Is a deuterated internal standard appropriate for the reliable determination of olmesartan in human plasma?

Development and validation of an UPLC-MS/MS method for the simultaneous determination of fexofenadine and olmesartan in human serum: Application to in vivo pharmacokinetic studies

A sensitive, reproducible, robust, high-throughput ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the simultaneous quantification of fexofenadine and olmesartan in human serum. Samples (50 µL) undergo protein precipitation prior to UPLC-MS/MS analysis. The analytes were separated using an Acquity BEH C18 column (2.1 mm × 50 mm, 1.7 µm) at a flow rate of 0.5 mL/min using a gradient elution with a total run time of 4 min. The analytes were detected in positive ion mode and selected reaction monitoring (SRM) was used for quantitation. The standard curve concentration range was 1.0-500.0 ng/mL for both analytes and each analyte showed excellent linearity with correlation coefficients (R2 > 0.99). The intra- and inter-day accuracy and precision were ±15% for each analyte, and excellent recovery was demonstrated (93-98%) for both analytes. The method is well suited for high-throughput quantitative determination of fexofenadine and olmesartan simultaneously and was successfully applied to an in vivo pharmacokinetic and transporter phenotyping study in humans.

Analytical methods based on liquid chromatography for the analysis of albumin adducts involved in retrospective biomonitoring of exposure to mustard agents

The objective of the present review is to list, describe, compare, and critically analyze the main procedures developed in the last 20 years for the analysis of digested alkylated peptides, resulting from the adduction of albumin by different mustard agents, and that can be used as biomarkers of exposure to these chemical agents. While many biomarkers of sulfur mustard, its analogues, and nitrogen mustards can easily be collected in urine such as their hydrolysis products, albumin adducts require blood or plasma collection to be analyzed. Nonetheless, albumin adducts offer a wider period of detectability in human exposed patients than urine found biomarkers with detection up to 25 days after exposure to the chemical agent. The detection of these digested alkylated peptides of adducted albumin constitutes unambiguous proof of exposure. However, their determination, especially when they are present at very low concentration levels, can be very difficult due to the complexity of the biological matrices. Therefore, numerous sample preparation procedures to extract albumin and to recover alkylated peptides after a digestion step using enzymes have been proposed prior to the analysis of the targeted peptides by liquid chromatography coupled to mass spectrometry method with or without derivatization step. This review describes and compares the numerous procedures including a number of different steps for the extraction and purification of adducted albumin and its digested peptides described in the literature to achieve detection limits for biological samples exposed to sulfur mustard, its analogues, and nitrogen mustards in the ng/mL range.

Replicates Number for Drug Stability Testing during Bioanalytical Method Validation-An Experimental and Retrospective Approach

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.

Bioanalytical method validation: new FDA guidance vs. EMA guideline. Better or worse?

Bioanalysis concerns the identification and quantification of analytes in various biological matrices. Validation of any analytical method helps to achieve reliable results that are necessary for proper decisions on drug dosing and patient safety. In the case of bioanalytical methods, validation additionally covers steps of pharmacokinetic and toxicological studies - such as sample collection, handling, shipment, storage, and preparation. We drew our attention to the difference of both the newest FDA Guidance and the EMA Guideline on bioanalytical method validation. We aimed to point out advantages of both documents from the laboratory perspective. The FDA and the EMA documents are similar, but not identical. The EMA describes the practical conduct of experiments more precisely, while the FDA presents reporting recommendations more comprehensively. There are also differences in recommended validation parameters. We hope that the International Council for Harmonisation will combine advantages of both documents to avoid confusing differences in terminology as well as the unnecessary effort of being compliant with two or more guidelines.

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.

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.