Quantitative structure-activity relationship (QSAR) models are models linking a property or effect, such as boiling point or toxicity, to parameters associated with chemical structure, such as certain molecular descriptors. They can be used to assess chemical substances within the so-called in silico approach (as an analogy to the in vitro and in vivo approach). 

QSAR modeling has been traditionally used as a lead optimization approach in drug discovery research. However, in recent years QSAR modeling found broader applications in hit and lead discovery by the means of virtual screening as well as in the area of drug-like property prediction, and chemical risk assessment.  

STRENGTHS

PRO-ACTIVE DECISION-MAKING: The predictive ability of in silico methods enables a pro-activeapproach to toxicity within product development Toxicity evaluation can be brought ‘upstream’ in the product development and decision making processes, so that chemicals are selected, and products are developed, to be less toxic or nontoxic.

PRIORITIZATION FOR REDUCTION OF COSTS: In silico methods offer a valuable tool for prioritising substances according to their toxicity, so that costly experimental tests can be focussed on those substances with higherprobability of toxicity and higher risk.

INNOVATION : Regulatory authorities explicitly encourages innovation in toxicity evaluation, they demands the use of existing data where possible, and states that further animal testing can only be used ‘as a last resort’,In this way future toxicity, and therefore future costs and future impacts on health and the environment, can be reduced or avoided.

Looking ahead, in silico models will be put in one central role all toxicological procedures for the evaluation of chemical substances will be reshaped on them. Experience and understanding of in silico models will be essential for analysis and formake full use of these data in future toxicity assessments.

There are various Qsar models and many purposes, to ensure correctness and reproducibility, of central importance for the purposes of regulation, a high level of experience is required in order to allow careful identification of suitable parameters, chosen by the expert on a case-by-case basis.

We use prediction models implemented in commercial and free tools that allow us to meet all regulatory requirements:

• results are derived from a (Q)SAR model whose scientific validity has been established,

 • the substance falls within the applicability domain of the (Q)SAR model,

• results are adequate for the purpose of classification and labelling and/or risk assessment,

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