In Silico Toxicological Reports

Fast and Effective Risk Assessments with In Silico Toxicology

Computer-aided toxicological assessment methods strengthen the scientific basis of product safety. Within the scope of our in silico analysis services, we evaluate toxicological risks for pharmaceuticals and chemicals quickly and in accordance with regulatory standards with up-to-date approaches such as Read-Across, QSAR modeling (with OECD and US EPA tools) and SwissADME. Where physical testing is limited, these models are offered to comply with regulations and accelerate R&D processes, saving time and resources and backed by reliable expert opinion. With our scientific strength in toxicology, you can bring your products to market with confidence.

Laboratory technician writing report with samples in a medical lab.

Cross-reading is a technique for predicting endpoint information for a substance (target substance) using data from the same endpoint from (one) other substance(s) (source substance(s)). According to the European Chemicals Agency (ECHA), cross-reading for medicines, drug compounds and other molecules is performed in the context of Annex XI, section 1.5 "Grouping of substances and cross-reading approach": VII to X and Annex XI, section 1.2 "Weight of evidence: cross-reading may be used as an array of evidence". Cross-reading is now required by some regulatory authorities in some countries and is expected to be required for all medicines and drug ingredients in the near future. An expert opinion is added at the end of the report to provide an overall assessment of the toxicological risks of a particular substance.

Quantitative structure-activity relationship models (QSAR models) are regression or classification models used in chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the strength of the response variable (Y), while classification QSAR models relate predictor variables to a categorical value of the response variable. In QSAR modeling, predictors consist of physico-chemical properties or theoretical molecular descriptors of chemicals; the QSAR response variable can be a biological activity of chemicals. QSAR models first summarize the putative relationship between chemical structures and biological activity in a chemical data set. Second, QSAR models predict the activities of new chemicals. Second, QSAR models predict the activities of new chemicals. At the end of the report, an expert opinion is added to give an overall assessment of the toxicological risks of a particular substance.

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration and remain there in bioactive form long enough for the expected biological events to take place. Drug development involves the assessment of absorption, distribution, metabolism and excretion (ADME) at an increasingly early stage in the discovery process, when accepted compounds are plentiful but access to physical samples is limited. In this context, computer models provide valid alternatives to experiments. The SwissADME web tool provides access to a repository of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug similarity and medicinal chemistry relevance, including in-house competent methods such as BOILED-Egg, iLOGP and Bioavailability Radar. An expert opinion is added at the end of the report to provide an overall assessment of the toxicological risks of a given substance.

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