SAEDC: Development of a technological solution for exploratory data analysis and statistics in cytotoxicity

Introduction: 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. Objectives: 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. Methodology: 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 (Rsquared) 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. Conclusion: 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.