亚硝胺类化合物致癌作用力分类法(CPCA)预测的置信度分数计算方法

IF 3.1 Q2 TOXICOLOGY
Suman Chakravarti, Roustem D. Saiakhov, Mounika Girireddy
{"title":"亚硝胺类化合物致癌作用力分类法(CPCA)预测的置信度分数计算方法","authors":"Suman Chakravarti,&nbsp;Roustem D. Saiakhov,&nbsp;Mounika Girireddy","doi":"10.1016/j.comtox.2023.100298","DOIUrl":null,"url":null,"abstract":"<div><p>We present a method for computing confidence in the Carcinogenic Potency Categorization Approach (CPCA) based predictions for N-nitrosamines. Our method relies on capturing local structural variations surrounding the nitrosamine core, which can significantly influence potency and may introduce uncertainty into predictions relying on these features.</p><p>We use continuous-valued fingerprints to conduct a specialized neighborhood analysis, grouping nitrosamines with similar local features. Using a reference dataset of 7679 potential Nitrosamine Drug Substance Related Impurities (NDSRIs) with pre-computed CPCA-derived Acceptable Intake (AI) limits, we gauge the prediction confidence for a given query N-nitrosamine by evaluating the distances and CPCA derived potency category distribution among neighboring NDSRIs. Our methodology allows for a nuanced assessment of CPCA's discrete four-level outcomes (i.e. 18/26.5, 100, 400, and 1500 ng AI limits). It enables the differentiation of robust predictions from potentially uncertain ones, for instance, cases where low confidence arises from rare structural features in the query nitrosamine, helpful in regulatory decision-making.</p><p>In our analysis of 30 nitrosamines with animal carcinogenicity data, we often observed lower confidence scores when experimental TD<sub>50</sub> values significantly disagreed with CPCA-calculated potency. Moreover, lower confidence scores were associated with greater variability in the predicted α-carbon hydroxylation potential of neighboring compounds. In a list of 265 NDSRIs with established regulatory AI limits, approximately 68% received strong confidence scores for accurate CPCA potency class predictions. However, 8% received poor confidence in potency class predictions, as well as lacked sufficient neighbor support due to uncommon structural features.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Confidence score calculation for the carcinogenic potency categorization approach (CPCA) predictions for N-nitrosamines\",\"authors\":\"Suman Chakravarti,&nbsp;Roustem D. Saiakhov,&nbsp;Mounika Girireddy\",\"doi\":\"10.1016/j.comtox.2023.100298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We present a method for computing confidence in the Carcinogenic Potency Categorization Approach (CPCA) based predictions for N-nitrosamines. Our method relies on capturing local structural variations surrounding the nitrosamine core, which can significantly influence potency and may introduce uncertainty into predictions relying on these features.</p><p>We use continuous-valued fingerprints to conduct a specialized neighborhood analysis, grouping nitrosamines with similar local features. Using a reference dataset of 7679 potential Nitrosamine Drug Substance Related Impurities (NDSRIs) with pre-computed CPCA-derived Acceptable Intake (AI) limits, we gauge the prediction confidence for a given query N-nitrosamine by evaluating the distances and CPCA derived potency category distribution among neighboring NDSRIs. Our methodology allows for a nuanced assessment of CPCA's discrete four-level outcomes (i.e. 18/26.5, 100, 400, and 1500 ng AI limits). It enables the differentiation of robust predictions from potentially uncertain ones, for instance, cases where low confidence arises from rare structural features in the query nitrosamine, helpful in regulatory decision-making.</p><p>In our analysis of 30 nitrosamines with animal carcinogenicity data, we often observed lower confidence scores when experimental TD<sub>50</sub> values significantly disagreed with CPCA-calculated potency. Moreover, lower confidence scores were associated with greater variability in the predicted α-carbon hydroxylation potential of neighboring compounds. In a list of 265 NDSRIs with established regulatory AI limits, approximately 68% received strong confidence scores for accurate CPCA potency class predictions. However, 8% received poor confidence in potency class predictions, as well as lacked sufficient neighbor support due to uncommon structural features.</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468111323000397\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111323000397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

我们提出了一种方法,用于计算基于致癌性分类方法 (CPCA) 的 N-亚硝胺预测的置信度。我们的方法依赖于捕捉亚硝胺核心周围的局部结构变化,这些变化会显著影响药效,并可能给依赖于这些特征的预测带来不确定性。我们使用连续值指纹进行专门的邻域分析,将具有相似局部特征的亚硝胺分组。我们使用连续值指纹进行专门的邻域分析,对具有相似局部特征的亚硝胺进行分组。我们使用由 7679 个潜在亚硝胺药物物质相关杂质 (NDSRI) 组成的参考数据集(其中包含预先计算的 CPCA 导出的可接受摄入量 (AI) 限值),通过评估邻域 NDSRI 之间的距离和 CPCA 导出的药效类别分布来衡量特定查询 N-亚硝胺的预测可信度。我们的方法允许对 CPCA 的离散四级结果(即 18/26.5、100、400 和 1500 毫微克 AI 限制)进行细致评估。它能够将稳健的预测与潜在的不确定预测区分开来,例如,低置信度是由于查询亚硝胺中罕见的结构特征造成的,这有助于监管决策。在我们对 30 种具有动物致癌性数据的亚硝胺进行的分析中,当实验 TD50 值与 CPCA 计算出的效价明显不一致时,我们经常观察到较低的置信度分数。此外,较低的置信度分数与邻近化合物的预测 α 碳羟化潜力的较大变异性有关。在一份已确定监管 AI 限制的 265 种 NDSRI 清单中,约 68% 的化合物在 CPCA 药效类别准确预测方面获得了较高的置信度分数。但是,有 8% 的化合物在药效类别预测方面的置信度较低,并且由于结构特征不常见而缺乏足够的邻近支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Confidence score calculation for the carcinogenic potency categorization approach (CPCA) predictions for N-nitrosamines

We present a method for computing confidence in the Carcinogenic Potency Categorization Approach (CPCA) based predictions for N-nitrosamines. Our method relies on capturing local structural variations surrounding the nitrosamine core, which can significantly influence potency and may introduce uncertainty into predictions relying on these features.

We use continuous-valued fingerprints to conduct a specialized neighborhood analysis, grouping nitrosamines with similar local features. Using a reference dataset of 7679 potential Nitrosamine Drug Substance Related Impurities (NDSRIs) with pre-computed CPCA-derived Acceptable Intake (AI) limits, we gauge the prediction confidence for a given query N-nitrosamine by evaluating the distances and CPCA derived potency category distribution among neighboring NDSRIs. Our methodology allows for a nuanced assessment of CPCA's discrete four-level outcomes (i.e. 18/26.5, 100, 400, and 1500 ng AI limits). It enables the differentiation of robust predictions from potentially uncertain ones, for instance, cases where low confidence arises from rare structural features in the query nitrosamine, helpful in regulatory decision-making.

In our analysis of 30 nitrosamines with animal carcinogenicity data, we often observed lower confidence scores when experimental TD50 values significantly disagreed with CPCA-calculated potency. Moreover, lower confidence scores were associated with greater variability in the predicted α-carbon hydroxylation potential of neighboring compounds. In a list of 265 NDSRIs with established regulatory AI limits, approximately 68% received strong confidence scores for accurate CPCA potency class predictions. However, 8% received poor confidence in potency class predictions, as well as lacked sufficient neighbor support due to uncommon structural features.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信