Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach. Issue 3 (2nd November 2020)

• Record Type: Journal Article
• Authors: Cappelli, Claudia Ileana; Manganelli, Serena; Toma, Cosimo; Benfenati, Emilio; Mombelli, Enrico
• Is Part Of: Molecular informatics. Volume 40:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-02
• Subjects: adipose tissue:blood partition coefficient -- biological activity -- integrated modelling -- lipophilicity -- QSAR; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202000072
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: The adipose tissue:blood partition coefficient is a key‐endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non‐linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model. Abstract :