| 1. |
- Mamsen, Linn Salto, et al.
(författare)
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Concentrations of perfluoroalkyl substances (PFASs) in human embryonic and fetal organs from first, second, and third trimester pregnancies
- 2019
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Ingår i: Environment International. - : Elsevier BV. - 0160-4120 .- 1873-6750. ; 124, s. 482-492
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Tidskriftsartikel (refereegranskat)abstract
- Background: The persistent environmental contaminants perfluoroalkyl substances (PFASs) have gained attention due to their potential adverse health effects, in particular following early life exposure. Information on human fetal exposure to PFASs is currently limited to one report on first trimester samples. There is no data available on PFAS concentrations in fetal organs throughout all three trimesters of pregnancy. Methods: We measured the concentrations of perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnA), and perfluorohexane sulfonic acid (PFHxS) in human embryos and fetuses with corresponding placentas and maternal serum samples derived from elective pregnancy terminations and cases of intrauterine fetal death. A total of 78 embryos and fetuses aged 7–42 gestational weeks were included and a total of 225 fetal organs covering liver, lung, heart, central nervous system (CNS), and adipose tissue were analyzed, together with 71 placentas and 63 maternal serum samples. PFAS concentrations were assayed by liquid chromatography/triple quadrupole mass spectrometry. Results: All evaluated PFASs were detected and quantified in maternal sera, placentas and embryos/fetuses. In maternal serum samples, PFOS was detected in highest concentrations, followed by PFOA > PFNA > PFDA = PFUnA = PFHxS. Similarly, PFOS was detected in highest concentrations in embryo/fetal tissues, followed by PFOA > PFNA = PFDA = PFUnA. PFHxS was detected in very few fetuses. In general, PFAS concentrations in embryo/fetal tissue (ng/g) were lower than maternal serum (ng/ml) but similar to placenta concentrations. The total PFAS burden (i.e. the sum of all PFASs) was highest in lung tissue in first trimester samples and in liver in second and third trimester samples. The burden was lowest in CNS samples irrespective of fetal age. The placenta:maternal serum ratios of PFOS, PFOA and PFNA increased across gestation suggesting bioaccumulation in the placenta. Further, we observed that the ratios were higher in pregnancies with male fetuses compared to female fetuses. Conclusions: Human fetuses were intrinsically exposed to a mixture of PFASs throughout gestation. The compounds were detected in all analyzed tissues, suggesting that PFASs reach and may affect many types of organs. Collectively, our results demonstrate that PFASs pass the placenta and deposit to embryo and fetal tissues, calling for risk assessment of gestational exposures.
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| 2. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction of HDAC inhibitors
- 2019
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 30:4, s. 265-277
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Tidskriftsartikel (refereegranskat)abstract
- The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.
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| 3. |
- Zhang, Jin, et al.
(författare)
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LightGBM : An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets
- 2019
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Ingår i: Journal of Chemical Information and Modeling. - Washington : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 59:10, s. 4150-4158
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster speed and lower cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm, inherited its high predictivity but resolved its scalability and long computational time by adopting a leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity data sets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity data sets. We recommend LightGBM for applications of in silico safety assessment and also other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related end points of large compound libraries present in the pharmaceutical and chemical industry.
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