Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1289/EHP3614 |
Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across | |
Russo, Daniel P.1; Strickland, Judy2; Karmaus, Agnes L.2; Wang, Wenyi1; Shende, Sunil1,3; Hartung, Thomas4,5; Aleksunes, Lauren M.6; Zhu, Hao1,7 | |
2019-04-01 | |
发表期刊 | ENVIRONMENTAL HEALTH PERSPECTIVES |
ISSN | 0091-6765 |
EISSN | 1552-9924 |
出版年 | 2019 |
卷号 | 127期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Germany |
英文摘要 | BACKGROUND: Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data. OBJECTIVE: We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach. METHODS: A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioproliles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity. RESULT: Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment-in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms. CONCLUSIONS: The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000467131100003 |
WOS关键词 | INTEGRATED TESTING STRATEGIES ; IN-VITRO ; SIGNALING PATHWAY ; ORAL TOXICITY ; BIG DATA ; PREDICTION ; PHOSPHORYLATION ; ACTIVATION ; BIOAVAILABILITY ; REPLACEMENT |
WOS类目 | Environmental Sciences ; Public, Environmental & Occupational Health ; Toxicology |
WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health ; Toxicology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/182027 |
专题 | 资源环境科学 |
作者单位 | 1.Rutgers State Univ, Ctr Computat & Integrat Biol, Camden, NJ 08102 USA; 2.Integrated Lab Syst, Res Triangle Pk, NC USA; 3.Rutgers State Univ, Dept Comp Sci, Camden, NJ 08102 USA; 4.CAAT, Johns Hopkins Bloomberg Sch Publ Hlth, Baltimore, MD USA; 5.Univ Konstanz, CAAT Europe, Constance, Germany; 6.Rutgers State Univ, Ernest Mario Sch Pharm, Dept Pharmacol & Toxicol, Piscataway, NJ USA; 7.Rutgers State Univ, Dept Chem, Camden, NJ 08102 USA |
推荐引用方式 GB/T 7714 | Russo, Daniel P.,Strickland, Judy,Karmaus, Agnes L.,et al. Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across[J]. ENVIRONMENTAL HEALTH PERSPECTIVES,2019,127(4). |
APA | Russo, Daniel P..,Strickland, Judy.,Karmaus, Agnes L..,Wang, Wenyi.,Shende, Sunil.,...&Zhu, Hao.(2019).Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.ENVIRONMENTAL HEALTH PERSPECTIVES,127(4). |
MLA | Russo, Daniel P.,et al."Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across".ENVIRONMENTAL HEALTH PERSPECTIVES 127.4(2019). |
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