• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Toxicological Research
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• v.25 no.1
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• pp.9-15
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• 2009

Historically, research in toxicology has utilized non-human mammalian species, particularly rats and mice, to study in vivo the effects of toxic exposure on physiology and behavior. However, ethical considerations and the overwhelming increase in the number of chemicals to be screened has led to a shift away from in vivo work. The decline in in vivo experimentation has been accompanied by an increase in alternative methods for detecting and predicting detrimental effects: in vitro experimentation and in silico modeling. Yet, these new methodologies can not replace the need for in vivo work on animal physiology and behavior. The development of new, non-mammalian model systems shows great promise in restoring our ability to use behavioral endpoints in toxicological testing. Of these systems, the zebrafish, Danio rerio, is the model organism for which we are accumulating enough knowledge in vivo, in vitro, and in silico to enable us to develop a comprehensive, high-throughput toxicology screening system.

• Molecular & Cellular Toxicology
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• v.3 no.sup4
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• pp.37.4-37.4
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• 2007

• Mass Spectrometry Letters
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• v.10 no.3
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• pp.93-101
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• 2019

Algorithms and software for predicting tandem mass spectra have been developed in recent years. In this work, we explore how distinct in silico $MS^2$ spectra are predicted for isomers, i.e. compounds having the same formula and similar molecular structures, to differentiate between them. We used the CFM-ID 2.0/3.0 predictor with regard to (a) test compounds, whose experimental mass spectra had been randomly sampled from the MassBank of North America (MoNA) collection, and to (b) the most widespread isomers of test compounds searched in the PubChem database. In the first validation test, in silico mass spectra constitute a reference library, and library searches are performed for test experimental spectra of "unknowns". The searches led to the true positive rate (TPR) of ($46-48{\pm}10$)%. In the second test, in silico and experimental spectra were interchanged and this resulted in a TPR of ($58{\pm}10$)%. There were no significant differences between results obtained with different metrics of spectral similarity and predictor versions. In a comparison of test compounds vs. their isomers, a statistically significant correlation between mass spectral data and structural features was observed. The TPR values obtained should be regarded as reasonable results for predicting tandem mass spectra of related chemical structures.

• Toxicological Research
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• v.33 no.3
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• pp.173-182
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• 2017

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

• Journal of Ginseng Research
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• v.47 no.2
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• pp.283-290
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• 2023

Hypercoagulability is frequently observed in patients with severe coronavirus disease-2019 (COVID-19). Platelets are a favorable target for effectively treating hypercoagulability in COVID-19 patients as platelet hyperactivity has also been observed. It is difficult to develop a treatment for COVID-19 that will be effective against all variants and the use of antivirals may not be fully effective against COVID-19 as activated platelets have been detected in patients with COVID-19. Therefore, patients with less severe side effects often turn toward natural remedies. Numerous phytochemicals are being investigated for their potential to treat a variety of illnesses, including cancer and bacterial and viral infections. Natural products have been used to alleviate COVID-19 symptoms. Panax ginseng has potential for managing cardiovascular diseases and could be a treatment for COVID-19 by targeting the coagulation cascade and platelet activation. Using molecular docking, we analyzed the interactions of bioactive chemicals in P. ginseng with important proteins and receptors involved in platelet activation. Furthermore, the SwissADME online tool was used to calculate the pharmacokinetics and drug-likeness properties of the lead compounds of P. ginseng. Dianthramine, deoxyharrtingtonine, and suchilactone were determined to have favorable pharmacokinetic profiles.

• Toxicological Research
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• v.32 no.1
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• pp.15-20
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• 2016

Physiologically based pharmacokinetic (PBPK) modeling can provide an effective way to utilize in vitro and in silico based information in modern risk assessment for children and other potentially sensitive populations. In this review, we describe the process of in vitro to in vivo extrapolation (IVIVE) to develop PBPK models for a chemical in different ages in order to predict the target tissue exposure at the age of concern in humans. We present our on-going studies on pyrethroids as a proof of concept to guide the readers through the IVIVE steps using the metabolism data collected either from age-specific liver donors or expressed enzymes in conjunction with enzyme ontogeny information to provide age-appropriate metabolism parameters in the PBPK model in the rat and human, respectively. The approach we present here is readily applicable to not just to other pyrethroids, but also to other environmental chemicals and drugs. Establishment of an in vitro and in silico-based evaluation strategy in conjunction with relevant exposure information in humans is of great importance in risk assessment for potentially vulnerable populations like early ages where the necessary information for decision making is limited.

• Journal of Microbiology and Biotechnology
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• v.26 no.12
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• pp.2030-2035
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• 2016

Bacillus cereus causes food-borne illness through contaminated foods; therefore, its pathogenicity and genome sequences have been analyzed in several studies. We sequenced and analyzed B. cereus strain FORC_021 isolated from a sashimi restaurant. The genome sequence consists of 5,373,294 bp with 35.36% GC contents, 5,350 predicted CDSs, 42 rRNA genes, and 107 tRNA genes. Based on in silico DNA-DNA hybridization values, B. cereus ATCC $14579^T$ was closest to FORC_021 among the complete genome-sequenced strains. Three major enterotoxins were detected in FORC_021. Comparative genomic analysis of FORC_021 with ATCC $14579^T$ revealed that FORC_021 harbored an additional genomic region encoding virulence factors, such as putative ADP-ribosylating toxin, spore germination protein, internalin, and sortase. Furthermore, in vitro cytotoxicity testing showed that FORC_021 exhibited a high level of cytotoxicity toward INT-407 human epithelial cells. This genomic information of FORC_021 will help us to understand its pathogenesis and assist in managing food contamination.

• Toxicological Research
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• v.35 no.3
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• pp.257-270
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• 2019

Silver nanoparticles (AgNPs) have been widely used in a variety of applications in innovative development; consequently, people are more exposed to this particle. Growing concern about toxicity from AgNP exposure has attracted greater attention, while questions about nanosilver-responsive genes and consequences for human health remain unanswered. By considering early detection and prevention of nanotoxicology at the genetic level, this study aimed to identify 1) changes in gene expression levels that could be potential indicators for AgNP toxicity and 2) morphological phenotypes correlating to toxicity of HepG2 cells. To detect possible nanosilver-responsive genes in xenogenic targeted organs, a comprehensive systematic literature review of changes in gene expression in HepG2 cells after AgNP exposure and in silico method, connection up- and down-regulation expression analysis of microarrays (CU-DREAM), were performed. In addition, cells were extracted and processed for transmission electron microscopy to examine ultrastructural alterations. From the Gene Expression Omnibus (GEO) Series database, we selected genes that were up- and down-regulated in AgNPs, but not up- and down-regulated in silver ion exposed cells, as nanosilver-responsive genes. HepG2 cells in the AgNP-treated group showed distinct ultrastructural alterations. Our results suggested potential representative gene data after AgNPs exposure provide insight into assessment and prediction of toxicity from nanosilver exposure.

• Journal of Life Science
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• v.33 no.6
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• pp.490-497
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• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.