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Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals Across the Placenta
Abstract
The increasing diversity of environmental chemicals in the environment, some of which may be developmental toxicants, is a public health concern. The aim of this work was to contribute to the development of rapid and effective methods to assess prenatal exposure. Quantitative structure-activity relationships (QSAR) modeling has emerged as a promising method in the development of a predictive model for the placental transfer of contaminants. Cord to maternal plasma or serum concentration ratios for 105 chemicals were extracted from the literature, and 214 molecular descriptors were generated for each of these chemicals. Ten predictive models were built using Molecular Operating Environment (MOE) software, and the Python and R programming languages. Training and test datasets were used, respectively, to build and validate the models. The Applicability Domain Tool v1.0 was used to determine the applicability domain. Models developed with the partial least squares regression method in MOE and SuperLearner in R showed the best precision and predictivity, with internal coefficients of determination (R²) of 0.88 and 0.82, cross-validated R²s of 0.72 and 0.57, and external R²s of 0.73 and 0.74, respectively. All test chemicals were within the domain of applicability. The results obtained in this study suggest that QSAR modeling can help estimate the placental transfer of environmental chemicals.
... An increasing demand for ethically acceptable and effective methods to assess placenta permeability has led to the development of some ex vivo and in vitro models [3,[11][12][13][14][15][16][17], including a novel trend of "organs on a chip". Transport of compounds across the placenta has also been investigated in silico [5,[18][19][20][21][22][23][24][25][26][27]. Placenta clearance index (CI) models were reported by Giaginis, Zhang, Chou, Hewitt, Guan, Wang and Gely [5,19,20,22,26,28]; fetalto-maternal blood concentration ratios (FMs) were predicted by Takaku, Eguchi, Wang and Lévêque [18,24,27,29]. ...
... Transport of compounds across the placenta has also been investigated in silico [5,[18][19][20][21][22][23][24][25][26][27]. Placenta clearance index (CI) models were reported by Giaginis, Zhang, Chou, Hewitt, Guan, Wang and Gely [5,19,20,22,26,28]; fetalto-maternal blood concentration ratios (FMs) were predicted by Takaku, Eguchi, Wang and Lévêque [18,24,27,29]. The ex vivo parameter CI is usually expressed relative to a reference compound (antipyrine) to avoid inter-laboratory variances and differences arising from different placenta samples [29]. ...
... Human placenta permeability has been studied for different groups of compounds, in particular drugs and environmental pollutants such as pesticides, organophosphorus flame retardants, bisphenols, polycyclic aromatic hydrocarbons, polychlorinated dibenzop-dioxins and dibenzofurans, polychlorinated biphenyls, polybrominated ethers, perfluoroalkyl compounds, and chloronaphthalenes [18,20,24,25,28,[30][31][32][33][34][35][36][37][38]. ...
A total of 16 organic sunscreens and over 160 products of their degradation in biotic and abiotic conditions were investigated in the context of their safety during pregnancy. Drug-likeness and the ability of the studied compounds to be absorbed from the gastrointestinal tract and cross the human placenta were predicted in silico using the SwissADME software (for drug-likeness and oral absorption) and multiple linear regression and “ARKA” models (for placenta permeability expressed as fetus-to-mother blood concentration in the state of equilibrium), with the latter outperforming the MLR models. It was established that most of the studied compounds can be absorbed from the gastrointestinal tract. The drug-likeness of the studied compounds (expressed as a binary descriptor, Lipinski) is closely related to their ability to cross the placenta (most likely by a passive diffusion mechanism). The organic sunscreens and their degradation products are likely to cross the placenta, except for very bulky and highly lipophilic 1,3,5-triazine derivatives; an avobenzone degradation product, 1,2-bis(4-tert-butylphenyl)ethane-1,2-dione; diethylamino hydroxybenzoyl hexyl benzoate; and dimerization products of sunscreens from the 4-methoxycinnamate group.
... MOE is particularly valuable in drug discovery, where it helps researchers. There are some applications of MOE in drug design, including molecular docking [26], homology modeling of proteins [27], protein design [28], scaffold replacement [29], structure-based drug design [30], 3D visualization and making pictures, QSAR modeling [31,32]. ...
The field of materials science is undergoing a transformation driven by the integration of advanced computational methodologies. This chapter explores the application of machine learning tools and web services in materials science modeling, highlighting the significant advancements and efficiencies they bring to the discipline. Researchers can develop and deploy sophisticated models to predict and analyze material properties and behaviors by utilizing programming languages such as R, Python, and C. Machine learning provides a robust framework for handling complex datasets, enabling the discovery of patterns and relationships that are not easily discernible through traditional methods. This chapter delves into various machine learning techniques, including regression, classification, clustering, dimensionality reduction, and their specific applications in materials science. It also discusses the pivotal role of web services in enhancing the accessibility and scalability of these computational tools, fostering collaboration and resource sharing among researchers globally. The chapter offers an in-depth examination of the tools and libraries available in R and Python, such as Scikit-learn, TensorFlow, and the R caret package, illustrating their use through practical examples and case studies. Additionally, it addresses the performance and efficiency benefits C provides in developing computationally intensive simulations. Through detailed discussions and illustrative examples, readers will gain a comprehensive understanding of how to leverage these technologies to address complex materials science challenges.
... In this way, they automatically build an optimal weighted combination of candidate or base learners that minimize the generalization error rate [35,68]. Although the use of Machine Learning in modeling quantitative structure-activity relationships (QSAR) is an area with decades of experience [69][70][71][72][73][74], due to the typical non-linearity and complexity of the associations with countless predictive variables, the use of SuperLearners or other approaches for model stacking is relatively scarce in the field, with just a few examples in the literature [75,76]. This could be due to the relative newness of the Super-Learner method [35,68], coupled with the recent spate of Deep Learning methods that have absorbed most of the efforts in this field [69,[77][78][79]. ...
Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13321-023-00768-y.
Introducción: El Fipronil es un pesticida de amplio espectro que pertenece a la familia de los fenilpirazoles. Posee efectos gabaérgicos y glutamatérgicos. Se ha aplicado de manera extensiva, principalmente en cultivos de chontaduro Bactris gasipaes, como control al picudo Rhynchophorus palmarum. Objetivo: La presente revisión tiene como objetivo analizar la información bibliográfica centrada en las investigaciones realizadas acerca de la toxicidad del Fipronil, con especial énfasis en las herramientas de análisis toxicológico, los puntos finales y las rutas de toxicidad en humanos y animales. Materiales y métodos: La búsqueda de publicaciones con las palabras clave “Fipronil” y “toxicity”, se realizó en las bases de datos Thomson Reuters Web of Science (ISI Web of Knowledge) y Scopus en el periodo comprendido entre los años 1993 y 2022. Las 1492 referencias se descargaron para su análisis utilizando la teoría de grafos para determinar los artículos y autores relevantes, las palabras clave, la evolución de la temática y las distintas relaciones entre ellos. Se realizó, utilizando un script de RStudio desarrollado en el Core of science. Resultados y discusión: Esta revisión permitió identificar tendencias en investigación acerca de los efectos toxicológicos relacionados con la exposición a Fipronil en la reducción de los niveles hormonales asociados al desarrollo sexual, alteraciones en el sistema nervioso, malformaciones congénitas y alteraciones al del comportamiento, combinando estudios patológicos con aproximaciones metabolómicas, las metodologías analíticas para la identificación y propuestas de desarrollo de metodologías in silico para el análisis toxicológico.
In brief
Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of pharmaceutical use during pregnancy.
Abstract
Those undergoing pregnancy are often excluded from clinical drug trials due to the risk that participation would pose to their health and the health of the developing fetus. However, they often require pharmaceuticals to manage health conditions that, if left untreated, could harm themselves or the fetus. This can mean that such individuals take one or more pharmaceuticals during pregnancy, many of which have unknown reproductive effects. Machine learning models have been used to successfully predict a number of reproductive toxicological outcomes for pharmaceuticals, including transplacental transfer, US Food and Drug Administration safety rating, and drug interactions. Models use quantitative chemical and structural features of active compounds as inputs to make predictions concerning the outcome of interest using computational algorithms. Models are validated and evaluated rigorously with metrics such as accuracy, area under the receiver operator curve, sensitivity, and precision. Results from these models can be a potential source of valuable information for pregnant people and their medical providers when making decisions regarding therapeutic drug use. This review summarizes current machine learning applications to make predictions about the risk and toxicity of medication use during pregnancy. Our review of the recent literature revealed that machine learning quantitative structure-activity relationship models can be used successfully to predict the transplacental transfer and the US Food and Drug Administration pregnancy safety category of pharmaceuticals; such models have also been employed to predict drug interactions, though not specifically during pregnancy. This latter topic is a potential area for future research. In this review, no single algorithm or descriptor-calculation software emerged as the most widely used, and their performances depend on a variety of factors, including the outcome of interest and combination of such algorithms and software.
The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.
Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.
In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.
To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj²: 0.907–0.999), robustness (QBOOT²: 0.900–0.937) and predictability (Rext²: 0.889–0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.
Background
The measurement of human fetal-maternal blood concentration ratio (logFM) of chemicals is critical for the risk assessment of chemical-induced developmental toxicity. While a few in vitro and ex vivo experimental methods were developed for predicting logFM of chemicals, the obtained experimental results are not able to directly predict in vivo outcomes.
Methods
A total of 55 chemicals with logFM values representing in vivo fetal-maternal blood ratio were divided into training and test datasets. An interpretable linear regression model was developed along with feature selection methods. Cross-validation on training dataset and prediction on independent test dataset were conducted to validate the prediction model.
Results
This study presents the first valid quantitative structure-activity relationship model following the Organisation for Economic Co-operation and Development (OECD) guidelines based on multiple linear regression for predicting in vivo logFM values. The autocorrelation descriptor AATSC1c and information content descriptor ZMIC1 were identified as informative features for predicting logFM. After the adjustment of the applicability domain, the developed model performs well with correlation coefficients of 0.875, 0.850 and 0.847 for model fitting, leave-one-out cross-validation and independent test, respectively. The model is expected to be useful for assessing human transplacental exposure.
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on the Python machine learning package scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub Auto-sklearn, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won six out of ten phases of the first ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of Auto-sklearn.
The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next five years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.
After 40 years, the 1976 US Toxic Substances Control Act (TSCA) was revised under the Frank R. Lautenberg Chemical Safety for the 21st Century Act. Its original goals of protecting the public from hazardous chemicals were hindered by complex and cumbersome administrative burdens, data limitations, vulnerabilities in risk assessments, and recurring corporate lawsuits. As a result, countless chemicals were entered into commercial use without toxicological information. Few chemicals of the many identified as potential public health threats were regulated or banned. This paper explores the factors that have worked against a comprehensive and rational policy for regulating toxic chemicals and discusses whether the TSCA revisions offer greater public protection against existing and new chemicals.
To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
Exposures to environmental pollutants in utero may increase the risk of adverse health effects. We measured the concentrations of 59 potentially harmful chemicals in 77 maternal and 65 paired umbilical cord blood samples collected in San Francisco during 2010-2011, including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polybrominated diphenyl ethers (PBDEs), hydroxylated PBDEs (OH-PBDEs), and perfluorinated compounds (PFCs) in serum and metals in whole blood. Consistent with previous studies, we found evidence that concentrations of mercury (Hg) and lower-brominated PBDEs were often higher in umbilical cord blood or serum than in maternal samples (median cord:maternal ratio > 1), while for most PFCs and lead (Pb), concentrations in cord blood or serum were generally equal to or lower than their maternal pair (median cord:maternal ratio ≤ 1). In contrast to the conclusions of a recent review, we found evidence that several PCBs and OCPs were also often higher in cord than maternal serum (median cord:maternal ratio > 1) when concentrations are assessed on a lipid-adjusted basis. Our findings suggest that for many chemicals, fetuses may experience higher exposures than their mothers and highlight the need to characterize potential health risks and inform policies aimed at reducing sources of exposure.
Background
Pregnant women are an especially important population to monitor for environmental exposures given the vulnerability of the developing fetus. During pregnancy and lactation chemical body burdens may change due to the significant physiological changes that occur. Developmental exposures to some persistent organic pollutants (POPs) have been linked with adverse health outcomes. Methods
First trimester maternal and cord blood plasma concentrations of several POPs including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCs), polybrominated diphenyl ethers (PBDE)s and perfluoroalkyl substances (PFASs) were measured in samples from 1983 pregnant women enrolled in the Maternal-Infant Research on Environmental Chemicals (MIREC) cohort. Predictors of exposure were also identified. ResultsIn maternal plasma, there was >90 % detection for the perfluoroalkyl substances (PFASs) perfluorooctanoic acid (PFOA), perfluoroctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), and dichlorodiphenyldichloroethylene (DDE), oxychlordane and PCB 138 and 153. Cord blood plasma had much lower detection rates with low or very limited detection for most PCBs and PBDEs. The PFASs were the most frequently detected (23–64 %) chemical class in cord plasma. In a subset of 1st and 3rd trimester paired samples, PFAS concentrations were found to be strongly correlated and had ICCs ranging from 0.64 (PFOA) to 0.83 (PFHxS). The cord:maternal plasma concentration ratios ranged from 0.14 (PFOS) to 0.87 (oxychlordane, lipid adjusted). Similar to other studies, we found parity, maternal age, income, education, smoking status, pre-pregnancy BMI and fish consumption to be significant predictors for most chemicals. Those participants who were foreign-born had significantly higher concentrations of organochlorinated pesticides and PCBs. Conclusions
In the MIREC study, multiple chemical contaminants were quantified in the plasma of pregnant women. In cord plasma PFOA had the highest detection rate. However, compared to other Canadian and international population studies, the MIREC participants had lower contaminant concentrations of these substances.
Perfluoroalkyl substances (PFASs) have been detected in wildlife and human samples worldwide. Toxicology research showed that PFASs could interfere with thyroid hormone homeostasis. In this study, eight PFASs, fifteen PFAS precursors and five thyroid hormones were analyzed in 157 paired maternal and cord serum samples collected in Beijing around delivery. Seven PFASs and two precursors were detected in both maternal and cord sera with significant maternal-fetal correlations (r = 0.336 to 0.806, all P < 0.001). The median ratios of major PFASs concentrations in fetal versus maternal serum were from 0.25:1 (perfluorodecanoic acid, PFDA) to 0.65:1 (perfluorooctanoic acid, PFOA). Spearman partial correlation test showed that maternal thyroid stimulating hormone (TSH) was negatively correlated with most maternal PFASs (r = −0.261 to −0.170, all P < 0.05). Maternal triiodothyronin (T3) and free T3 (FT3) showed negative correlations with most fetal PFASs (r = −0.229 to −0.165 for T3; r = −0.293 to −0.169 for FT3, all P < 0.05). Our results suggest prenatal exposure of fetus to PFASs and potential associations between PFASs and thyroid hormone homeostasis in humans.
Validation is an act of proving that any procedure, process, equipment, material, activity or system performs as expected under given set of conditions and also give the required accuracy, precision, sensitivity, ruggedness, etc. When extended to an analytical procedure, depending upon the application, it means that a method works reproducibly, when carried out by same or different persons, in same or different laboratories, using different reagents, different equipments, etc. In this review article we discussed about the strategy and importance of validation of analytical methods.
Pregnancy is a complex state where changes in maternal physiology have evolved to favor the development and growth of the placenta and the fetus. These adaptations may affect preexisting disease or result in pregnancy-specific disorders. Similarly, variations in physiology may alter the pharmacokinetics or pharmacodynamics that determines drug dosing and effect. It follows that detailed pharmacologic information is required to adjust therapeutic treatment strategies during pregnancy. Understanding both pregnancy physiology and the gestation-specific pharmacology of different agents is necessary to achieve effective treatment and limit maternal and fetal risk. Unfortunately, most drug studies have excluded pregnant women based on often-mistaken concerns regarding fetal risk. Furthermore, over two-thirds of women receive prescription drugs while pregnant, with treatment and dosing strategies based on data from healthy male volunteers and non-pregnant women, and with little adjustment for the complex physiology of pregnancy and its unique disease states. This review will describe basic concepts in pharmacokinetics and their clinical relevance and highlight the variations in pregnancy that may impact the pharmacokinetic properties of medications.
The present study aims to predict the maternal–fetal transfer rates of the polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and polybrominated diphenyl ethers (PBDEs), and dioxin-like compounds using a quantitative structure–activity relationship model. The relation between the maternal–fetal transfer rate and the contaminants’ physicochemical properties was investigated by multiple linear regression (MLR), partial least square regression (PLS), and random forest regression (RF). The 10-fold cross-validation technique estimated low predictive performances for both MLR and PLS models (R
2CV = 0.425 ± 0.0964 for MLR and R
2CV = 0.492 ± 0.115 for PLS) and is in agreement with an external test (R
2pred = 0.129 for MLR and R
2pred = 0.123 for PLS). In contrast, the RF model exhibits good predictive performance, estimated through 10-fold cross-validation (R
2CV = 0.566 ± 0.0885) and an external test set (R
2pred = 0.519). Molecular weight and polarity were selected in all models as important parameters that may predict the ability of a molecule to cross the placenta to the fetus.
Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR) method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS) variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI). The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r² = 0.9064, RMSE = 0.09, q² = 0.7323, rp² = 0.7656, RMSP = 0.14). The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.
The developing fetus is likely to be exposed to the same environmental chemicals as the mother during critical periods of growth and development. The degree of maternal-fetal transfer of chemical compounds will be affected by chemical and physical properties such as lipophilicity, protein binding, and active transport mechanisms that influence absorption and distribution in maternal tissues. However, these transfer processes are not fully understood for most environmental chemicals. This review summarizes reported data from more than 100 studies on the ratios of cord:maternal blood concentrations for a range of chemicals including brominated flame-retardant compounds, polychlorinated biphenyls (PCB), polychlorinated dibenzodioxins and dibenzofurans, organochlorine pesticides, perfluorinated compounds, polyaromatic hydrocarbons, metals, and tobacco smoke components. The studies for the chemical classes represented suggest that chemicals frequently detected in maternal blood will also be detectable in cord blood. For most chemical classes, cord blood concentrations were found to be similar to or lower than those in maternal blood, with reported cord:maternal ratios generally between 0.1 and 1. Exceptions were observed for selected brominated flame-retardant compounds, polyaromatic hydrocarbons, and some metals, for which reported ratios were consistently greater than 1. Careful interpretation of the data in a risk assessment context is required because measured concentrations of environmental chemicals in cord blood (and thus the fetus) do not necessarily imply adverse effects or risk. Guidelines and recommendations for future cord:maternal blood biomonitoring studies are discussed.
Prenatal life is the most sensitive stage of human development to environmental pollutants. Early exposure to persistent organic pollutants (POPs) may increase the risk of adverse health effects during childhood. The mechanisms of transference of POPs during pregnancy are still not well understood. The present study is aimed to investigate the transfer of POPs between mother and fetus. The concentrations of 14 organochlorine pesticides, 7 polychlorinated biphenyls (PCBs) and 14 polybromodiphenyl ether (PBDEs) congeners have been measured in 308 maternal serum samples, their respective umbilical cords and 50 placental tissues from a mother-infant cohort representative of Spanish general population. In general, the adjusted lipid-basis concentrations were higher in maternal serum than in cord serum and placenta. The concentrations of most pollutants between maternal serum and cord serum and between maternal serum and placenta were significantly correlated. These distributions were consistent with a predominant maternal source that transfers the pollutants into the placenta and the fetus. However, this distribution did not correspond to passive diffusion of these compounds between these tissues according to lipid content. The compounds more readily metabolized were higher in newborns, which suggest that differences in metabolic capabilities may be responsible of the observed variations in POP distributions between mother and newborns. Prenatal exposure to 4,4'-DDT and some PBDEs such as BDE 99 and BDE 209 is much higher than it could be anticipated from the composition of maternal serum. POP exposure assessment studies of newborns may overlook the effects of some of these pollutants if they only consider maternal determinations.
The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.
Due to the relevance that molecular descriptors are constantly gaining in several scientific fields, software for the calculation of molecular descriptors have become very important tools for the scientists. In this paper, the main characteristics of DRAGON software for the calculation of molecular descriptors are shortly illustrated.
Venous blood was drawn from 35 pregnant Hispanic women living in Brownsville, Texas, and matched cord blood was collected at birth. Gas chromatography/mass spectrometry was used to measure concentrations of 55 individual PAHs or groups of PAHs. Results indicate that these women and their fetuses were regularly exposed to multiple PAHs at comparatively low concentrations, with levels in cord blood generally exceeding levels in paired maternal blood. While the possibility of related adverse effects on the fetus is uncertain, these exposures in combination with socioeconomically-disadvantaged and environmentally-challenging living conditions raise legitimate public health concerns.
We have measured 29 pesticides in plasma samples collected at birth between 1998 and 2001 from 230 mother and newborn pairs enrolled in the Columbia Center for Children's Environmental Health prospective cohort study. Our prior research has shown widespread pesticide use during pregnancy among this urban minority cohort from New York City. We also measured eight pesticides in 48-hr personal air samples collected from the mothers during pregnancy. The following seven pesticides were detected in 48-83% of plasma samples (range, 1-270 pg/g): the organophosphates chlorpyrifos and diazinon, the carbamates bendiocarb and 2-isopropoxyphenol (metabolite of propoxur), and the fungicides dicloran, phthalimide (metabolite of folpet and captan), and tetrahydrophthalimide (metabolite of captan and captafol). Maternal and cord plasma levels were similar and, except for phthalimide, were highly correlated (p < 0.001). Chlorpyrifos, diazinon, and propoxur were detected in 100% of personal air samples (range, 0.7-6,010 ng/m(3)). Diazinon and propoxur levels were significantly higher in the personal air of women reporting use of an exterminator, can sprays, and/or pest bombs during pregnancy compared with women reporting no pesticide use or use of lower toxicity methods only. A significant correlation was seen between personal air level of chlorpyrifos, diazinon, and propoxur and levels of these insecticides or their metabolites in plasma samples (maternal and/or cord, p < 0.05). The fungicide ortho-phenylphenol was also detected in 100% of air samples but was not measured in plasma. The remaining 22 pesticides were detected in 0-45% of air or plasma samples. Chlorpyrifos, diazinon, propoxur, and bendiocarb levels in air and/or plasma decreased significantly between 1998 and 2001. Findings indicate that pesticide exposures are frequent but decreasing and that the pesticides are readily transferred to the developing fetus during pregnancy.
Previous studies demonstrated that per- and polyfluoroalkyl substances (PFASs) can cross human placental barrier. However, their transplacental transfer efficiencies (TTEs) have not been investigated in preterm delivery and the role of placental transport proteins has rarely been explored. Our study hypothesized that the TTEs of PFASs could differ between preterm and full-term delivery and some placental transporters could be involved in active maternofetal PFAS transfer. In the present study, the median TTEs of 16 individual PFAS chemicals or isomers were determined to be 0.23 – 1.72 in matched maternal-cord serum pairs with preterm delivery (N = 86), significantly lower than those (0.35 – 2.26) determined in full-term delivery (N = 187). Significant associations were determined between the TTEs of several PFASs and the mRNA expression levels of selected transporters located on the brush border membrane. The association patterns also differed significantly between preterm and full-term delivery and exhibited a chemical-specific manner. For example, the expression of MRP2 exhibited significantly positive associations with the TTEs of linear and branched PFOS isomers in full-term delivery, but negative, non-significant associations were observed in preterm delivery. This is the first study to compare transplacental transfer of PFASs between preterm and full-term delivery and indicate that some placental transport proteins could be involved in active transmission. The mechanisms underlying cross-placental transfer of PFASs require further investigations in order to better elucidate their risks to fetal health and birth outcomes.
Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth “floating” methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.
Currently, there is limited information about the mechanism of the human transplacental transfer for organochlorine pesticides (OCPs). This study aimed to evaluate the transplacental transfer of OCPs to better understand the influencing factors of exposure and transplacental efficiency. The study involved quantitative determination of OCPs and the enantiomer fraction (EF) of chiral OCPs in pregnant women from Wuhan, China. The results indicate that the exposure levels of OCPs varied in the order: maternal serum > cord serum > placenta. Chiral contaminants, such as α-HCH, o,p′-DDD and o,p′-DDT, were non-racemic in the three biological matrices, wherein EFα-HCH
We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree‐based models are briefly described.
The stability problem of natural slopes, filled slopes, and cut slopes are commonly encountered in Civil Engineering Projects. Predicting the slope stability is an everyday task for geotechnical engineers. In this paper, a study has been done to predict the factor of safety (FOS) of the slopes using multiple linear regression (MLR) and artificial neural network (ANN). A total of 200 cases with different geometric and shear strength parameters were analyzed by using the well-known slope stability methods like Fellenius method, Bishop’s method, Janbu method, and Morgenstern and Price method. The FOS values obtained by these slope stability methods were used to develop the prediction models using MLR and ANN. Further, a few case studies have been done along the Jorabat-Shillong Expressway (NH-40) in India, using the finite element method (FEM). The output values of FEM were compared with the developed prediction models to find the best prediction model and the results were discussed.
Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) is a high-priority public health concern. However, maternal to fetal transplacental transfer of PAHs has not been systematically studied. To investigate the transplacental transfer of PAHs from mother to fetus and determine the influence of lipophilicity (octanol-water partition coefficient, KOW) on transfer process, in the present study, we measured the concentrations of 15 PAHs in 95 paired maternal and umbilical cord serum, and placenta samples (in total 285 samples) collected in Shanghai, China. The average concentration of total PAHs was the highest in maternal serums (1290 ng g⁻¹ lipid), followed by umbilical cord serums (1150 ng g⁻¹ lipid). The value was the lowest in placenta samples (673 ng g⁻¹ lipid). Low molecular weight PAHs were the predominant compounds in the three matrices. Increases in fish and meat consumption did not lead to increases in maternal PAH levels, and no obvious gender differences in umbilical cord serums were observed. The widespread presence of PAHs in umbilical cord serums indicated the occurrence of transplacental transfer. The ratios of PAH concentrations in umbilical cord serum to those in maternal serum (F/M) and the concentrations in placenta to those in maternal serum (P/M) of paired samples were analyzed to characterize the transfer process of individual PAHs. Most F/M ratios on lipid basis were close to one (range: 0.79 to 1.36), which suggested that passive diffusion may control the transplacental transfer of PAHs from maternal serum to the fetal circulation. The P/M and F/M values calculated on lipid basis showed that PAHs with lower KOW were more likely to transfer from mother to fetus via the placenta.
In the beginning of this book we postulated (without any discussion) that learning is a problem of function estimation on the basis of empirical data. To solve this problem we used a classical inductive principle — the ERM principle. Later, however, we introduced a new principle — the SRM principle. Nevertheless, the general understanding of the problem remains based on the statistics of large samples: the goal is to derive the rule that possesses the lowest risk. The goal of obtaining the “lowest risk” reflects the philosophy of large sample size statistics: the rule with low risk is good because if we use this rule for a large test set, with high probability, the means of losses will be small.
Abnormalities of placental development and function are known to underlie many pathologies of pregnancy, including spontaneous preterm birth, fetal growth restriction, and preeclampsia. A growing body of evidence also underscores the importance of placental dysfunction in the lifelong health of both mother and offspring. However, our knowledge regarding placental structure and function throughout pregnancy remains limited. Understanding the temporal growth and functionality of the human placenta throughout the entirety of gestation is important if we are to gain a better understanding of placental dysfunction. The utilization of new technologies and imaging techniques that could enable safe monitoring of placental growth and function in vivo has become a major focus area for the National Institutes of Child Health and Human Development, as evident by the establishment of the "Human Placenta Project." Many of the objectives of the Human Placenta Project will necessitate preclinical studies and testing in appropriately designed animal models that can be readily translated to the clinical setting. This review will describe the advantages and limitations of relevant animals such as the guinea pig, sheep, and nonhuman primate models that have been used to study the role of the placenta in fetal growth disorders, preeclampsia, or other maternal diseases during pregnancy.
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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.
Quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/QSTR) modeling has been used in medicinal chemistry, material sciences, environmental fate modeling, risk assessment and computational toxicology for a long time. The Organization of Economic Co-operation and Development (OECD) has recommended that for application of validated QSAR models for prediction of new data points, there is a strict requirement of defining the applicability domain (AD) according to the Principle 3. The AD is a theoretical region in chemical space encompassing both the model descriptors and modeled response which allows one to estimate the uncertainty in the prediction of a particular compound based on how similar it is to the training compounds employed in the model development. The AD is an important tool for reliable application of QSAR models, while characterization of interpolation space is significant in defining the AD. An attempt is made here to suggest a simple method for defining the X-outliers (in case of the training set) and identifying the compounds that reside outside the AD (in case of the test set) employing the basic theory of the standardization approach. Further, a standalone application named “Applicability domain using standardization approach” (available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/) has been developed. The present study reports that the web application can be easily used for identification of the X-outliers for training set compounds and detection of the test compounds residing outside the AD using the descriptor pool of the training and test sets.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ∗∗∗, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
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Establishing methods for the assessment of fetal exposure to chemicals is important for the prevention or prediction of the child’s future disease risk. In the present study, we aimed to determine the influence of molecular weight on the likelihood of chemical transfer from mother to fetus via the placenta. The correlation between molecular weight and placental transfer rates of congeners/isomers of polychlorinated biphenyls (PCBs) and dioxins was examined. Twenty-nine sample sets of maternal blood, umbilical cord, and umbilical cord blood were used to measure PCB concentration, and 41 sample sets were used to analyze dioxins. Placental transfer rates were calculated using the concentrations of PCBs, dioxins, and their congeners/isomers within these sample sets. Transfer rate correlated negatively with molecular weight for PCB congeners, normalized using wet and lipid weights. The transfer rates of PCB or dioxin congeners differed from those of total PCBs or dioxins. The transfer rate for dioxin congeners did not always correlate significantly with molecular weight, perhaps because of the small sample size or other factors. Further improvement of the analytical methods for dioxin congeners is required. The findings of the present study suggested that PCBs, dioxins, or their congeners with lower molecular weights are more likely to be transferred from mother to fetus via the placenta. Consideration of chemical molecular weight and transfer rate could therefore contribute to the assessment of fetal exposure.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
This paper is an exposition of the use of ridge regression methods. Two examples from the literature are used as a base. Attention is focused on the RIDGE TRACE which is a two-dimensional graphical procedure for portraying the complex relationships in multifactor data. Recommendations are made for obtaining a better regression equation than that given by ordinary least squares estimation.
Although levels of poly- and perfluoroalkyl substances (PFASs) in human maternal and neonatal blood have been widely reported in the literature, maternal-fetal transmission of PFASs with carbon chain length is presently not well understood. In this study, 11 PFASs were analyzed in matched samples, including not only maternal blood (MB, n = 31) and cord blood (CB, n = 30), but also placenta (n = 29), and amniotic fluid (AF, n = 29). Except for perfluorohexanoic acid (PFHxA), the detection frequencies of PFASs were similar among placenta, MB, and CB (> 80% for 8 PFASs, non-detectable for 2 PFASs). Though only perfluorooctanoic acid (PFOA) was frequently detected (> 90%) in AF, with a median concentration of 0.043 ng/mL, other 5 PFASs were also detectable in AF samples with low concentrations (mean: 0.013 to 0.191 ng/mL). This suggests that in addition to blood borne in utero exposure, the fetus is also exposed to low levels of PFASs through AF. Concentrations of PFOA in AF were positively correlated with those in MB (r = 0.738, p < 0.01) and CB (r = 0.683, p < 0.001), suggesting that AF concentration could reflect fetal PFOA exposure during pregnancy and can be used as a biomarker. To clarify the effects of carbon chain length on maternal transfer of PFASs, we calculated maternal transfer efficiencies of PFASs from MB to CB (TMB-CB). A U-shaped trend in TMB-CB of C7-C12 perfluoroalkyl carboxylic acids (PFCAs) with increasing carbon chain length was found in this study for the first time. The U-shaped TMB-CB of PFCAs with carbon chain length is an integrated result of opposite trend of the ratios between MB/placenta and placenta/CB based on carbon chain length. This is the first study to report the occurrence of PFASs in human placenta. The results reported here enabled better understanding of the maternal-fetal transmission of PFASs.
Ridge regression is a popular parameter estimation method used to address the collinearity problem frequently arising in multiple linear regression. The formulation of the ridge methodology is reviewed and properties of the ridge estimates capsulated. In particular, four rationales leading to a regression estimator of the ridge form are summarized. Algebraic properties of the ridge regression coefficients are given, which elucidate the behavior of a ridge trace for small values of the ridge parameter (i.e., close to the least squares solution) and for large values of the ridge parameter. Further properties involving coefficient sign changes and rates-of-change, as functions of the ridge parameter, are given for specific correlation structures among the independent variables. These results help relate the visual behavior of a ridge trace to the underlying structure of the data. Copyright © 2009 John Wiley & Sons, Inc.
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In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.
Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth “floating” methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.
The chemical notation language SMILES is designed for the conversion of an arbitrarily chosen description of a chemical structure to one unique notation. This is accomplished in a two-stage algorithm, CANGEN. The first stage involves CANonicalization of structure, whereby the molecule is treated as a graph with nodes (atoms) and edges (bonds). Each atom is canonically ordered and labeled. In the second stage, starting with the lowest labeled atom, a molecular graph is GENerated, which is the unique SMILES structure.
SMILES (Simplified Molecular Input Line Entry System) is a chemical notation system designed for modern chemical information processing. Based on principles of molecular graph theory, SMILES allows rigorous structure specification by use of a very small and natural grammar. The SMILES notation system is also well suited for high-speed machine processing. The resulting ease of usage by the chemist and machine compatability allow many highly efficient chemical computer applications to be designed including generation of a unique notation, constant-speed (zeroeth order) database retrieval, flexible substructure searching, and property prediction models.
After nearly five decades ``in the making'', QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever growing collections of biologically active chemical compounds. This review examines most critical QSAR modeling routines that we regard as best practices in the field. We discuss these procedures in the context of integrative predictive QSAR modeling workflow that is focused on achieving models of the highest statistical rigor and external predictive power. Specific elements of the workflow consist of data preparation including chemical structure (and when possible, associated biological data) curation, outlier detection, dataset balancing, and model validation. We especially emphasize procedures used to validate models, both internally and external
PaDEL-Descriptor is a software for calculating molecular descriptors and fingerprints. The software currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth.
PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular descriptors.
The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded.
PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor.
Brominated flame retardants (BFRs), in particular the polybrominated diphenyl ethers (PBDEs), have been used in consumer products for many years to increase fire resistance. Recently, developmental neurotoxicity at very low levels has increased the concern about these compounds. The major objectives of this study were to investigate the maternal and fetal exposure to PBDEs on the basis of maternal and umbilical cord plasma samples and to study the extent of placental transfer for different PBDE congeners. The findings were also compared with previously observed PBDE levels and patterns determined in placental tissue from the same individuals, and the relationship with the external exposure from house dust from the participants' homes was explored. Samples of maternal and umbilical cord plasma from a cohort of 51 pregnant women from the Copenhagen area were collected. Paired maternal and umbilical cord plasma were analysed for BDE-28, 37, 47, 85, 99, 100, 119, 138, 153, 154, 183, 209 and the brominated biphenyl BB-153 using automated SPE extraction and GC-HRMS for the tri- to hepta-BDEs and GC-LRMS (ECNI) for BDE-209. PBDEs were detected in all maternal and umbilical cord plasma samples. The sum of tri- to hexa-BDEs (SigmaPBDE) in maternal plasma varied between 640 and 51,946 pg/g lipid weight (lw) with a median level of 1765 pg/g lw. In the umbilical cord samples SigmaPBDE varied between 213 and 54,346 pg/g lw with a median of 958 pg/g lw. The levels observed in fetal and maternal plasma were highly correlated, but the placental transport of PBDE congeners was found to decrease with increasing diphenyl ether bromination. Maternal concentrations were significantly correlated (p<0.05) for most congeners with the previously determined concentrations in placental tissue from the same individuals. Furthermore, positive correlations (p<0.05) were found for BDE-28, 47, 100, 209 and SigmaPBDE in maternal plasma and house dust as well as for SigmaPBDE in umbilical cord plasma and house dust. The positive correlations for PBDEs for both maternal and umbilical cord plasma with house dust showed that domestic house dust is a significant source of human exposure to PBDEs in Denmark including in utero exposure.
Pharmacological agents and environmental pollutants can transfer from mother to fetus across the placental barrier, leading to reproductive toxic effects. Ex vivo human placental perfusion constitutes the most widely used method to study placental transfer and metabolism of drugs and chemicals. The aim of the present study was to evaluate whether quantitative structure-activity relationship (QSAR) methodology could serve as an effective alternative tool to estimate drugs and chemicals transport across the human placental barrier on the basis of easily interpretable molecular, physicochemical and structural properties. Multivariate data analysis (MVDA) was applied to a set of 88 structurally diverse drugs and chemicals to model placental transfer expressed by clearance index values compiled from literature sources. An adequate and robust QSAR model (r(2) = 0.73, Q(2) = 0.71, RMSEE = 0.15) was established, providing an informative illustration of the contributing physicochemical, molecular and structural properties of the compounds in placental transfer process. Descriptors reflecting the polarity of compounds proved to be the most important with a negative sign. Lipophilicity and, at a lower extent, molecular size parameters exerted positive contribution in the model. Thus, QSAR analysis may be considered as a promising alternative tool to support high-throughput screening of drugs and chemicals in respect to their transport across placental barrier.
Polychlorinated biphenyls (PCBs) and organochlorine pesticides, such as hexachlorobenzene (HCB) and 1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethylene (p,p'-DDE) are compounds widespread in the environment, highly lipophilic, and accumulate in biological systems. The newborn are exposed to these organochlorine compounds across the placenta and through breastfeeding. This study reports the levels of selected PCB congeners, p,p'-DDE and HCB in maternal blood and cord blood samples collected at delivery between November and December 1999 from 44 women living in the urban area of Antwerp, Belgium. Results show that all newborns contained detectable levels of PCBs, p,p'-DDE and HCB. The median concentration of PCBs was 450 pg/ml and ranged between 120 and 1580 pg/ml, while the median concentrations of HCB and p,p'-DDE were 70 and 490 pg/ml, respectively. Concentrations of PCBs and p,p'-DDE in cord blood (ng/ml) were positively associated with concentrations in maternal blood (ng/ml) (coefficients=0.74 and 0.92, P<0.05). We conclude that all investigated organochlorine compounds have an efficient transplacental transfer.