Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Mixtures of persistent organic pollutants are found in vital organs of late gestation human fetuses.

Persistent organic pollutants (POPs) are industrial chemicals with long half-lives. Early life exposure to POPs has been associated with adverse effects. Fetal exposure is typically estimated based on concentrations in maternal serum or placenta and little is known on the actual fetal exposure. We measured the concentrations of nine organochlorine pesticides (OCPs), ten polychlorinated biphenyl (PCB) congeners, and polybrominated diphenyl ether (PBDE) congeners by gas chromatography - tandem mass spectrometry in maternal serum, placenta, and fetal tissues (adipose tissue, liver, heart, lung and brain) in 20 pregnancies that ended in stillbirth (gestational weeks 36-41). The data were combined with our earlier data on perfluoroalkyl substances (PFASs) in the same cohort (Mamsen et al. 2019). HCB, p,p'-DDE, PCB 138 and PCB 153 were quantified in all samples of maternal serum, placenta and fetal tissues. All 22 POPs were detected in all fetal adipose tissue samples, even in cases where they could not be detected in maternal serum or placenta. Tissue:serum ratios were significantly higher in later gestations, male fetuses, and pregnancies with normal placental function. OCPs showed the highest tissue:serum ratios and PFAS the lowest. The highest chemical burden was found in adipose tissue and lowest in the brain. Overall, all studied human fetuses were intrinsically exposed to mixtures of POPs. Tissue:serum ratios were significantly modified by gestational age, fetal sex and placental function. Importantly, more chemicals were detected in fetal tissues compared to maternal serum and placenta, implying that these proxy samples may provide a misleading picture of actual fetal exposures.

On the early events of the calcium-induced activation of coagulation factor XIII-A2 and tissue transglutaminase: an in silico study.

Both coagulation factor XIII-A2 (FXIII-A2) and tissue transglutaminase (TG2) play distinctive and important roles in homeostasis by crosslinking proteins or peptides via isopeptide bonds. In this present study, a series of microsecond-long all-atom molecular dynamics (MD) simulations were carried out in order to reveal the dynamic, atomic-level events which may contribute to the activation of these proteins via the binding of calcium ions. In addition to previously conducted in vitro and crystallographic studies, further suggestions have been made concerning the calcium binding features of these enzymes. The different systems used for running the simulations were based on the zymogen, computationally cleaved and even the activation peptide (AP-FXIII) free FXIII-A2' homodimer form. The effects of various ionic environments have also been explored in the simulations of FXIII-A2. Our results suggest that the presence of calcium ions can cause increased AP fluctuations, which ultimately could lead to their relocation on the homodimer surface. The release of these APs seems to be crucial for rotation of the A subunits based on equilibrium MD simulations. The primary evidence for this assumption comes from the predicted principal component eigenvector which is considered as the first, large-scale event of the overall activation process. To get a more accurate atomic-level description, the calcium binding sites of TG2 have also been investigated in our extensive in silico experiments, which suggests the presence of previously unidentified binding sites as well.Communicated by Ramaswamy H. Sarma.

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets.

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.

Concentrations of perfluoroalkyl substances (PFASs) in human embryonic and fetal organs from first, second, and third trimester pregnancies.

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.

Fluoxetine Affects Differentiation of Midbrain Dopaminergic Neurons In Vitro.

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 α (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 ß3-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) α and ß Further investigations using ERß 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.

Phthalate esters on urban airborne particles: Levels in PM10 and PM2.5 from Mexico City and theoretical assessment of lung exposure.

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 (229.7µg/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 18ng/8h 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.

Half-lives of PFOS, PFHxS and PFOA after end of exposure to contaminated drinking water.

BACKGROUND: Municipal drinking water contaminated with perfluorinated alkyl acids had been distributed to one-third of households in Ronneby, Sweden. The source was firefighting foam used in a nearby airfield since the mid-1980s. Clean water was provided from 16 December 2013. OBJECTIVE: To determine the rates of decline in serum perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), and their corresponding half-lives. METHODS: Up to seven blood samples were collected between June 2014 and September 2016 from 106 participants (age 4-84 years, 53% female). RESULTS: Median initial serum concentrations were PFHxS, 277 ng/mL (range 12-1660); PFOS, 345 ng/mL (range 24-1500); and PFOA, 18 ng/mL (range 2.4-92). The covariate-adjusted average rates of decrease in serum were PFHxS, 13% per year (95% CI 12% to 15%); PFOS, 20% per year (95% CI 19% to 22%); and PFOA, 26% per year (95% CI 24% to 28%). The observed data are consistent with a first-order elimination model. The mean estimated half-life was 5.3 years (95% CI 4.6 to 6.0) for PFHxS, 3.4 years (95% CI 3.1 to 3.7) for PFOS and 2.7 years (95% CI 2.5 to 2.9) for PFOA. The interindividual variation of half-life was around threefold when comparing the 5th and 95th percentiles. There was a marked sex difference with more rapid elimination in women for PFHxS and PFOS, but only marginally for PFOA. CONCLUSIONS: The estimated half-life for PFHxS was considerably longer than for PFOS and PFOA. For PFHxS and PFOS, the average half-life is shorter than the previously published estimates. For PFOA the half-life is in line with the range of published estimates.

Chemical space, diversity and activity landscape analysis of estrogen receptor binders.

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.

HLA-DR7 and HLA-DQ2: Transgenic mouse strains tested as a model system for ximelagatran hepatotoxicity.

The oral thrombin inhibitor ximelagatran was withdrawn in the late clinical trial phase because it adversely affected the liver. In approximately 8% of treated patients, drug-induced liver injury (DILI) was expressed as transient alanine transaminase (ALT) elevations. No evidence of DILI had been revealed in the pre-clinical in vivo studies. A whole genome scan study performed on the clinical study material identified a strong genetic association between the major histocompatibility complex alleles for human leucocyte antigens (HLA) (HLA-DR7 and HLA-DQ2) and elevated ALT levels in treated patients. An immune-mediated pathogenesis was suggested. Here, we evaluated whether HLA transgenic mice models could be used to investigate whether the expression of relevant HLA molecules was enough to reproduce the DILI effects in humans. In silico modelling performed in this study revealed association of both ximelagatran (pro-drug) and melagatran (active drug) to the antigen-presenting groove of the homology modelled HLA-DR7 molecule suggesting "altered repertoire" as a key initiating event driving development of DILI in humans. Transgenic mouse strains (tgms) expressing HLA of serotype HLA-DR7 (HLA-DRB1*0701, -DRA*0102), and HLA-DQ2 (HLA-DQB1*0202,-DQA1*0201) were created. These two lines were crossed with a human (h)CD4 transgenic line, generating the two tgms DR7xhCD4 and DQ2xhCD4. To investigate whether the DILI effects observed in humans could be reproduced in tgms, the mice were treated for 28 days with ximelagatran. Results revealed no signs of DILI when biomarkers for liver toxicity were measured and histopathology was evaluated. In the ximelagatran case, presence of relevant HLA-expression in a pre-clinical model did not fulfil the prerequisite for reproducing DILI observed in patients. Nonetheless, for the first time an HLA-transgenic mouse model has been investigated for use in HLA-associated DILI induced by a low molecular weight compound. This study shows that mimicking of genetic susceptibility, expressed as DILI-associated HLA-types in mice, is not sufficient for reproducing the complex pathogenesis leading to DILI in man.

The application of quantum mechanics in structure-based drug design.

INTRODUCTION: Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein-ligand recognition. AREAS COVERED: In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein-ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein-ligand docking, protein-ligand binding affinities and ligand strain on binding. EXPERT OPINION: Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.

Application of shape-based and pharmacophore-based in silico screens for identification of Type II protein kinase inhibitors.

The identification of new, potent and selective inhibitors of important protein kinase targets is a major goal of drug discovery. Here we analyze the crystal structures of 55 protein kinase complexes with Type II inhibitors and find they adopt a conserved twisted V-shape, with an angle of 121 ± 8° and twist of 78 ± 8°. The tightly conserved twist appears important in ensuring ligands curve around the protein backbone and towards the deep pocket. From this, we develop predictive pharmacophore- and shape-based screens to identify Type II inhibitors from a database which also contains Type I inhibitors as decoys. Both approaches exhibit a good level of discrimination for Type II molecules. The most effective pharmacophore model requires six features and three excluded volume regions. Shape-based screening using ROCS generally performs at least as well as pharmacophore approaches. There is only a moderate dependence of shape-based or pharmacophore-based screens on the underlying conformer generator (MOE, Macromodel, Omega and SPE), as well as on ligand linkage chemistry (amide and urea). Finally, we apply our approach to retrieval of Type II inhibitors from a modified version of the DUD database, containing over 104,000 compounds. We observe good enrichment, providing further evidence that the in silico screens developed here will constitute useful guides for identification of small molecule inhibitors targetting protein kinases in their inactive conformational state.