It is challenging to treat canine brucellosis due to the immune evading and stealthy characteristic of the causative bacteria, Brucella (B.) canis. Gold nanoparticle aptamer (AuNP-Apt) conjugated antimicrobial peptide (AMP) is a promising alternative to antibiotics for various bacterial infections. However, the toxicity of AuNP-Apt has been variable throughout research, and the in vivo toxic mechanism has not been fully elucidated. This study evaluated the therapeutic potential against B. canis, and the toxicity of AuNP-Apt conjugated antimicrobial peptide, RW-BP100 (AuNP-AptHis-RW-BP100His), in a mouse model. Intravenous (IV) treatment with AuNP-AptHis-RW-BP100His reduced the bacteria burden and histopathologic lesions. The IV treatment also induced CD4+ T cell differentiation and modulated serum cytokine levels. However, high-dose AuNP-Apt was lethal, resulting in tissue accumulation and vessel embolism. Therefore, AuNP-AptHis-RW-BP100His is a promising therapeutic agent for B. canis treatment, but due to its toxicity, further studies are needed for its utilization in clinical practice.

This study evaluated the sub-chronic oral toxicity of freeze-dried deer velvet antler (DVA) extract in ICR mice over a 90-day period. The extract was administered orally to male and female mice at doses of 1,000, 2,000, and 5,000 mg/kg body weight daily; the control group (0 mg/kg body weight) received vehicle only. No clinical signs of toxicity or mortality were observed. Body weight gain, food consumption, and general behavior remained normal across all groups. Hematological analysis revealed no dose-dependent alterations in hemoglobin levels, platelet characteristics, or red or white blood cell counts. Males administered 2,000 mg/kg DVA exhibited a slight increase in reticulocyte count, whereas females exhibited reduced count at 5,000 mg/kg; however, neither finding was doseresponsive or toxicologically significant. Serum biochemistry including liver and kidney function markers (ALT, AST, -GT, BUN, and creatinine) remained within normal range, and no evidence of organ damage was observed. Notably, male mice exhibited a significant reduction in serum triglycerides compared with those in the control group, suggesting potential lipid-modulating effects of DVA; however, this finding requires further investigation. Based on these findings, the no-observed-adverse-effect level (NOAEL) of DVA extract was determined to be greater than 5,000 mg/kg/day in both sexes. These results support the safety profile of DVA extract at the tested doses, and its potential metabolic benefits.

This study developed a QSAR regression model using the XGBoost machine learning algorithm to predict the acute aquatic toxicity of highly hazardous PCBs. EC50 values for Daphnia magna were obtained from QSAR Toolbox 4.7. Input features consisted of approximately 3,000 molecular descriptors and fingerprints generated from official structure data using RDKit and the Morgan algorithm, excluding mixtures. The dataset was split into training and test sets (7 : 3) based on 500,000 randomized seeds, and the most balanced combination was selected using Kolmogorov-Smirnov and Wilcoxon rank-sum tests. Z-score standardization was applied based on the training set, and the XGBoost model was trained using 5-fold cross-validation with grid search optimization. The final model showed excellent predictive performance (R2 =0.97, RMSE= 0.19). A simplified model using only the top 10 predictive molecular features retained approximately 95% of the original accuracy while improving interpretability and efficiency. The model was applied to 38 PCB compounds lacking EC50 values, and the predicted values showed a statistically similar distribution to the measured group, with only minor differences in a few structural fingerprints. These results demonstrate the applicability of XGBoost-based models for reliable toxicity prediction and offer a promising alternative approach for assessing the environmental risk of untested PCBs.

This study aimed to develop a model for accurately predicting the acute aquatic toxicity (48h- EC50) of chlorine disinfection by-products (DBPs). DBPs have caused environmental risks, but experimental toxicity data are difficult to obtain due to time, cost, and ethical constraints. Therefore, a deep learning model was developed using actual concentration-based data. Toxicity data for 139 aliphatic chlorinated compounds were from the OECD QSAR Toolbox and from aquatic toxicity test results provided by the japan ministry of the environment. Various concentration criteria, including nominal and measured concentrations, were encoded as additional inputs, and EC50 values were augmented via log transformation and structural string modifications to overcome small data limitations. The directed message passing neural network (D-MPNN) model, which considers bond directionality, was applied to reflect structural complexity accurately. Also, this model effectively reflected subtle structural differences and showed stable performance even with limited data. Comparisons between models with and without concentration criteria revealed that the model considering all concentration criteria had superior predictive accuracy. This result shows that concentration criteria are a critical factor in toxicity prediction. This study suggests a baseline model that works reliably even with small datasets reflecting realistic concentration criteria, showing its potential use for replacing some experiments and for screening toxic substances.

In this study, chemicals with acute toxicity experimental data were selected as research subjects, and compareed the model derived from statistical analysis and QSAR open-source programs. The physical and chemical properties, dynamic behaviors, and toxicological estimates of the chemicals were calculated using Mordred, a molecular descriptor calculation program based on RDKit. LD50 was set as the toxicity comparison target for each chemical, and independent variables or factors with similarity to independent variables were estimated from the molecular descriptors calculated through Mordred. Molecule descriptors composed of independent variables were compared to predictions from QSAR open-source models, A regression model was created with the selected molecule descriptors and compared with predictions from QSAR programs, confirming high accuracy for specific functional groups. The QSAR model created in this study considers the characteristics and experimental values of each chemical, and provides evidence for selecting variables when constructing toxicity data for machine learning applications.

인삼양영탕은 한국·중국·일본의 전통의약권에서 허약 개 선에 복용되는 14종 한약재로 구성된 의약품이다. 먼저 고 성능 액체 크로마토그래피(HPLC)를 적용하여 인삼양영탕 주요 물질을 규명하였다. 식품의약품안전처 단회투여독성 시험기준에 따라 Sprague-Dawley 랫드 암수 각 5마리에게 0, 1,000, 2,000, 5,000 mg/kg 용량으로 단회 경구 투여하 였다. 시험 결과 사망한 랫드는 없었으며 일반 증상 및 부 검소견에서도 어떠한 이상 반응도 관찰되지 않아, 랫드에 서 인삼양영탕 반수 치사량(LD50)은 5,000 mg/kg 이상으로 판단되었다. 문헌조사를 통해 조사된 인삼양영탕 일반독 성시험 5건(일본 2건, 중국 2건, 한국 1건)에 따르면 개략 치사량(ALD)은 160,000 mg/kg 초과, LD50는 10,000 mg/kg 초과, 최대내성용량(MTD)은 160,000 mg/kg 초과한다고 보 고하였다. 랫드 무독성량 5,000 mg/kg을 기준으로 인체 등 가 용량 변환하면 인삼양영탕 최대안전권장복용량(MRSD) 은 4.1 g 또는 4.8 g이며, 한의서 기준 인삼양영탕 성인 1 일 복용량 28.3 g을 랫드 독성시험 투여 용량으로 변환하 면 34,750 mg/kg 또는 29,100 mg/kg이다. 인삼양영탕 안전 성 평가를 위하여 동물과 인체 종간 등가 용량을 계산하 여 보니, 동물 독성시험 투여 용량과 실제 환자 복용 용 량 간에 유의미한 상관관계가 없음을 시사한다. 학계에서 는 전통적으로 확립된 한약처방 복용량에 대한 동물 독성 시험 평가시에 ICH 및 OECD 기준이외에 다각적인 투여 용량 검토가 필요하다고 사료된다.

The purpose of this study, when predicting acute oral toxicity using QSAR software, the reliability of the predicted values was studied according to a single functional group or multiple functional groups within a single chemical. Acute oral toxicity is predicted using EPA T.E.S.T S/W for chemicals registered in ChemIDplus. The effect of a combination of specific functional periods on the degree of consistency of predicted values was studied. When some specific functional groups (combinations) exist, it was confirmed that the experimental and predicted values were high and low. It was confirmed that the prediction accuracy was high when the Anion group and the Halogen group were together, and the perdiction accuracy was significantly low when the Nitrile group was present. As a result of accumulating such data and showing reliability in predicting acute oral toxicity with EPA T.E.S.T S/W for 10 SVHC substances without experimental values, the matching rate was derived from at least 0% to 73.33%. It was confirmed that there was some tendency of the QSAR prediction value according to the combination of specific complex functional groups. When 10 SVHC substances without experimental data were predicted to be toxic through T.E.S.T S/W by quantitatively databaseizing the above tendency, 0~73.33% of the results were derived as a result of showing the realiability of the program prediction

Baicalin, a flavonoid isolated from Scutellaria baicalensis, has anti-inflammatory, antioxidant, and neuroprotective effects. Glutamate is a major neurotransmitter that plays an important role in brain function, but excessive release of glutamate causes excitotoxicity and damages cells. We investigated the neuroprotective effects of baicalin in glutamate-exposed neurons. The mouse hippocampal neuronal cell line (HT22) was cultured in a general manner, glutamate and/or baicalin were treated on the cells. Baicalin was administered 1 hr before glutamate treatment. Cells were collected 24 hr after glutamate, and cell viability was measured using MTT assay. Reactive oxygen species (ROS) and lipid peroxidation (LPO) assays were performed to measure oxidative stress. Glutamate reduced cell viability in a dose- and time-dependent manner. MTT assay showed that baicalin treatment ameliorated the decrease in cell viability due to glutamate toxicity. The effect of baicalin is dose-dependent. Glutamate caused severe nerve damage, including condensation of the cell shape, loss of dendrites and axons. However, baicalin treatment attenuated these morphological changes, and the effect of baicalin was dose-dependent. ROS and LPO analyses showed that glutamate increases oxidative stress, and baicalin attenuates this change due to glutamate toxicity. The effect of baicalin on these results was dose-dependent. We confirmed that baicalin performs an antioxidant function against glutamate toxicity in neurons. In conclusion, these results suggest that baicalin exerts neuroprotective effects on damaged neurons through antioxidant activity.

This study conducted an acute toxicity assessment using Daphnia magna to evaluate the potential for increased toxicity when pharmaceuticals persist in aquatic environments not as single substances but in mixed forms. In single-substance toxicity tests, the antibiotics clarithromycin and sulfamethoxazole showed EC50 values of 22.3 mg L-1 and 61.05 mg L-1, respectively. However, the EC50 for the mixture of the two substances was determined to be 31.1 mg L-1. Based on these findings, applying the Similar Mode of Action (MOA) equation from the QSAR Toolbox, as recommended by OECD non-testing methods, produced an estimated EC50 of 33.7 mg L-1 for the mixture, showing a difference of 8.5% compared to the experimental value. This study confirms that combined exposure to pharmaceuticals can increase toxicity due to synergistic effects, indicating a significant potential risk to aquatic ecosystems. According to the UN-GHS classification criteria, clarithromycin, sulfamethoxazole, and their mixture were all classified as Category 3, indicating potential toxicity to aquatic organisms. These results emphasize the importance of toxicity assessments that consider interactions between multiple contaminants in real environmental settings, contributing to the development of effective toxicity evaluation and management strategies for the protection of aquatic ecosystems.