Quantitative Structure–Activity Relationship Model for the Fetal–Maternal Blood Concentration Ratio of Chemicals in Humans

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 (R a 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.Key words placental transfer; human; quantitative structure-activity relationship; fetal-maternal blood concentration ratioThe placental transfer of chemicals is crucial for toxicity assessment of fetus, because chemicals that do not penetrate the placenta are considered safe for fetus.1) Therefore, investigations of many drugs that are transferred from mother to fetus through the placenta have been performed.Since it is difficult to measure the placental transfer of chemicals in humans ethically, in vitro and ex vivo methods have been developed and used for the evaluation of human placental transfer.2) In in vitro methods, cell lines from human placenta, such as BeWo, JAr, and JEG cells, have been commonly used as models of the placental barrier.3) In ex vivo methods, the human placenta is perfused to investigate the transfer of chemicals from maternal to fetal compartment. Although this system has been validated in many studies, and therefore this system is considered reliable, there are difficulties in conducting this evaluation method. 2,4)Quantitative structure-activity relationship (QSAR) models are useful tools for predicting the biological activity of chemicals. Descriptors of chemicals such as molecular weight and log P, are needed to develop QSAR prediction, and those are determined by experiments, calculation, or predicted using software. In the field of pharmacokinetics, QSAR models have been developed and reported in areas such as oral bioavailability, Caco-2 permeability, and metabolism. 5-7)QSAR models for predicting the placental transfer of chemicals using ex vivo data have already been reported. 8,9) However, to our knowledge, this is the first report that indicates prediction of the fetal-maternal blood concentration ratio (F/M ratio) of chemicals using in vivo data. To predict t...

Prediction of human fetal–maternal blood concentration ratio of chemicals

“…The logFM values representing in vivo fetal-maternal blood ratio of 55 chemicals were obtained from a previous work collecting in vivo data from 16 published studies (Takaku et al, 2015). The 55 chemicals were randomly divided into a training dataset and a test dataset with 41 and 14 chemicals, respectively.…”

“…For the development of quantitative structure-activity relationship (QSAR) models, 1-dimensional (1D) and 2-dimensional (2D) descriptors including physicochemical properties were calculated for each chemical using the PaDEL-Descriptor v2.21 software (Yap, 2011). PaDEL-Descriptor has been shown to be useful for developing QSAR models for several endpoints (Takaku et al, 2015;Huang et al, 2015;Tseng et al, 2017) and currently it is able to calculate 1,875 descriptors (1,444 1D, 2D descriptors and 431 3D descriptors) and 12 types of fingerprints. In this study, a 1,444-dimensional feature vector for each chemical was generated consisting of 1,444 1D and 2D descriptors for model development.…”

“…The selected features of AATSC1c and ZMIC1 are only moderately correlated with a correlation coefficient of 0.449 showing no multicollinearity problems. To compare with the three features (MW, Hmax and TopoPSA) identified by a previous study (Takaku et al, 2015), the correlation coefficients among the two informative features Table 4 The correlation among features identified in this study and a previous study (Takaku et al, 2015). of this study and previously identified three features were calculated as shown in Table 4.…”

Placental transfer of xenobiotics in pregnancy physiologically-based pharmacokinetic models: Structure and data

“…The latter failed to reproduce the difference of lipid blood content between maternal and fetal blood in vivo. This would promote the transfer of low polarity compounds because of the high lipid content in the membrane, leading to the negative values of the polarity descriptors[100]. They also assume that the positive values found for polarity descriptors in their QSPR might be attributed to the influence of transporters on hydrophilic substances transfer.The QSPRs currently available show the same limitations as their data sources, i.e.…”

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

“…e reduction of variables achieved using the genetic algorithm does not always guarantee that descriptors with clear meaning will be selected. Nevertheless, among descriptors which appeared in the p(23, 3) model, IC0 and MLFER_S are quite simple to interpret in the context of polarity HSP since IC0 index expresses the diversity (heterogeneity) of atomic types [81], while MLFER_S is associated with the dipolarity/ polarizability features of molecules [57,82,83]. Also autocorrelation descriptors GATS1e, GATS2e, and MATS1v deserve for special attention.…”