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Abstract：
BACKGROUND: The intergovernmental organizations Organisation for Economic Co-operation and Development (OECD) and Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) have developed guidelines for the use of in vitro models for toxicological evaluation of chemicals. However, the presence of manual steps and the requirement of multiple tools for data analysis, apart from being costly and time-consuming, can inadvertently introduce errors by researchers.
OBJECTIVE: We have developed the SAEDC platform (Technological Solution for Exploratory Data Analysis and Statistics for Cytotoxicity, in Portuguese), which enables analysis of cytotoxicity data from assays following OECD Guideline No. 129.
METHODS: In vitro experimental data were used to compare with the analysis methodology suggested in the Guideline. We analyzed 117 data sets covering chemicals from Category I to Unclassified according to GHS classification.
RESULTS: The four-parameters of non-linear regression (4PL) calculated by the SAEDC platform showed no significant differences compared to standard methodology in any of the data sets (p > 0.05). The coefficient of determination (R-squared) also demonstrated not only a good fit of the 4PL model to the data but also significant similarity to values obtained by the conventional methodology. Finally, the SAEDC platform predicted LD50 values for the chemicals from IC50, using the Registry of Cytotoxicity (RC) regression models.
CONCLUSIONS: The comparison with the standard data analysis methodology revealed that SAEDC platform fulfills the requirements for cytotoxicity data analysis, generating reliable and accurate results with fewer steps performed by researchers. The use of SAEDC platform for obtaining toxicity values can reduce analysis time compared to the standard methodology proposed by regulatory agencies. Thus, automation of the analysis using the SAEDC platform has the potential to save time and resources for cytotoxicity researchers and laboratories while generating reliable results.
OBJECTIVE: We have developed the SAEDC platform (Technological Solution for Exploratory Data Analysis and Statistics for Cytotoxicity, in Portuguese), which enables analysis of cytotoxicity data from assays following OECD Guideline No. 129.
METHODS: In vitro experimental data were used to compare with the analysis methodology suggested in the Guideline. We analyzed 117 data sets covering chemicals from Category I to Unclassified according to GHS classification.
RESULTS: The four-parameters of non-linear regression (4PL) calculated by the SAEDC platform showed no significant differences compared to standard methodology in any of the data sets (p > 0.05). The coefficient of determination (R-squared) also demonstrated not only a good fit of the 4PL model to the data but also significant similarity to values obtained by the conventional methodology. Finally, the SAEDC platform predicted LD50 values for the chemicals from IC50, using the Registry of Cytotoxicity (RC) regression models.
CONCLUSIONS: The comparison with the standard data analysis methodology revealed that SAEDC platform fulfills the requirements for cytotoxicity data analysis, generating reliable and accurate results with fewer steps performed by researchers. The use of SAEDC platform for obtaining toxicity values can reduce analysis time compared to the standard methodology proposed by regulatory agencies. Thus, automation of the analysis using the SAEDC platform has the potential to save time and resources for cytotoxicity researchers and laboratories while generating reliable results.
摘要：
背景：政府间组织经济合作与发展组织（OECD）和替代方法验证机构间协调委员会（ICCVAM）制定了使用体外模型进行毒理学评估的指南化学品。然而,手动步骤的存在和数据分析的多种工具的需求，除了昂贵和耗时之外，可能会无意中引入研究人员的错误。
目的：我们开发了SAEDC平台（用于细胞毒性的探索性数据分析和统计的技术解决方案，葡萄牙语)，这使得能够分析来自遵循经合组织准则号的测定的细胞毒性数据。129.
方法：使用体外实验数据与指南中建议的分析方法进行比较。我们分析了117个数据集，涵盖了根据GHS分类从I类到未分类的化学品。
结果：通过SAEDC平台计算的非线性回归（4PL）的四个参数与标准方法相比，在任何数据集中都没有显着差异（p>0.05）。确定系数（R平方）不仅证明了4PL模型与数据的良好拟合，而且还证明了与常规方法获得的值的显着相似性。最后,SAEDC平台使用细胞毒性注册（RC）回归模型从IC50预测化学品的LD50值。
结论：与标准数据分析方法的比较表明，SAEDC平台符合细胞毒性数据分析的要求，生成可靠和准确的结果与研究人员执行更少的步骤。与监管机构提出的标准方法相比，使用SAEDC平台获得毒性值可以减少分析时间。因此，使用SAEDC平台的自动化分析有可能为细胞毒性研究人员和实验室节省时间和资源，同时产生可靠的结果。
目的：我们开发了SAEDC平台（用于细胞毒性的探索性数据分析和统计的技术解决方案，葡萄牙语)，这使得能够分析来自遵循经合组织准则号的测定的细胞毒性数据。129.
方法：使用体外实验数据与指南中建议的分析方法进行比较。我们分析了117个数据集，涵盖了根据GHS分类从I类到未分类的化学品。
结果：通过SAEDC平台计算的非线性回归（4PL）的四个参数与标准方法相比，在任何数据集中都没有显着差异（p>0.05）。确定系数（R平方）不仅证明了4PL模型与数据的良好拟合，而且还证明了与常规方法获得的值的显着相似性。最后,SAEDC平台使用细胞毒性注册（RC）回归模型从IC50预测化学品的LD50值。
结论：与标准数据分析方法的比较表明，SAEDC平台符合细胞毒性数据分析的要求，生成可靠和准确的结果与研究人员执行更少的步骤。与监管机构提出的标准方法相比，使用SAEDC平台获得毒性值可以减少分析时间。因此，使用SAEDC平台的自动化分析有可能为细胞毒性研究人员和实验室节省时间和资源，同时产生可靠的结果。
