Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.
Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.
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DOI：
10.1016/j.comtox.2020.100126
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发表时间：
2020-08-01
期刊：
影响因子：
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通讯作者：
Paul Friedman, Katie
中科院分区：
文献类型：
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作者：
Ly Pham, Ly;Watford, Sean;Paul Friedman, Katie
化学危害评估的新方法方法（NAMs）通常通过与动物研究的比较来评价；然而，动物研究数据的可变性限制了NAM的准确性。美国环保局毒性参考数据库（ToxRefDB）考虑了影响水平的可变性，包括治疗相关影响的最低影响水平（LEL）和专家评审定义的最低可观察不良影响水平（LOAEL），这些影响来自亚急性、亚慢性、慢性、多代生殖和发育毒性研究。这项工作的目的是量化系统性LEL和LOAEL值（定义为仅对成年或亲代动物产生影响的效价值）之间的差异，并估计NAM预测精度的上限。使用多元线性回归（MLR）和增强细胞均值（ACM）模型来量化总方差，以及可用研究描述符（如给药途径、研究类型）解释的系统性LEL和LOAEL值的方差比例。MLR方法将每个研究描述符视为方差的独立贡献者，而ACM方法将分类描述符组合到细胞中以定义重复。使用这些方法，系统LEL和LOAEL值的总方差（以log10-mg/kg/天为单位）范围为0.74至0.92。LEL和LOAEL值的不明原因方差，由残差均方误差（MSE）近似，范围为0.20-0.39。分别考虑亚慢性、慢性或发展性研究设计的结果相似。基于MSE和r平方的拟合优度之间的关系，使用这些数据作为参考，基于nam的系统毒性预测模型的最大r平方可能接近55至73%。根据数据集和回归方法的不同，均方根误差（RMSE）范围为0.47至0.63 log10-mg/kg/day，表明系统效应水平的双边最小预测区间可能为58至284倍。这些发现建议在基于nama的系统毒性预测中建立科学信心的定量考虑。
New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log10-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log10-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.