Statistical methods for in silico tools used for risk assessment and toxicology

In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.

In Vitro and In Vivo Approaches for Screening the Potential of Anti Cancer Agents: A Review

Background: Anticancer drug development is a tedious process, requiring several in vitro, in vivo, and clinical studies. To avoid chemical toxicity in animals during an experiment, it is necessary to envisage toxic doses of screened drugs in vivo at different concentrations. Several in vitro and in vivo studies have been reported to discover the management of cancer. Materials and Methods: This study has focused on bringing together a wide range of in vivo and in vitro assay methods, developed to evaluate each hallmark feature of cancer. Result: This review provides elaborated information about target-based and cell-based screening of new anticancer drugs in the molecular targeting period. This would help to incite an alteration from the preclinical screening of pragmatic compound-orientated to target-orientated drug selection. Conclusion: Selection methodologies for finding anticancer activity have importance for tumor-specific agents. In this study, advanced rationalization of the cell-based assay is explored along with broad applications of the cell-based methodologies considering other opportunities also.

Agriculture and forestry impact assessment for tephra fall hazard: fragility function development and New Zealand scenario application

Developing approaches to assess the impact of tephra fall to agricultural and forestry systems is essential for informing effective disaster risk management strategies. Fragility functions are commonly used as the vulnerability model within a loss assessment framework and represent the relationship between a given hazard intensity measure (e.g., tephra thickness) and the probability of impacts occurring. Impacts are represented here using an impact state (IS), which categorises qualitative and quantitative statements into a numeric scale. This study presents IS schemes for pastoral, horticultural, and forestry systems, and a suite of fragility functions estimating the probability of each IS occurring for 13 sub-sectors. Temporal vulnerability is accounted for by a ‘seasonality coefficient,’ and a ‘chemical toxicity coefficient’ is included to incorporate the increased vulnerability of pastoral farming systems when tephra is high in fluoride. The fragility functions are then used to demonstrate a deterministic impact assessment with current New Zealand exposure.

HiPSC-Derived Hepatocyte-like Cells Can Be Used as a Model for Transcriptomics-Based Study of Chemical Toxicity

Traditional toxicity risk assessment approaches have until recently focussed mainly on histochemical readouts for cell death. Modern toxicology methods attempt to deduce a mechanistic understanding of pathways involved in the development of toxicity, by using transcriptomics and other big data-driven methods such as high-content screening. Here, we used a recently described optimised method to differentiate human induced pluripotent stem cells (hiPSCs) to hepatocyte-like cells (HLCs), to assess their potential to classify hepatotoxic and non-hepatotoxic chemicals and their use in mechanistic toxicity studies. The iPSC-HLCs could accurately classify chemicals causing acute hepatocellular injury, and the transcriptomics data on treated HLCs obtained by TempO-Seq technology linked the cytotoxicity to cellular stress pathways, including oxidative stress and unfolded protein response (UPR). Induction of these stress pathways in response to amiodarone, diclofenac, and ibuprofen, was demonstrated to be concentration and time dependent. The transcriptomics data on diclofenac-treated HLCs were found to be more sensitive in detecting differentially expressed genes in response to treatment, as compared to existing datasets of other diclofenac-treated in vitro hepatocyte models. Hence iPSC-HLCs generated by transcription factor overexpression and in metabolically optimised medium appear suitable for chemical toxicity detection as well as mechanistic toxicity studies.

In silico-labeled ghost cytometry

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive ‘imaging’ data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

Earthworm coelomocytes as a soil health assessment tool

Different pollutants can disrupt earthworm coelomocytes integrity and functions, and their responses can be applied as biomarkers of sublethal contaminant exposure. In this context, the aim of this study was to develop an in vitro protocol for coelomocyte extraction, maintenance and analysis with regard to soil health status and chemical toxicity profile assessments. The extrusion technique was first tested comparing previously depurated (purged stomach content) and non-depurated and resampled earthworms. After testing, earthworms were exposed to different 2,4D and chloroacetamide concentrations for methodology validation. The values of viability were not affected by food restriction since no statistical difference was observed between non-depurated (sample A) and depurated (sample B) organisms. Regarding to cell density, a significant (p<0;05) reduction of 22% was observed between non-depurated and depurated organisms, indicating that food restriction may affect cell density. However, the non-depurated resampling did not show a significant reduction, indicating that this assessment may not be affect by resampling of the same organism. For both chemical compounds, no change in cell viability was observed at all assessed concentrations and exposure times. However, for cell density, a mainly time-dependent effect was observed for organisms exposed to chloroacetamide, and concentration-dependent effect for organisms exposed to 2,4D. The proportion of immune system cells was altered, mainly after 24 h, with the increasing of granular amoebocytes proportion. The difference in the proportion of granular amoebocytes in earthworms exposed to 2,4D can be explained by the existence of recognition and elimination mechanisms for this chemical substance. Thus, assessments of pollutant responses with in vitro coelomocytes seem to be a powerful tool for ecotoxicological studies.

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.

Effectiveness of a Participatory Program on Pesticide Use Behavior and Blood Cholinesterase Levels in Chiang Mai Province, Northern Thailand

This quasiexperimental study was designed to determine the effectiveness of a participatory program on pesticide use behavior and blood cholinesterase levels. The participants were 18–60 years old, living in Thung Satok subdistrict, San Pa Tong District, Chiang Mai Province. Eighty subjects, including 32 farmers and 48 consumers, were recruited to participate in the study voluntarily by a purposive sampling technique. Data concerning each individual’s behaviors were collected using questionnaires, and blood cholinesterase levels were obtained from whole blood finger, providing whole blood pre and postexperiment. The data were analyzed using Fishers’ exact and paired t-tests, for the problem situations were independently analyzed. The results show that the participatory process made it possible to improve methods for the prevention of the unhealthy effects of pesticides. After participating in the activities, both groups showed significantly higher scores in before and after knowledge ( P < 0.05 ) and a decrease in pesticide contamination in their bodies as a result of the measurement of average cholinesterase which increased significantly ( P < 0.001 ). In conclusion, the participatory program was applied to solve health problems from chemical hazards. The program can raise awareness about chemical toxicity, leading to the modification of the related behavior toward chemicals and reduction of the contamination of chemicals in the body. Therefore, the adoption of participatory processes can effectively solve problems related to chemical hazards that affect health.

Toxic Effects on Bioaccumulation, Hematological Parameters, Oxidative Stress, Immune Responses and Tissue Structure in Fish Exposed to Ammonia Nitrogen: A Review

Ammonia nitrogen is the major oxygen-consuming pollutant in aquatic environments. Exposure to ammonia nitrogen in the aquatic environment can lead to bioaccumulation in fish, and the ammonia nitrogen concentration is the main determinant of accumulation. In most aquatic environments, fish are at the top of the food chain and are most vulnerable to the toxic effects of high levels of ammonia nitrogen exposure. In fish exposed to toxicants, ammonia-induced toxicity is mainly caused by bioaccumulation in certain tissues. Ammonia nitrogen absorbed in the fish enters the circulatory system and affects hematological properties. Ammonia nitrogen also breaks balance in antioxidant capacity and causes oxidative damage. In addition, ammonia nitrogen affects the immune response and causes neurotoxicity because of the physical and chemical toxicity. Thence, the purpose of this review was to investigate various toxic effects of ammonia nitrogen, including oxidative stress, neurotoxicity and immune response.