Literature DB >> 26027836

Quantitative structure-activity relationship model for the fetal-maternal blood concentration ratio of chemicals in humans.

Tomoyuki Takaku1, Hirohisa Nagahori, Yoshihisa Sogame, Tatsuya Takagi.   

Abstract

A quantitative structure-activity relationship (QSAR) model of the fetal-maternal blood concentration ratio (F/M ratio) of chemicals was developed to predict the placental transfer in humans. Data on F/M ratio of 55 compounds found in the literature were separated into training (75%, 41 compounds) and testing sets (25%, 14 compounds). The training sets were then subjected to multiple linear regression analysis using the descriptors of molecular weight (MW), topological polar surface area (TopoPSA), and maximum E-state of hydrogen atom (Hmax). Multiple linear regression analysis and a cross-validation showed a relatively high adjusted coefficient of determination (Ra(2)) (0.73) and cross-validated coefficient of determination (Q(2)) (0.71), after removing three outliers. In the external validation, R(2) for external validation (R(2)pred) was calculated to be 0.51. These results suggested that the QSAR model developed in this study can be considered reliable in terms of its robustness and predictive performance. Since it is difficult to examine the F/M ratio in humans experimentally, this QSAR model for prediction of the placental transfer of chemicals in humans could be useful in risk assessment of chemicals in humans.

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Year:  2015        PMID: 26027836     DOI: 10.1248/bpb.b14-00883

Source DB:  PubMed          Journal:  Biol Pharm Bull        ISSN: 0918-6158            Impact factor:   2.233


  3 in total

1.  Prediction of Maternal and Fetal Pharmacokinetics of Dolutegravir and Raltegravir Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Xiaomei I Liu; Jeremiah D Momper; Natella Y Rakhmanina; Dionna J Green; Gilbert J Burckart; Tim R Cressey; Mark Mirochnick; Brookie M Best; John N van den Anker; André Dallmann
Journal:  Clin Pharmacokinet       Date:  2020-11       Impact factor: 6.447

2.  Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS).

Authors:  Dimitri Abrahamsson; Adi Siddharth; Joshua F Robinson; Anatoly Soshilov; Sarah Elmore; Vincent Cogliano; Carla Ng; Elaine Khan; Randolph Ashton; Weihsueh A Chiu; Jennifer Fung; Lauren Zeise; Tracey J Woodruff
Journal:  J Expo Sci Environ Epidemiol       Date:  2022-10-07       Impact factor: 6.371

3.  Prediction of human fetal-maternal blood concentration ratio of chemicals.

Authors:  Chia-Chi Wang; Pinpin Lin; Che-Yu Chou; Shan-Shan Wang; Chun-Wei Tung
Journal:  PeerJ       Date:  2020-07-21       Impact factor: 2.984

  3 in total

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