Dataset Information

Estimation of acute oral toxicity in rat using local lazy learning.

ABSTRACT:

Background

Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.

Results

In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R(2) of 0.712 on a test set containing 2,896 compounds.

Conclusion

Encouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

SUBMITTER: Lu J

PROVIDER: S-EPMC4047767 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Estimation of acute oral toxicity in rat using local lazy learning.

Lu Jing J Peng Jianlong J Wang Jinan J Shen Qiancheng Q Bi Yi Y Gong Likun L Zheng Mingyue M Luo Xiaomin X Zhu Weiliang W Jiang Hualiang H Chen Kaixian K

Journal of cheminformatics 20140516

<h4>Background</h4>Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single ...[more]

PMID: 24959207

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Project description:The toxicity of soluble metal compounds is often different from that of the parent metal. Since no reliable data on acute toxicity, local effects, and mutagenicity of beryllium metal have ever been generated, beryllium metal powder was tested according to the respective Organisation for Economical Co-Operation and Development (OECD) guidelines. Acute oral toxicity of beryllium metal was investigated in rats and local effects on skin and eye in rabbits. Skin-sensitizing properties were investigated in guinea pigs (maximization method). Basic knowledge about systemic bioavailability is important for the design of genotoxicity tests on poorly soluble substances. Therefore, it was necessary to experimentally compare the capacities of beryllium chloride and beryllium metal to form ions under simulated human lung conditions. Solubility of beryllium metal in artificial lung fluid was low, while solubility in artificial lysosomal fluid was moderate. Beryllium chloride dissolution kinetics were largely different, and thus, metal extracts were used in the in vitro genotoxicity tests. Genotoxicity was investigated in vitro in a bacterial reverse mutagenicity assay, a mammalian cell gene mutation assay, a mammalian cell chromosome aberration assay, and an unscheduled DNA synthesis (UDS) assay. In addition, cell transformation was tested in a Syrian hamster embryo cell assay, and potential inhibition of DNA repair was tested by modification of the UDS assay. Beryllium metal was found not to be mutagenic or clastogenic based on the experimental in vitro results. Furthermore, treatment with beryllium metal extracts did not induce DNA repair synthesis, indicative of no DNA-damaging potential of beryllium metal. A cell-transforming potential and a tendency to inhibit DNA repair when the cell is severely damaged by an external stimulus were observed. Beryllium metal was also found not to be a skin or eye irritant, not to be a skin sensitizer, and not to have relevant acute oral toxic properties.

Project description:PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.

Dataset Information

Estimation of acute oral toxicity in rat using local lazy learning.

Background

Results

Conclusion

Publications

Estimation of acute oral toxicity in rat using local lazy learning.

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