Abstract：
We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D-2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors. Then, the combined pool of RASAR and the identified 0D-2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.
摘要：
我们已经开发了一种定量的安全预测模型，用于亚慢性重复剂量的不同有机化学物质对大鼠使用新的定量阅读结构-活性关系（q-RASAR）方法，它使用基于相似性的描述符来生成预测模型。实验-Log(NOAEL)值在这里已被用作大鼠口服亚慢性安全性的潜在指标,因为它确定了未发现观察到的化学品不良反应的最大剂量水平。使用结构和物理化学（0D-2D）描述符，总共使用了186个不同有机化学物质的数据点用于模型生成。跨读派生的相似性，错误,并从初步的0D-2D描述符中提取了一致性度量(RASAR描述符)。然后，通过使用偏最小二乘(PLS)算法,采用RASAR组合池和训练集确定的0D-2D描述符来开发最终模型.根据经济合作与发展组织（OECD）的建议，开发的PLS模型已通过各种内部和外部验证指标进行了严格验证。最终的q-RASAR模型被证明是统计上合理的，稳健和外部预测性（R2=0.85，Q2LOO=0.82和Q2F1=0.94）取代了相应的定量结构-活性关系（QSAR）模型以及先前报道的亚慢性重复剂量毒性模型的内部和外部预测性。简而言之，q-RASAR是一种有效的方法，有可能作为一种很好的替代方法来提高外部预测性，可解释性,亚慢性口服安全性预测和生态毒性风险识别的可转移性。
