DOSE-RESPONSE ASSESSMENT FOR DEVELOPMENTAL TOXICITY .3. STATISTICAL-MODELS
DOSE-RESPONSE ASSESSMENT FOR DEVELOPMENTAL TOXICITY .3. STATISTICAL-MODELS
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DOI：
10.1006/faat.1994.1134
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发表时间：
1994-11-01
期刊：
影响因子：
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通讯作者：
FAUSTMAN, EM
中科院分区：
文献类型：
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作者：
ALLEN, BC;KAVLOCK, RJ;FAUSTMAN, EM
虽然定量模型多年来一直是癌症风险评估的核心，但发育效应的剂量反应模型的概念相对较新。已提出将基准剂量（BMD）方法用于发育（以及其他非癌症）终点，以确定参考剂量和参考浓度。已经开发并应用了适用于代表发育毒性试验独特特征的统计模型（K。Rai和J.货车Ryzin，1985，Biometrics 41，1-9; L.库珀角波捷，你好。Hogan和E. Yamamoto，1986，Biometrics 42，85-98; R.科德尔河，巴西-地Howe，J. Chen，and D.盖勒1991年，《风险分析》11，583-590）。这些模型的推广（分别称为RVR、RVR和NCTR模型）解释了窝内个体胎仔或植入物中观察结果之间的相关性;剂量以外的变量（如窝仔数）影响不良结局概率的可能性;以及阈值剂量的可能性，低于该阈值剂量时背景应答率不变。将广义模型应用于607个终点的数据库，应答率显著增加，与剂量相关。经确定，模型通常能够拟合观察到的剂量-反应模式，其中RISK模型似乎在拟合方面上级。一个显着的贡献者的能力，拟合的数据是它的灵活性方面的代表性的反应概率对窝仔数的依赖性，一个性状不共享的其他两个模型。窝仔数似乎是预测应答率的显著协变量，即使通过假设个体胎仔间观察结果的β-二项分布来解释窝仔内相关性。相比之下，阈值剂量参数似乎不需要充分描述观察到的剂量反应模式。所有三种模型的BMD估计值（对应于5%的额外风险）彼此相似，并且与其他通用剂量反应模型（并非专门设计用于发育毒性试验）估计的BMD相似，这些模型模拟了受影响胎儿的平均比例。5%风险水平下的BMD与通过趋势统计检验确定的未观察到不良反应水平相似。需要更加重视和进一步审查发育毒性试验的剂量-反应模型;应鼓励采用生物学方法，考虑在此类试验中诱导的发育效应的连续性。(C)1994年毒理学学会。
Although quantitative modeling has been central to cancer risk assessment for years, the concept of dose-response modeling for developmental effects is relatively new. The benchmark dose (BMD) approach has been proposed for use with developmental (as well as other noncancer) endpoints for determining reference doses and reference concentrations. Statistical models appropriate for representing the unique features of developmental toxicity testing have been developed and applied (K. Rai and J. Van Ryzin, 1985, Biometrics 41, 1-9; L. Kupper, C. Portier, hi. Hogan, and E. Yamamoto, 1986, Biometrics 42, 85-98; R. Kodell, R. Howe, J. Chen, and D. Gaylor. 1991, Risk Anal. 11, 583-590). Generalizations of those models (designated the RVR, LOG, and NCTR models, respectively) account for the correlations among observations in individual fetuses or implant within litters; the potential for variables other than dose, such as litter size, to affect the probability of adverse outcome; and the possibility of a threshold dose below which background response rates are unaltered. The generalized models were applied to a database of 607 endpoints with significant dose-related increases in response rate. It was determined that the models were generally capable of fitting the observed dose-response patterns, with the LOG model appearing to be superior with respect to fit. A significant contributor to the ability of the LOG model to fit the data was its flexibility with respect to the representation of the dependence of response probability on litter size, a trait not shared by the other two models. Litter size appeared to be a significant covariable for predicting response rates, even when intralitter correlation was accounted for by assuming a beta-binomial distribution for the observations among individual fetuses. In contrast, a threshold dose parameter did not appear to be necessary to adequately describe the observed dose-response patterns. BMD estimates (corresponding to 5% additional risk) from all three models were similar to one another and to BMDs estimated from other, generic dose-response models (not specifically designed for developmental toxicity testing) that modeled average proportion of fetuses affected. The BMDs at the 5% level of risk were similar to no observed adverse effect levels determined by statistical tests of trend. Greater emphasis on and further examination of dose-response modeling for developmental toxicity testing are needed; biologically based approaches that consider the continuum of developmental effects induced in such tests should be encouraged. (C) 1994 Society of Toxicology.