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.

Incurred sample reanalysis in AstraZeneca small molecule portfolio – what have we learned and where do we go next?

In this paper, experiences and learnings are shared from the 10-year application of incurred sample reanalysis (ISR) in support of the AstraZeneca small molecule portfolio. The conclusions from including ISR in every clinical bioanalysis study for a period of 5 years, generating ISR data from 550 studies, are shared. Our preclinical ISR approach is described and data generated using capillary microsampling demonstrate confidence in its routine application. The data demonstrate that ISR failures are very rare and the assessment can and should therefore be limited. Dialogue between the bioanalytical teams internally, as well as with the partner contract research organizations, is however critical for a successful bioanalytical method validation and to avoid any ISR failures.

ISR: what have we learned after a decade of experience?

Within our company, incurred sample reproducibility (ISR) was implemented a decade ago. Only 11 studies (<2%) with failed ISR were identified over that period. These cases are described along with the strategy followed to resolve the issue. For three studies the failing ISR was caused by a method failure and all instances could be traced back to an instability problem. The majority of the failed ISR experiments were due to human error. For most of the studies the issue could be resolved and results reported. In two studies, no valid results could be generated. Based on this analysis we advise to limit the number of studies that require ISR assessment.

Extended 3D and 4D cumulative plots for evaluation of unmatched incurred sample reanalysis

Aim: Incurred sample reanalysis (ISR) helps ensure the reliability of pharmacokinetic studies. An appropriate graph may facilitate the evaluation of an unmatched reanalyses or a failed ISR test. Methods: We evaluated different ways of visualizing multidimensional ISR data using an extended cumulative ISR plot. Results: 3D and 4D cumulative ISR plots enable comprehensive data analysis using a single plot. We propose to use color for percentage difference classes in bar and XY-scatter plots. For the latter the shape of symbols may represent analyte concentration class, study phase, analyst or subject. Conclusion: The extended 3D and 4D cumulative ISR plots facilitate in-study monitoring and post-study inspection of data. It helps find the root cause of unmatched ISR, thus increasing reliability of bioanalytical data.

Incurred sample reanalysis: adjusted procedure for sample size calculation

Aim: The incurred sample reanalysis (ISR) helps to assure bioanalysis reliability. The regulatory guidelines recommend the reanalysis of up to 10% of the study samples for this test, but not all reanalyses are necessary to evaluate the test result. Materials & methods: We have optimized ISR sample size calculation to eliminate negligible reanalyses. Results: Adjusted procedure eliminates negligible reanalyses – up to 66% of currently analyzed ISRs – without affecting the test outcome. Conclusion: The procedure is universal as it may be applied in the studies compliant with EMA and US FDA requirements, for both small and large molecules. It may assist the evolution of bioanalytical method validation as the current ISR sample size recommendations seem to be ill-matched with the acceptance criteria.

Comprehensive graphical presentation of data from incurred sample reanalysis

Aim: Incurred sample reanalysis (ISR) contributes to the reliability of pharmacokinetic studies. Despite regulatory guidelines having adopted ISR methodology, graphical presentation of data has been overlooked. Materials & methods: Different graphs were tested for datasets including limited, standard and large numbers of ISR pairs. The datasets covered both passed and failed cases. Results: We have developed a combination of complementary plots enabling the visual inspection of ISR data quality: %difference versus mean concentration and cumulative ISR plot. The former shows individual ISR datapoints and concentration-dependent trends, while the latter presents the contribution of individual pairs to the overall result as well as time-dependent trends. Conclusion: The proposed visualization of ISR data shows at a glance whether acceptance criteria for each sample and whole experiment are met or not. Standardized graphical presentation of ISR outcomes may increase quality of bioanalytical data.

Comparative assessment of bioanalytical method validation guidelines for pharmaceutical industry

The concepts, importance, and application of bioanalytical method validation have been discussed for a long time and validation of bioanalytical methods is widely accepted as pivotal before they are taken into routine use. United States Food and Drug Administration (USFDA) guidelines issued in 2001 have been referred for every guideline released ever since; may it be European Medical Agency (EMA) Europe, National Health Surveillance Agency (ANVISA) Brazil, Ministry of Health and Labour Welfare (MHLW) Japan or any other guideline in reference to bioanalytical method validation. After 12 years, USFDA released its new draft guideline for comments in 2013, which covers the latest parameters or topics encountered in bioanalytical method validation and approached towards the harmonization of bioanalytical method validation across the globe. Even though the regulatory agencies have general agreement, significant variations exist in acceptance criteria and methodology. The present review highlights the variations, similarities and comparison between bioanalytical method validation guidelines issued by major regulatory authorities worldwide. Additionally, other evaluation parameters such as matrix effect, incurred sample reanalysis including other stability aspects have been discussed to provide an ease of access for designing a bioanalytical method and its validation complying with the majority of drug authority guidelines.