欢迎来到麦多课文档分享! | 帮助中心 海量文档,免费浏览,给你所需,享你所想!
麦多课文档分享
全部分类
  • 标准规范>
  • 教学课件>
  • 考试资料>
  • 办公文档>
  • 学术论文>
  • 行业资料>
  • 易语言源码>
  • ImageVerifierCode 换一换
    首页 麦多课文档分享 > 资源分类 > PPT文档下载
    分享到微信 分享到微博 分享到QQ空间

    Carcinogenicity prediction for Regulatory Use.ppt

    • 资源ID:379320       资源大小:965.50KB        全文页数:36页
    • 资源格式: PPT        下载积分:2000积分
    快捷下载 游客一键下载
    账号登录下载
    微信登录下载
    二维码
    微信扫一扫登录
    下载资源需要2000积分(如需开发票,请勿充值!)
    邮箱/手机:
    温馨提示:
    如需开发票,请勿充值!快捷下载时,用户名和密码都是您填写的邮箱或者手机号,方便查询和重复下载(系统自动生成)。
    如需开发票,请勿充值!如填写123,账号就是123,密码也是123。
    支付方式: 支付宝扫码支付    微信扫码支付   
    验证码:   换一换

    加入VIP,交流精品资源
     
    账号:
    密码:
    验证码:   换一换
      忘记密码?
        
    友情提示
    2、PDF文件下载后,可能会被浏览器默认打开,此种情况可以点击浏览器菜单,保存网页到桌面,就可以正常下载了。
    3、本站不支持迅雷下载,请使用电脑自带的IE浏览器,或者360浏览器、谷歌浏览器下载即可。
    4、本站资源下载后的文档和图纸-无水印,预览文档经过压缩,下载后原文更清晰。
    5、试题试卷类文档,如果标题没有明确说明有答案则都视为没有答案,请知晓。

    Carcinogenicity prediction for Regulatory Use.ppt

    1、Carcinogenicity prediction for Regulatory Use,Natalja Fjodorova Marjana Novi, Marjan Vrako, Marjan TuarNational institute of Chemistry, Ljubljana, Slovenia,Kemijske Dnevi 25-27 September 2008,UNIVERZA MARIBOR,Overview,1. EU project CAESAR aimed for development of QSAR models for prediction of toxico

    2、logical properties of substances, used for regulatory purposes.2. The principles of validations of QSARs which will be used for chemical regulation.3. Carcinogenicity models using Counter Propagation Artificial Network,It is estimated that over 30000 industrial chemicals used in Europe require addit

    3、ional safety testing to meet requirements of new chemical regulation REACH. If conducted on animals this testing would require the use of an extra 10-20 million animal experiments. Quantitative Structure Activity Relationships (QSAR) is one major prospect between alternative testing methods to be us

    4、ed in a regulatory context.,aimed to develop (Q)SARs as non-animal alternative tools for the assessment of chemical toxicity under the REACH.,FR6- CAESAR European Project Computer Assisted Evaluation of Industrial chemical Substances According to Regulations,Coordinator- Emilio Benfenati- Istituto d

    5、i Ricerche Farmacologiche “Mario Negri”,The general aim of CAESAR is,1. To produce QSAR models for toxicity prediction of chemical substances, to be used for regulatory purposes under REACH in a transparent manner by applying new and unique modelling and validation methods.,2. Reduce animal testing

    6、and its associated costs, in accordance with Council Directive 86/609/EEC and Cosmetics Directive (Council Directive 2003/15/EC),CAESAR is solving several problems:,Ethical- save animal lifes; Economical- cost reduction on testing; Political- REACH implementation- new chemical legislation,CAESAR aim

    7、ed to develop new (Q)SAR models for 5 end-points:Bioaccumulation (BCF), Skin sensitisationMutagenicity Carcinogenicity Teratogenicity,The characterization of the QSAR models follows the general scheme of 5 OECD principles:,A defined endpoint An unambiguous algorithm A defined domain of applicability

    8、 Appropriate measures of goodness-of-fit, robustness and predictivity A mechanistic interpretation, if possible.,Principle1- A defined endpoint,Endpoint is the property or biological activity determined in experimental protocol, (OECDTest Guideline).Carcinogenicity is a defined endpoint addressed by

    9、 an officially recognized test method (Method B.32 Carcinogenicity test Annex V to Directive 67/548/EEC).,Principle2- An unambiguous algorithm,Algorithm is the form of relationship between chemical structure and property or biological activity being modelled. Examples: 1. Statistically (regression)

    10、based QSARs 2. Neural network model, which includes both learning process and prediction process.,Transparency in the (Q)SAR algorithm can be provided by means of the following information: a) Definition of the mathematical form of a QSAR model, or of the decision rule (e.g. in the case of a SAR) b)

    11、 Definitions of all descriptors in the algorithm, and a description of their derivation c) Details of the training set used to develop the algorithm.,Principle3- A Defined Domain of Applicability,The definition of the Applicability Domain (AD) is based on the assumption that a model is capable of ma

    12、king reliable predictions only within the structural, physicochemical and response space that is known from its training set. List of basic structures (for example, aniline, fluorene) The range of chemical descriptors values.,The assessment of model performance is sometimes called statistical valida

    13、tion.,Principle4- Appropriate measures goodness-of-fit, robustness (internal performance) and predictivity (external performance),Principle5- A mechanistic interpretation, if possible,Mechanistic interpretation of (Q)SAR provides a ground for interaction and dialogue between model developer, and tox

    14、icologists and regulators, and permits the integration of the (Q)SAR results into wider regulatory framework, where different types of evidence and data concur or compliment each other as a basis for making decisions and taking actions. Example: enhancing/inhibition the metabolic activation of subst

    15、ances may be discussed.,National Institute of Chemistry in Ljubljana (NIC-LJU) is responsible for development of models for predicton of carcinogenicity,DATA ON CARCINOGENICITY,1.Studies of carcinogenicity in humans 2.Carcinogenicity studies in animals 3.Other relevant data additional evidence relat

    16、ed to the possible carcinogenicity Genetic Toxicology Structure-Activity Comparisons Pharmacokinetics and Metabolism Pathology,Cancer Risk Assessment IARC International Agency for Research of Cancer,Predictive Toxicology Approaches,1. Quantitative models (QSARs) Continuous data prediction on the bas

    17、is of experimental evidence of rodent carcinogenic potential (TD50 tumorgenic dose)2. Categorical models based on YES/NO data. (P-positive; NP-not positive),Dataset:,805 chemicals were filtered from 1481compounds taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network h

    18、ttp:/www.epa.gov/ncct/dsstox/sdf_cpdbas.html which was derived from the Lois Gold Carcinogenic Database (CPDBAS)The chemicals involved in the study belong to different chemical classes, (noncongeneric substances),Descriptors:,252 MDL descriptors were calculated in program MDL QSAR.2. Descriptors dat

    19、aset was reduced to 27 MDL descriptors, using Kohonen map and Principle Component Analisis.,Counter Propagation Artificial Neural Network,Step1: mapping of molecule Xs (vector representing structure) into the Kohonen layer,Step2: correction of weights in both, the Kohonen and the Output layer,Step3:

    20、 prediction of the four-dementional target (toxicity) Ts,Investigation of quantitative models shows us low results RESPONCE- TD50mmol,Correlation coefficient in the external validation is lower then 0.5,Continuouse data models (Quantitative models),Investigation of categorical models shows us satisf

    21、actory results,YES/NO principeRESPONCE: P-positive-active NP-not positive-inactive,Characteristics used for validation of categorical model,true positive(TP), true negative (TN) Accuracy(AC), AC=(TN+TP)/(TN+TP+FN+FP) TPrate=Sensitivity(SE)=TP/(TP+FN) TNrate=Specificity(SP)=TN/(TN+FP),Categorical mod

    22、el for dataset 805 chemicals (Training=644 and Test=161), using 27 MDL descriptors,Confusion matrix TR(644)/TE(161) classes (Positive- Negative),FP,FN,TP,TN,How we find optimal model, using threshold,Threshold=0.45 Accuracy=0.68 SE=0.73 SP=0.63,Changing of threshold allows us to get models with diff

    23、erent statistical performances.,ROC(Receiver operating characteristic) curve,Training set,Test set,The area under the curve is 0.988 and 0.699 in the training and test sets, respectively.,How requrements of REACH reflect development of models,To focus model to high sensitivity in prediction of carci

    24、nogenicity From regulatory perspective, the higher sensitivity in predicting carcinogens is more desirable than high specificity Sensitivity- percentage of correct predictions of carcinogens Specificity- percentage of correct predictions of non-carcinogens,Conclusion,1.We have bult the carcinogenici

    25、ty models in accordance with 5 OECD principles principle of validation 2. We have got satisfactory results for categorical models with accuracy 68% which is good for carcinogenicity as it meet the level of uncertanty of test data. 3. The goal of our future investigation will be dedicated to research

    26、 of relationship between results of carcinogenicity tests and presence of Genotoxic, non Genotoxic alerts using TOX TREE program.,Acknowledgements,The financial support of the European Union through CAESAR project (SSPI-022674) as well as of the Slovenian Ministry of Higher Education, Science and Technology (grant P1-017) is gratefully acknowledged.,THANK YOU,


    注意事项

    本文(Carcinogenicity prediction for Regulatory Use.ppt)为本站会员(deputyduring120)主动上传,麦多课文档分享仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对上载内容本身不做任何修改或编辑。 若此文所含内容侵犯了您的版权或隐私,请立即通知麦多课文档分享(点击联系客服),我们立即给予删除!




    关于我们 - 网站声明 - 网站地图 - 资源地图 - 友情链接 - 网站客服 - 联系我们

    copyright@ 2008-2019 麦多课文库(www.mydoc123.com)网站版权所有
    备案/许可证编号:苏ICP备17064731号-1 

    收起
    展开