Handling unstable analytes: literature review and expert panel survey by Japan Bioanalysis Forum Discussion Group

Analyzing unstable small molecule drugs and metabolites in blood continues to be challenging for bioanalysis. Although scientific countermeasures such as immediate cooling, immediate freezing, addition of enzyme inhibitors, pH adjustment, dried blood spot or derivatization have been developed, selecting the best practices has become an issue in the pharmaceutical industry as the number of drugs with such problems is increasing, even for generic drugs. In this study, we conducted a comprehensive literature review and a questionnaire survey to determine a suitable practice for evaluating instability and implementing countermeasures. Three areas of focus, matrix selection, effect of hemolysis and selection of esterase inhibitors, are discussed.

Incurred Sample Reanalysis: Time to Change the Sample Size Calculation?

Reliable results of pharmacokinetic and toxicokinetic studies are vital for correct decision making during drug discovery and development. Thus, ensuring high quality of bioanalytical methods is of critical importance. Incurred sample reanalysis (ISR)-one of the tools used to validate a method-is included in the bioanalytical regulatory recommendations. The methodology of this test is well established, but the estimation of the sample size is still commented on and contested. We have applied the hypergeometric distribution to evaluate ISR test passing rates in different clinical study sizes. We have tested both fixed rates of the clinical samples-as currently recommended by FDA and EMA-and a fixed number of ISRs. Our study revealed that the passing rate using the current sample size calculation is related to the clinical study size. However, the passing rate is much less dependent on the clinical study size when a fixed number of ISRs is used. Thus, we suggest using a fixed number of ISRs, e.g., 30 samples, for all studies. We found the hypergeometric distribution to be an adequate model for the assessment of similarities in original and repeated data. This model may be further used to optimize the sample size needed for the ISR test as well as to bridge data from different methods. This paper provides a basis to re-consider current ISR recommendations and implement a more statistically rationalized and risk-controlled approach.