A new approach to evaluate regression models during validation of bioanalytical assays.
The quality of bioanalytical data is highly dependent on using an appropriate regression model for calibration curves. Non-weighted linear regression has traditionally been used but is not necessarily the optimal model. Bioanalytical assays generally benefit from using either data transformation and/or weighting since variance normally increases with concentration. A data set with calibrators ranging from 9 to 10000 ng/mL was used to compare a new approach with the traditional approach for selecting an optimal regression model. The new approach used a combination of relative residuals at each calibration level together with precision and accuracy of independent quality control samples over 4 days to select and justify the best regression model. The results showed that log-log transformation without weighting was the simplest model to fit the calibration data and ensure good predictability for this data set.
Singtoroj
T
T
Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Tarning
J
J
Annerberg
A
A
Ashton
M
M
Bergqvist
Y
Y
White
N J
NJ
Lindegardh
N
N
Day
N P J
NP
D000465:Algorithms; D002138:Calibration; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D002626:Chemistry, Pharmaceutical / Q000379:methods*; D002853:Chromatography, Liquid / Q000379:methods*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D015233:Models, Statistical; D011804:Quinolines / Q000032:analysis* / Q000737:chemistry; D012044:Regression Analysis; D015203:Reproducibility of Results; D013678:Technology, Pharmaceutical / Q000379:methods*; D013997:Time Factors; D010879:Piperazines
D011804:Quinolines; C034759:piperaquine; D010879:Piperazines