کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5856517 | 1131976 | 2015 | 44 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): Part I: Irritation potential
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کلمات کلیدی
CFSANSMILESECETOCGHSEECMCCNIEHSOECD3RsTLAHBAADMEHbdCenter for Food Safety and Applied NutritionANNEuropean Union - اتحادیه اروپاEuropean Economic Community - اتحادیه اروپا اقتصادیGenetic algorithm - الگوریتم ژنتیکQSAR - بزرگسال SPR - تشدید پلاسمون سطحیDescriptors - توصیفگرهاRegistration, Evaluation, Authorisation and Restriction of Chemicals - ثبت، ارزیابی، مجوز و محدودیت مواد شیمیاییabsorption, distribution, metabolism, and excretion - جذب، توزیع، متابولیسم و دفعapplicability domain - دامنه کاربردDomain of applicability - دامنه کاربردDOA - دعاQuantitative structure–activity relationship - رابطه ساختاری و فعالیت کمیREACH - رسیدنStructure–property relationship - ساختار-مالکیت رابطهOrganisation for Economic Co-operation and Development - سازمان همکاری اقتصادی و توسعهglobally harmonized system of classification and labeling of chemicals - سیستم طبقه بندی و برچسب زدن مواد شیمیایی در سطح جهانی هماهنگ شده استArtificial Neural Network - شبکه عصبی مصنوعیArchitecture - معماری National Institute of Environmental Health Sciences - موسسه ملی علوم بهداشت محیطNeuron - نورونINPUT - ورودیSimplified Molecular Input Line Entry System - ورودی خط ساده ورودی مولکولیMolecular weight - وزن مولکولیEnsemble - گروهی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
بهداشت، سم شناسی و جهش زایی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor⢠program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Regulatory Toxicology and Pharmacology - Volume 71, Issue 2, March 2015, Pages 318-330
Journal: Regulatory Toxicology and Pharmacology - Volume 71, Issue 2, March 2015, Pages 318-330
نویسندگان
Rajeshwar P. Verma, Edwin J. Matthews,