scholarly journals Replicates Number for Drug Stability Testing during Bioanalytical Method Validation—An Experimental and Retrospective Approach

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 457
Author(s):  
Elżbieta Gniazdowska ◽  
Wojciech Goch ◽  
Joanna Giebułtowicz ◽  
Piotr J. Rudzki

Background: The stability of a drug or metabolites in biological matrices is an essential part of bioanalytical method validation, but the justification of its sample size (replicates number) is insufficient. The international guidelines differ in recommended sample size to study stability from no recommendation to at least three quality control samples. Testing of three samples may lead to results biased by a single outlier. We aimed to evaluate the optimal sample size for stability testing based on 90% confidence intervals. Methods: We conducted the experimental, retrospective (264 confidence intervals for the stability of nine drugs during regulatory bioanalytical method validation), and theoretical (mathematical) studies. We generated experimental stability data (40 confidence intervals) for two analytes—tramadol and its major metabolite (O-desmethyl-tramadol)—in two concentrations, two storage conditions, and in five sample sizes (n = 3, 4, 5, 6, or 8). Results: The 90% confidence intervals were wider for low than for high concentrations in 18 out of 20 cases. For n = 5 each stability test passed, and the width of the confidence intervals was below 20%. The results of the retrospective study and the theoretical analysis supported the experimental observations that five or six repetitions ensure that confidence intervals fall within 85–115% acceptance criteria. Conclusions: Five repetitions are optimal for the assessment of analyte stability. We hope to initiate discussion and stimulate further research on the sample size for stability testing.

2014 ◽  
Vol 16 (2) ◽  
pp. 352-356 ◽  
Author(s):  
Joseph F. Bower ◽  
Jennifer B. McClung ◽  
Carl Watson ◽  
Takahiko Osumi ◽  
Kátia Pastre

PEDIATRICS ◽  
1989 ◽  
Vol 83 (3) ◽  
pp. A72-A72
Author(s):  
Student

The believer in the law of small numbers practices science as follows: 1. He gambles his research hypotheses on small samples without realizing that the odds against him are unreasonably high. He overestimates power. 2. He has undue confidence in early trends (e.g., the data of the first few subjects) and in the stability of observed patterns (e.g., the number and identity of significant results). He overestimates significance. 3. In evaluating replications, his or others', he has unreasonably high expectations about the replicability of significant results. He underestimates the breadth of confidence intervals. 4. He rarely attributes a deviation of results from expectations to sampling variability, because he finds a causal "explanation" for any discrepancy. Thus, he has little opportunity to recognize sampling variation in action. His belief in the law of small numbers, therefore, will forever remain intact.


2005 ◽  
Vol 22 (9) ◽  
pp. 1425-1431 ◽  
Author(s):  
JoMarie Smolec ◽  
Binodh DeSilva ◽  
Wendell Smith ◽  
Russell Weiner ◽  
Marian Kelly ◽  
...  

2014 ◽  
Vol 36 (6) ◽  
pp. 739-745 ◽  
Author(s):  
Sara Baldelli ◽  
Dario Cattaneo ◽  
Serena Fucile ◽  
Emilio Clementi

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