COMPUTATIONAL TOXICOLOGY QSAR

The toxicological evaluation of chemical structures is carried out through preclinical in vitro and in vivo studies, which allow their mutagenic, carcinogenic, or reprotoxic potential to be defined. However, the need to replace animal testing has led to the widespread development of computational toxicology in recent decades.

There are different computational toxicology tools, such as in silico methods based on structure-activity relationships and quantitative structure-activity relationships ((Q)SAR, -Quantitative- Structure-Activity Relationships), or Read Across models.

Expert-type (or rule-based) QSAR systems QSAR) systems are developed from bibliographic data that allow the empirical identification of toxicity associated with specific structural groups, while statistical QSAR systems are based on empirical data that allow the assignment of a toxicological probability value based on the presence of functional groups, physicochemical properties, adjacent groups, or the chemical environment in the molecule under study. The combination of both “expert” and “statistical” QSAR studies will allow different toxicological parameters to be accurately predicted, in many cases avoiding the need for experimental in vitro and in vivo testing.

At Dalia Global, we have a team of toxicologists with extensive experience in the use of statistical and expert-type QSAR models in the development of toxicological assessments of impurities or molecules in development.

DALIA GLOBAL SERVICES

1.- Prediction of the toxicological profile of substances using in silico methods.

2.- Expert QSAR (Rule-Based).

3.- Statistical QSAR.