Thousands Of Chemicals Research Articles

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

The chemical landscape of high-throughput new approach methodologies for exposure.

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Environmental chemical risk factors of breast cancer in Nigeria I: Broad overview

Nigeria is currently experiencing a most disturbing level of incidence rate, morbidity and mortality from the ravages of female breast cancer, with its attendant burden of emotional, social and financial consequences. Tens of thousands of chemicals make their way into our everyday products and environment, with little regard for safety. The need to explore the impact of adverse environmental chemical exposures to breast health in Nigeria informed this study as there is need for public health approach to long term prevention. After exploring the robust scientific database on environmental chemical risk factors for female breast cancer, we found that many mammary toxicants are ubiquitous in our environment in Nigeria but have been taken for granted as inevitable in normal life exposures. This study identifies some of the potential environmental chemical risk factors of breast cancer in Nigeria. We describe the special exposure scenarios in Nigeria and prescribe some modest but practical ways to mitigate exposures. The factors that constitute risk to breast cancer could potentially be modified either through community education or government action and regulation. The subject is discussed in multiple Parts before releasing the manuscripts for publication to prevent criminalizing only a few chemicals if discussed and published to the exclusion of others. To our knowledge, this is the first ever comprehensive work on the subject in the African continent and is useful to typical developing countries as are found in sub-Saharan countries and elsewhere since findings and recommendations are usually generic in nature and mutually beneficial.

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA’s ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA’s ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r 2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.

Why Indoor Chemistry Matters: A National Academies Consensus Study

BACKGROUND AND AIM The indoor environment is a significant contributor to human chemical exposures and health risks, and indoor chemistry plays an important role in driving and modifying exposure to many indoor pollutants. Despite recent developments, much of the chemical complexity and variability of indoor environments remains poorly understood. METHODS The National Academies of Sciences, Engineering, and Medicine (the National Academies) convened an ad hoc committee of 16 experts to conduct a consensus study examining the state of the science regarding chemicals in non-industrial indoor environments. RESULTS This presentation will summarize findings and emerging discoveries related to indoor chemistry and sources, chemical partitioning and reactions, and links between chemical exposure, air quality, and human health. Briefly, thousands of chemicals are found indoors in air, particles, dust, and surfaces, and they partition across phases. Humans can modify indoor chemistry and act as primary sources. Indoor chemicals undergo oxidation, photolysis, hydrolysis, acid-base, and other reactions and can form reaction products with differing properties. Improved knowledge of molecular reaction rates, kinetics, emission rates, building and environmental factors, and human behaviors and time activity patterns along with their uncertainties are needed to refine exposure and health risk models. This presentation will also summarize recommendations regarding: 1) key implications of recent scientific research, including potential near-term opportunities for translating research findings into practice, and 2) opportunities for new scientific research in several priority areas critical to achieving a better understanding of the chemistry of the indoor environment and resulting adverse exposures and health risks. CONCLUSIONS The committee noted opportunities for advancing this field of study by enhancing interdisciplinary collaboration and addressing methodological or technological barriers to research. This scientific foundation will further our understanding of indoor chemistry and its impacts on health and the environment and support regulatory action and guidelines.

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors – primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

Insights into the Use of Eco-Friendly Synergists in Resistance Management of Leptinotarsa decemlineata (Coleoptera: Chrysomelidae).

Simple SummaryThe discovery of insecticides requires time and has a high investment cost. However, injudicious use of insecticides has resulted in insect pest resistance and pest resurgence. These factors limit the market life of insecticides. To cope with this problem, synergists working as blockers of detoxification enzymes can provide a unique solution in resistance management. Owing to problems associated with chemical synergists, plant-based and RNAi-based synergists are much safer and easier to develop against insects. In fact, the Colorado potato beetle (CPB) has a unique detoxification mechanism due to its co-evolution with Solanaceous plants. This review is about how synergists can be used to deal with the resistance management of the CPB and will be helpful for researchers devising unique pest management strategies for other insect pests.The Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say), is the most notorious insect pest of potato globally. Injudicious use of insecticides for management of this pest has resulted in resistance to all major groups of insecticides along with many human, animal health, and environmental concerns. Additionally, the input cost of insecticide development/discovery is markedly increasing because each year thousands of chemicals are produced and tested for their insecticidal properties, requiring billions of dollars. For the management of resistance in insect pests, synergists can play a pivotal role by reducing the application dose of most insecticides. These eco-friendly synergists can be classified into two types: plant-based synergists and RNAi-based synergists. The use of plant-based and RNAi-based synergists in resistance management of insect pests can give promising results with lesser environmental side effects. This review summarizes the resistance status of CPB and discusses the potential advantage of plant-based and RNAi-based synergists for CPB resistance management. It will motivate researchers to further investigate the techniques of using plant- and RNAi-based synergists in combination with insecticides.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods—even if high throughput—are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful—experimentally verified—associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Circular economy could expose children to hazardous phthalates and chlorinated paraffins via old toys and childcare articles

The European waste framework directive encourages reuse, refurbishment and recycling of products and materials in order to reduce plastic waste. However, thousands of chemicals are used in plastic materials. Many of these are potentially toxic, and may cause hormonal and developmental disruption in children. This includes phthalates and short chain chlorinated paraffins, which are used as plasticizers and flame-retardants. European legal frameworks regulate the amounts of these substances in toys in an effort to protect children's health and safety. Currently, limits are set to 0.1% for phthalates and 0.15% for SCCPs. Here, we have investigated levels of these compounds in toys and childcare products that were purchased prior to and after legislation on stricter exposure levels was implemented (total of 157 items, 54 and 103 new and old, respectively). We found that a larger portion of older toys and items (83.5%) contained amounts that exceed legal limits, compared to newer toys and items (29.6%). Concentrations of DEHP, BBP, DIDP, and SCCPs were significantly higher in old items, and both DEHP and DINP were found at concentrations exceeding 400,000 mg/kg in several old balls, which is approximately 40% of the weight of the toy, and 400 times above the legal limit. These findings indicate that old toys have the potential to pose a greater risk to children, and that regulations can be useful tools to protect children from exposure to toxic chemicals. We also stress that the waste framework directive, which urges reuse and repurposing of objects such as second hand items used for dress-up play, can lead to continued exposure via chemicals in older items. We conclude that movement towards circular economy threatens to expose children from legacy compounds already restricted on the market if efforts are not made to remove these items from circulation.

Metabolomics application for the design of an optimal diet.

Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Rethinking our chemical legacy and reclaiming our planet

Rethinking our chemical legacy and reclaiming our planet

In vivo neurophysiological assessment of in silico predictions of neurotoxicity: Citronellal, 3,4-dichloro-1-butene, and benzyl bromoacetate

Neurotoxicants may be widespread in the environment and can produce serious health impacts in the human population. Screening programs that use in vitro methods have generated data for thousands of chemicals. However, these methods often do not evaluate repeated or prolonged exposures, which are required for many neurotoxic outcomes. Additionally, the data produced by such screening methods may not include mechanisms which play critical biological roles necessary for in vivo neurotoxicity. The Hard and Soft Acids and Bases (HSAB) in silico model focuses on chemical structure and electrophilic properties which are important to the formation of protein adducts. A group of structurally diverse chemicals have been evaluated with an in silico screening approach incorporating HSAB parameters. However, the predictions from the expanded chemical space have not been evaluated using in vivo methods. Three chemicals predicted to be cumulative toxicants were selected for in vivo neurotoxicological testing. Adult male Long-Evans rats were treated orally with citronellal (CIT), 3,4-dichloro-1-butene (DCB), or benzyl bromoacetate (BBA) for 8 weeks. Behavioral observations were recorded weekly to assess motor function. Peripheral neurophysiological measurements were derived from nerve excitability (NE) tests which involved compound muscle action potentials (CMAPs) in the tail and foot, and mixed nerve action potentials (MNAPs) in the tail. Compound nerve action potentials (CNAPs) and nerve conduction velocity (NCV) in the tail were also quantified. Peripheral inputs into the central nervous system were examined using somatosensory evoked potentials recorded from the cortex (SEPCTX) and cerebellum (SEPCEREB). CIT or BBA did not result in significant alterations to peripheral nerve or somatosensory function. DCB reduced grip-strength and altered peripheral nerve function. The MNAPs required less current to reach 50% amplitude and had a lower calculated rheobase, suggesting increased excitability. Increased CNAP amplitudes and greater NCV were also observed. Novel changes were found in the SEPCTX with an abnormal peak forming in the early portion of the waveforms of treated rats, and decreased latencies and increased amplitudes were observed in SEPCEREB recordings. These data contribute to testing an expanded chemical space from an in silico HSAB model for predicting cumulative neurotoxicity and may assist with prioritizing chemicals to protect human health.

Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening.

Studies in in vivo rodent models have been the accepted approach by regulatory agencies to evaluate potential developmental neurotoxicity (DNT) of chemicals for decades. These studies, however, are inefficient and cannot meet the demand for the thousands of chemicals that need to be assessed for DNT hazard. As such, several in vitro new approach methods (NAMs) have been developed to circumvent limitations of these traditional studies. The DNT NAMs, some of which utilize human-derived cell models, are intended to be employed in a testing battery approach, each focused on a specific neurodevelopmental process. The need for multiple assays, however, to evaluate each process can prolong testing and prioritization of chemicals for more in depth assessments. Therefore, a multi-endpoint higher-throughput approach to assess DNT hazard potential would be of value. Accordingly, we have adapted a high-throughput phenotypic profiling (HTPP) approach for use with human-derived neural progenitor (hNP1) cells. HTPP is a fluorescence-based assay that quantitatively measures alterations in cellular morphology. This approach, however, required optimization of several laboratory procedures prior to chemical screening. First, we had to determine an appropriate cell plating density in 384-well plates. We then had to identify the minimum laminin concentration required for optimal cell growth and attachment. And finally, we had to evaluate whether addition of antibiotics to the culture medium would alter cellular morphology. We selected 6,000 cells/well as an appropriate plating density, 20 µg/ml laminin for optimal cell growth and attachment, and antibiotic addition in the culture medium. After optimizing hNP1 cell culture conditions for HTPP, it was then necessary to select appropriate in-plate assay controls from a reference chemical set. These reference chemicals were previously demonstrated to elicit unique phenotypic profiles in various other cell types. Aphidicolin, bafilomycin A1, berberine chloride, and cucurbitacin I induced robust phenotypic profiles as compared to dimethyl sulfoxide vehicle control in the hNP1 cells, and thus can be employed as in-plate assay controls for subsequent chemical screens. We have optimized HTPP for hNP1 cells, and consequently this approach can now be assessed as a potential NAM for DNT hazard evaluation and results compared to previously developed DNT assays.

PFAS Analysis with Ultrahigh Resolution 21T FT-ICR MS: Suspect and Nontargeted Screening with Unrivaled Mass Resolving Power and Accuracy.

Per- and polyfluoroalkyl substances (PFASs) are a large family of thousands of chemicals, many of which have been identified using nontargeted time-of-flight and Orbitrap mass spectrometry methods. Comprehensive characterization of complex PFAS mixtures is critical to assess their environmental transport, transformation, exposure, and uptake. Because 21 tesla (T) Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest available mass resolving power and sub-ppm mass errors across a wide molecular weight range, we developed a nontargeted 21 T FT-ICR MS method to screen for PFASs in an aqueous film-forming foam (AFFF) using suspect screening, a targeted formula database (C, H, Cl, F, N, O, P, S; ≤865 Da), isotopologues, and Kendrick-analogous mass difference networks (KAMDNs). False-positive PFAS identifications in a natural organic matter (NOM) sample, which served as the negative control, suggested that a minimum length of 3 should be imposed when annotating CF2-homologous series with positive mass defects. We putatively identified 163 known PFASs during suspect screening, as well as 134 novel PFASs during nontargeted screening, including a suspected polyethoxylated perfluoroalkane sulfonamide series. This study shows that 21 T FT-ICR MS analysis can provide unique insights into complex PFAS composition and expand our understanding of PFAS chemistries in impacted matrices.

High-Throughput Screening Assay Profiling for Large Chemical Databases.

High-throughput screening (HTS) techniques are increasingly being adopted by a variety of fields of toxicology. Notably, large-scale research efforts from government, industrial, and academic laboratories are screening millions of chemicals against a variety of biomolecular targets, producing an enormous amount of publicly available HTS assay data. These HTS assay data provide toxicologists important information on how chemicals interact with different biomolecular targets and provide illustrations of potential toxicity mechanisms. Open public data repositories, such as the National Institutes of Health's PubChem ( http://pubchem.ncbi.nlm.nih.gov ), were established to accept, store, and share HTS data. Through the PubChem website, users can rapidly obtain the PubChem assay results for compounds by using different chemical identifiers (including SMILES, InChIKey, IUPAC names, etc.). However, obtaining these data in a user-friendly format suitable for modeling and other informatics analysis (e.g., gathering PubChem data for hundreds or thousands of chemicals in a modeling friendly format) directly through the PubChem web portal is not feasible. This chapter aims to introduce two approaches to obtain the HTS assay results for large datasets of compounds from the PubChem portal. First, programmatic access via PubChem's PUG-REST web service using the Python programming language will be described. Second, most users, who lack programming skills, can directly obtain PubChem data for a large set of compounds by using the freely available Chemical In vitro-In vivo Profiling (CIIPro) portal ( http://www.ciipro.rutgers.edu ).

Assessment of health risks from environmental factors. 16 Years of collaboration between the United States Environmental Protection Agency (US EPA), hygiene and environmental organizations in the Russian Federation: results and reflections

Environmental health risk assessment has a long history in both the United States and Russia. Risk assessment methods have arisen and developed to protect against the dangers of the forced use of tens of thousands of chemicals in the modern world. Because chemicals play such an important role, it is impossible to ban their use altogether, and the task of their safe use becomes more and more critical. With the solution of this problem, the creation of modern environmental legislation began, based on an assessment of the hazard or potential hazard associated with the impact of these substances on humans. Risk assessment has become the scientific basis for calculating exposure limits for many chemicals that pollute the environment. Since compliance with environmental legislation is costly to industry, methods have been developed. They are being improved on to quantify the burden of proof and the appropriateness of environmental quality regulation. Environmental Health Risk Assessment was first developed by the United States Environmental Protection Agency (US EPA). Russia has accumulated a great deal of practical experience in assessing environmental risks and developing appropriate methodological recommendations. Thus, the basis was created for long-term cooperation between the two countries. This article summarizes the history of such collaboration, including a joint project to disseminate practical risk assessment in Ukraine.

Integrated Omic Analyses Identify Pathways and Transcriptomic Regulators Associated With Chemical Alterations of In Vitro Neural Network Formation.

Development of in vitro new approach methodologies has been driven by the need for developmental neurotoxicity (DNT) hazard data on thousands of chemicals. The network formation assay characterizes DNT hazard based on changes in network formation but provides no mechanistic information. This study investigated nervous system signaling pathways and upstream physiological regulators underlying chemically induced neural network dysfunction. Rat primary cortical neural networks grown on microelectrode arrays were exposed for 12 days in vitro to cytosine arabinoside, 5-fluorouracil, domoic acid, cypermethrin, deltamethrin, or haloperidol as these exposures altered network formation in previous studies. RNA-seq from cells and gas chromatography/mass spectrometry analysis of media extracts collected on days in vitro 12 provided gene expression and metabolomic identification, respectively. The integration of differentially expressed genes and metabolites for each neurotoxicant was analyzed using ingenuity pathway analysis. All 6 compounds altered gene expression that linked to developmental disorders and neurological diseases. Other enriched canonical pathways overlapped among compounds of the same class; eg, genes and metabolites altered by both cytosine arabinoside and 5-fluorouracil exposures are enriched in axonal guidance pathways. Integrated analysis of upstream regulators was heterogeneous across compounds, but identified several transcriptomic regulators including CREB1, SOX2, NOTCH1, and PRODH. These results demonstrate that changes in network formation are accompanied by transcriptomic and metabolomic changes and that different classes of compounds produce differing responses. This approach can enhance information obtained from new approach methodologies and contribute to the identification and development of adverse outcome pathways associated with DNT.

Ecotoxicological risk ranking of 19 metals in the lower Yangtze River of China based on their threats to aquatic wildlife

With thousands of chemicals discharged into the aquatic environment, it is necessary to identify those that are likely to be having the greatest impact on wildlife to better protect the ecosystem. A risk ranking approach was developed to compare the ecotoxicological risk of chemicals on aquatic wildlife with a wide range of environmental measurement data and ecotoxicity data. Nineteen metals including some rarely monitored ones including antimony (Sb), molybdenum (Mo), cobalt (Co), vanadium (V), titanium (Ti) and thallium (Tl) in the lower Yangtze River were risk ranked as a case study. The risk ranking approach was conducted in three tiers: general risk ranking, lethal effects vs. non-lethal effects risk ranking, and species group-specific risk ranking. Iron, copper and titanium were identified as being of greatest concern. The contamination of iron, zinc, copper and nickel was widespread and may have already harmed wildlife according to the overlap between ecotoxicity and monitored levels. Based on this analysis, the risk from copper and some rarely monitored metals (titanium and boron) may have been underestimated. Greater efforts to reduce copper, iron and titanium contamination could make an important difference to the health of Chinese freshwater organisms in the Yangtze River.

Medicinal Plants Explain the Significant Role of Uric Acid for Malaria Parasite

Medicinal plants, recognized and employed in conventional medicine practices since prehistoric era. Plants produce thousands of chemical substances for functions counting defence against insects, fungi, bacterial and parasitic diseases.
 Malaria is most widespread parasitic infection , it caused by coccidian protozoa of the genus plasmodium , four species are mostly infect human, P. falciparum, P. vivax, P. malriae and P. ovale, Majority of malaria cases resulted from P. falciparum and P. vivax.
 Uric acid regarded as one of the damaging molecular patterns of malaria parasite infection, and in this review we discussed the potential role of medicinal plants used as antimalarial to diminish the level of uric acid in gout patients. These may suggest that most of the complication associated with malaria, may attributed to amplified level of uric acid . Experimental studies recommended.