T/SHSOT 004.1-2022 Active Group standards

T/SHSOT 004.1-2022 Guidelines for optimizing in vitro toxicological test methods Part 1 General principles

Publish Date: 2022-09-28 Implement Date: 2022-10-15 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

Price: $ 20.00

Out of stock

Basic Information

Standard Code: T/SHSOT 004.1-2022

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: Integrated Environmental Protection

ICS Name: Integrated Environment and Environmental Protection

Publish Date: 2022-09-28

Implement Date: 2022-10-15

Scope

Scope: This document specifies the basic principles for optimizing standard methods of in vitro toxicity testing, as well as guidelines for their development, validation, and technical details. This document applies to the validation, transfer, and implementation of similar or optimized methods of standard methods of in vitro toxicity testing for chemicals and related products. Main technical content: - Issues related to the acceptability of standard statistical methods for toxicity testing. It stipulates statistical power analysis and the use of statistical power analysis to calculate the ideal number of replicates to determine the exact differences between different treatments at a predefined confidence level. - Interference from non-statistical factors. It points out that, in addition to statistical requirements, the impact of practical factors such as cost and the feasibility of repeat testing should also be considered. - Data analysis for in vitro experiments. It specifies requirements for data collection, data analysis methods, and influencing factors. Data analysis for in vitro methods includes data conversion, such as the standardization of mathematical model formulas; data fitting, used to fit responses with concentration-effect relationships to obtain statistical parameters. This involves various alternative scenarios, such as when fitting dose-response curves, the type of equation used (e.g., four-parameter logistic curve) should be recorded along with any constraints (e.g., maximum constraint of normalized data at 100%), restrictions (e.g., monotonicity assumption), and weights (e.g., inverse data uncertainty). - Understanding the mathematical logical relationship between methods and parameters, such as the median effective concentration (EC50), maximum reference positive control concentration (RPCMax), maximum positive control concentration (PCMax), median positive control concentration (PC50), 10% positive control concentration (PC10), and