Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.
Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.
复制标题
结合化学结构和生物学数据预测胎儿发育毒性。
DOI：
10.1021/acs.est.2c01040
复制
发表时间：
2022-05-03
影响因子：
11.4
通讯作者：
Zhu, Hao
中科院分区：
文献类型：
--
作者：
Ciallella, Heather L.;Russo, Daniel P.;Sharma, Swati;Li, Yafan;Sloter, Eddie;Sweet, Len;Huang, Heng;Zhu, Hao
为了危害识别、分类和标签的目的，法律要求制定动物试验指南，以评估新的和现有的化学产品的发育毒性。然而，指南发育毒性研究是昂贵的，耗时的，需要许多实验室动物。计算建模已成为一种有前途的，动物节约，成本效益高的方法，用于评估化学品的发育毒性潜力，如内分泌干扰物，而不使用动物。我们的目标是开发一个预测和解释的计算模型发育毒物。为此，从公共知识库和文献来源中整理了一个包含1，244种具有发育毒性分类的化学品的综合数据集。从PubChem和该数据集的ToxCast程序中提取了2，140项毒理学高通量筛选（HTS）试验的数据，并结合834种化学片段的信息，根据其化学-机理关系对试验进行分组。这项工作揭示了两个检测集群，分别包含83和76个检测，具有较高的阳性预测率的发育毒物确定与动物试验指南（PPV = 72.4%和77.3%，在交叉验证）。这两个试验聚类可用作发育毒性模型，并用于预测新的化学品进行外部验证。这项研究提供了一个新的战略，构建替代化学发育毒性评价，可以复制其他毒性建模研究。
For hazard identification and classification and labeling purposes, animal testing guidelines are required by law to evaluate developmental toxicity for new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1,244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2,140 toxicological high throughput screening (HTS) assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4% and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.
登录
查看更多内容
DOI：
10.1038/s41374-020-00477-2
发表时间：
2021-04
期刊：
Laboratory investigation; a journal of technical methods and pathology
影响因子：
--
作者：
Ciallella HL;Russo DP;Aleksunes LM;Grimm FA;Zhu H
通讯作者：
Zhu H
影响因子：
--
作者：
Cassano A;Manganaro A;Martin T;Young D;Piclin N;Pintore M;Bigoni D;Benfenati E
通讯作者：
Benfenati E
影响因子：
2.9
作者：
Barak Y;Sadovsky Y;Shalom-Barak T
通讯作者：
Shalom-Barak T
影响因子：
3
作者：
Arena, VC;Sussman, NB;Macina, OT
通讯作者：
Macina, OT
影响因子：
64.8
作者：
Christians, E;Davis, AA;Benjamin, IJ
通讯作者：
Benjamin, IJ