• 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.

• Herbal Formula Science
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• v.30 no.3
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• pp.205-210
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• 2022

Conventional pharmacology has followed the notion of the reductionist 'single target selective drug paradigm'. Network pharmacology has made conventional pharmacology newer while meeting the challenges of this era. Conventional pharmacological methods have not solved the problems of Korean Medicine. For this reason, Network pharmaco- logy needs urgently and desperately for Korean medicine research. However, the information on drug interactions in herbal medicines is complex and less known. There are still some hurdles before network pharmacology emerges, one factor which constitutes Korean medicine research. There is a need to look for solutions other than inheriting the network pharmacology to solve problems that Korean medicine has before. The way of 'in silico' research should be the best to meet this challenge. With the help of 'in silico' research, there might have been emerged new findings of experimental data in Korean Medicine. If 'herbalomics' has been close to foundation through the 'in silico' method, it will contribute to the formation of modern Korean medicine and, simultaneously, come to a foundation for revitalizing exchanges with orthodox Western medicine. Eventually, it ends with a significant profitable and healthy result for the patients.

• 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.

• BMB Reports
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• v.45 no.12
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• pp.736-741
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• 2012

Certain members of the cytochromes P450 superfamily metabolize polyunsaturated long-chain fatty acids to several classes of oxygenated metabolites. An approach based on in silico analysis predicted that Streptomyces peucetius CYP107N3 might be a fatty acid-metabolizing enzyme, showing high homology with epoxidase enzymes. Homology modeling and docking studies of CYP107N3 showed that oleic acid can fit directly into the active site pocket of the double bond of oleic acid within optimum distance of $4.6{\AA}$ from the Fe. In order to confirm the epoxidation activity proposed by in silico analysis, a gene coding CYP107N3 was expressed in Escherichia coli. The purified CYP107N3 was shown to catalyze $C_9-C_{10}$ epoxidation of oleic acid in vitro to 9,10-epoxy stearic acid confirmed by ESI-MS, HPLC-MS and GC-MS spectral analysis.

• Bulletin of the Korean Chemical Society
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• v.28 no.3
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• pp.379-385
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• 2007

Receptor-oriented pharmacophore-based in silico screening is a powerful tool for rapidly screening large number of compounds for interactions with a given protein. Inhibition of the enzyme catechol-Omethyltransferase (COMT) offers a novel possibility for treating Parkinson's disease. Bisubstrate inhibitors of COMT containing the adenine of S-adenosylmethionine (SAM) and a catechol moiety are a new class of potent and selective inhibitor. In the present study, we used receptor-oriented pharmacophore-based in silico screening to examine the interactions between the active site of human COMT and bisubstrate inhibitors. We generated 20 pharmacophore maps, of which 4 maps reproduced the docking model of hCOMT and a bisubstrate inhibitor. Only one of these four, pharmacophore map I, effectively described the common features of a series of bisubstrate inhibitors. Pharmacophore map I consisted of one hydrogen bond acceptor (to Mg2+), three hydrogen bond donors (to Glu199, Glu90, and Gln120), and one hydrophobic feature (an active site region surrounded by several aromatic and hydrophobic residues). This map represented the most essential pharmacophore for explaining interactions between hCOMT and a bisubstrate inhibitor. These results revealed a pharmacophore that should help in the development of new drugs for treating Parkinson's disease.

• 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 the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.483-492
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• 2021

Agastachis Herba (AH) to treat anorexia and nausea and its antidiabetic efficacy was recently reported. This study examined the antioxidant activities and chemical constituents of AH and predicted the target proteins of each compound using in silico approaches. The results showed that EC50 values of AH methanol extract for DPPH and ABTS radical scavenging were 78.6 ㎍/mL and 31.0 ㎍/mL, respectively. Compared to the EC50 values of ascorbic acid (9.9 ㎍/mL, 5.2 ㎍/mL), the AH methanol extract possessed excellent antioxidant activities. Rosmarinic acid, tilianin, agastachoside, and acetin were confirmed as the major compounds of extracts by qualitative analysis performed with HPLC-PDA-MS/MS. The antidiabetic target proteins of these compounds were predicted by applying a structural similarity and inverse docking methodology using a DIA-DB server. The resulting target proteins were PPAR-γ, DPP IV, glucokinase, α-glucosidase, SGLT2, aldose reductase, and corticosteroid 11-beta-dehydrogenase, some of which have already been proven experimentally as target proteins. Therefore, the in silico methods can be considered valid. Finally, AH were extracted with various solvents to determine the optimal conditions for the extraction of active components. Methanol among organic solvents and 80% ethanol in ethanol-water mixtures were identified as the most effective solvent for the extraction.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2291-2295
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• 2016

Small molecule tyrosine kinase inhibitors targeting HER 2 receptors have emerged as an important therapeutic approach in inhibition of downstream proliferation and survival signals for the treatment of breast cancers. Recent drug discovery efforts have demonstrated that naturally occurring polyphenolic compounds like delphinidin have potential to inhibit proliferation and promote apoptosis of breast cancer cells by targeting HER2 receptors. While delphinidin may thus reduce tumour size, it is associated with serious side effects like dysphonia. Owing to the narrow therapeutic window of delphinidin, the present study aimed to identify high affinity compounds targeting HER2 with safer pharmacological profiles than delphinidin through virtual screening approaches. Delphinidin served as the query parent for identification of structurally similar compounds by Tanimoto-based similarity searching with a threshold of 95% against the PubChem database. The compounds retrieved were further subjected to Lipinski and Verber's filters to obtain drug like agents, then further filtered by diversity based screens with a cut off of 0.6. The compound with Pubchem ID: 91596862 was identified to have higher affinity than its parent. In addition it also proved to be non-toxic with a better ADMET profile and higher kinase activity. The compound identified in the study can be put to further in vitro drug testing to complement the present study.

• Journal of Microbiology and Biotechnology
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• v.13 no.4
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• pp.571-577
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• 2003

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellulal reactions along with mass balances around the intracellular metabolites. In this study, metabolic flux analyses were carried out to estimate flux distributions for the maximum in silico yields of various metabolites in Escherichia coli. The maximum in silico yields of acetic acid and lactic acid were identical to their theoretical yields. On the other hand, the in silico yields of succinic acid and ethanol were only 83% and 6.5% of their theoretical yields, respectively. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power. but this lower yield could be increased to its theoretical yield by supplying more reducing power. The maximum theoretical yield of ethanol could be achieved, when a reaction catalyzed by pyruvate decarboxylase was added in the metabolic network. Futhermore, optimal metabolic pathways for the production of various metabolites could be proposed, based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that the pyruvate carboxylation pathway should be used for its optimal production in E. coli rather than the phosphoenolpyruvate carboxylation pathway.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5005-5006
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• 2015

Gastric cancer as one of the most common cancers worldwide has various genetic and environmental risk factors including Helicobacter pylori (H.pylori) infection. Recently, loss of a tumor suppressor gene named promyelocytic leukemia (PML) has been identified in gastric cancer. However, no mutation has been found in this gene in gastric cancer samples. Cag A H.pylori protein has been shown to exert post transcriptional regulation of some tumor suppressor genes. In order to assess such a mechanism for PML degradation, we performed in silico analyses to establish any interaction between PML and Cag A proteins. In silico interaction and docking studies showed that these two proteins may have stable interactions. In addition, we showed that imatinib kinase inhibitor can restore PML function by inhibition of casein kinase 2.