| 1. |
- 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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| 2. |
- 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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| 3. |
- Chelcea, Ioana C., et al.
(författare)
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Investigating the OECD database of per- And polyfluoroalkyl substances-chemical variation and applicability of current fate models
- 2020
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Ingår i: Environmental Chemistry. - : CSIRO. - 1448-2517 .- 1449-8979. ; 17:7, s. 498-508
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Tidskriftsartikel (refereegranskat)abstract
- Many per- and polyfluoroalkyl substances (PFASs) have been identified in the environment, and some have been shown to be extremely persistent and even toxic, thus raising concerns about their effects on human health and the environment. Despite this, little is known about most PFASs. In this study, the comprehensive database of over 4700 PFAS entries recently compiled by the OECD was curated and the chemical variation was analysed in detail. The analysis revealed 3363 individual PFASs with a huge variation in chemical functionalities and a wide range of mixtures and polymers. A hierarchical clustering methodology was employed on the curated database, which resulted in 12 groups, where only half were populated by well-studied compounds thus indicating the large knowledge gaps. We selected both a theoretical and a procurable training set that covered a substantial part of the chemical domain based on these clusters. Several computational models to predict physicochemical and environmental fate related properties were assessed, which indicated their lack of applicability for PFASs and the urgent need for experimental data for training and validating these models. Our findings indicate reasonable predictions of the octanol-water partition coefficient for a small chemical domain of PFASs but large data gaps and uncertainties for water solubility, bioconcentration factor, and acid dissociation factor predictions. Improved computational tools are necessary for assessing risks of PFASs and for including suggested training set compounds in future testing of both physicochemical and effect-related data. This should provide a solid basis for better chemical understanding and future model development purposes.
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| 4. |
- Jesús Naveja, J., et al.
(författare)
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Chemical space, diversity and activity landscape analysis of estrogen receptor binders
- 2018
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Ingår i: RSC Advances. - : Royal Society of Chemistry. - 2046-2069. ; 8:67, s. 38229-38237
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.
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| 5. |
- Lupu, Diana, et al.
(författare)
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Fluoxetine Affects Differentiation of Midbrain Dopaminergic Neurons In Vitro
- 2018
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Ingår i: Molecular Pharmacology. - New York : American Society for Pharmacology and Experimental Therapeutics. - 0026-895X .- 1521-0111. ; 94:4, s. 1220-1231
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Tidskriftsartikel (refereegranskat)abstract
- Recent meta-analyses found an association between prenatal exposure to the antidepressant fluoxetine (FLX) and an increased risk of autism in children. This developmental disorder has been related to dysfunctions in the brains' rewards circuitry, which, in turn, has been linked to dysfunctions in dopaminergic (DA) signaling. The present study investigated if FLX affects processes involved in dopaminergic neuronal differentiation. Mouse neuronal precursors were differentiated into midbrain dopaminergic precursor cells (mDPCs) and concomitantly exposed to clinically relevant doses of FLX. Subsequently, dopaminergic precursors were evaluated for expression of differentiation and stemness markers using quantitative polymerase chain reaction. FLX treatment led to increases in early regional specification markers orthodenticle homeobox 2 (Otx2) and homeobox engrailed-1 and -2 (En1 and En2). On the other hand, two transcription factors essential for midbrain dopaminergic (mDA) neurogenesis, LIM homeobox transcription factor 1 alpha (Lmx1a) and paired-like homeodomain transcription factor 3 (Pitx3) were downregulated by FLX treatment. The stemness marker nestin (Nes) was increased, whereas the neuronal differentiation marker beta 3-tubulin (Tubb3) decreased. Additionally, we observed that FLX modulates the expression of several genes associated with autism spectrum disorder and downregulates the estrogen receptors (ERs) alpha and beta. Further investigations using ER beta knockout (BERKO) mDPCs showed that FLX had no or even opposite effects on several of the genes analyzed. These findings suggest that FLX affects differentiation of the dopaminergic system by increasing production of dopaminergic precursors, yet decreasing their maturation, partly via interference with the estrogen system.
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| 6. |
- Quintana-Belmares, Raúl Omar, et al.
(författare)
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Phthalate esters on urban airborne particles : Levels in PM10 and PM2.5 from Mexico City and theoretical assessment of lung exposure
- 2018
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Ingår i: Environmental Research. - : Elsevier BV. - 0013-9351 .- 1096-0953. ; 161, s. 439-445
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Tidskriftsartikel (refereegranskat)abstract
- Endocrine disrupting chemicals (EDCs) from the environment are associated with reproductive abnormalities (i.e. decreased sperm concentration; increased endometriosis) and alterations of the cardiovascular system (i.e. increased blood pressure and risk of coronary disease). Some phthalates esters have been identified as EDCs, for which inhalation is considered as one of the routes of exposure. However, only little is known regarding inhalational exposure to EDCs via urban airborne particles. In the present study, we report the monthly concentration of 8 phthalate esters measured in PM10 and PM2.5 collected and recovered during 7 months in a highly populated area of Mexico City. Using the levels of PM10 and PM2.5 reported by the automatized network of environmental monitoring of Mexico City for the sampling site, we estimated exposure levels for people of different ages and gender. Two endocrine disrupting compounds, the phthalate esters DEHP and DnBP, were found on the particles in higher concentrations during the warmer months of the year. The highest concentration was reported for DEHP (229.7 mu g/g of particles) in PM2.5 collected in May 2013. After calculations of the DEHP concentration in the atmosphere, and using the respiratory flow rate, we determined males were potentially exposed to larger quantities of DEHP, reaching up to 18 ng/8 h in April 2013. Despite the concentrations of phthalates seem to be rather small, a comprehensive characterization of its presence is necessary in order to evaluate the overall exposure to these compounds, providing a clear view of exposure on children, adolescents and pregnant women.
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