8 years, SAXOCON has been at the forefront of the in-silico toxicological evaluation field.

SAXOCON has leading experts in quantitative structure-activity relationship (QSAR) methods. These methods allow us to determine the toxicity of materials or chemicals compounds without the need for animal testing, which helps you keep abreast of regulations.

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Unexpected Findings

If you have ever experienced a production line shutdown or product recall due to an unexpected finding, you know how critical it is to quickly identify what is wrong and how to deal with it.

SAXOCON’s Service Level Agreement (SLA) is a turnkey solution that provides a quick risk assessment of your inputs, products, and processes. We not only identify your unexpected finding, but we also deliver a toxicological risk assessment and mitigation strategy. Our SLA gives you the comfort of an agreed-upon turn-around time for dealing with unexpected findings.

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Modelling and Simulation

Animal testing is subject to ever stricter regulations, with regulatory agencies around the world encouraging the development and use of alternative testing methods.

SAXOCON is an industry leader in computational (in silico) modelling and simulation. These powerful tools complement traditional methods such as in-vitro and in-vivo testing and allow us to support your company’s testing needs while addressing current and future regulations.

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(Q)SAR and RAX Solutions

QSAR alone is not always enough to determine the toxicity of a chemical structure. Employing read-across (RAX) methods enhances the prediction of chemical properties by grouping chemicals according to their structural and biological similarity and inferring the relevant properties of data-poor chemicals by comparing them to “similar” data-rich chemicals.

With our expertise and a unique workflow that intelligently combines data-driven and knowledge-based approaches, we ensure the highest quality in-silico data.

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Data Gap Filling

Not completely understanding your inputs can lead to a long, expensive study to gather missing data.

SAXOCON’s QSAR and read-across (RAX) methods (various endpoints) allows us quickly data-gap fill for you.

QSAR and RAX methods are powerful tools that generate reliable in-silico data and complement traditional methods, giving faster, more reliable results that resolve lacking data points in your safety assessment.