ChemTunes/ToxGPS

ChemTunes/ToxGPS (Database and Knowledgebase for Safety Evaluation and Risk Assessment, Altamira LLC and Molecular Networks GmbH) is a knowledge base of in vitro and in vivo toxicity information and comprises multiple components/workflows to support the safety and risk assessment of chemical compounds, including an expert-QC'ed database and the MoA-based ToxGPS prediction system for a series of human health and regulatory-relevant toxicity endpoints, as well as the Liver BioPath, a tool for human metabolism prediction.

ToxGPS prediction system comprises a set of albums for a series of human health toxicity endpoints, including genetic toxicity, carcinogenicity, developmental and reproductive toxicity, skin sensitization. Each knowledgebase album consists of the following components:

  1. alerting chemotypes (structural alerts)
  2. mechanistically-informed (mode-of-action driven) QSAR models, i.e. an approach used at US FDA CERES (Chemical Evaluation and Risk Estimation System)
  3. nearest neighbors (“structural analogues”) analysis and optional access to training sets (with expert-aggregated study calls)
  4. optional access to ToxGPS toxicity database (QC’ed by experts) for provided endpoints.

All ToxGPS QSAR models consist of chemical mode-of-action (MoA) category models as well as a general global model. The computational modelling approach is a hybrid of partial least squares (PLS)/ordinal logistic regression methods. For model building, global molecular and shape descriptors (from CORINA Symphony) and quantum-mechanic parameters are used. The models return probabilistic predictions (positive and negative probabilities plus a quantitative estimate of the associated uncertainty) and an overall prediction (positive/negative/equivocal).

Unlike QSAR models, chemotype alerts generate only positive predictions. The reliability of each alert is determined by exploring the ability of the alert to hit positive compounds in a large training set. Different training sets were used for the QSAR models and the alerts, so that predictions from these are indeed independent. A mathematically rigorous and quantitative weight-of-evidence (WoE) decision theory approach (i.e., Dempster-Shafer theory (DST)) is used to obtain the final overall assessment (by combining the predictions from QSAR and alerts), and to provide a quantitative estimation of the uncertainty associated with the prediction.

Applicability domain analysis is performed based on the QSAR global models and reports whether the target compound is out-of-domain.