Aimin Tan

Internal standard response variations during incurred sample analysis by LC–MS/MS: Case by case trouble-shooting

Internal standard (IS) responses can directly impact the accuracy of reported concentrations in bioanalysis as the majority of LC-MS/MS methods are based on analyte/IS response ratios for quantitation. Due to the complexity of incurred sample matrices and drug formulation, variable IS responses are quite common upon applying a validated method to the analysis of incurred samples. To maintain the integrity of a study and to avoid economic losses, it is therefore extremely important to monitor IS response variations during bioanalysis and to quickly identify the root causes if variations are observed. Presented in this article are twelve trouble-shooting examples from the analyses of incurred samples by a wide variety of bioanalytical methods, including human error, malfunctioning equipment/instruments, wrong material, matrix effect and inherent issues with a bioanalytical method. Insightful ideas for how to trouble-shoot and how to develop more reliable bioanalytical methods can be drawn from these practical examples.

Bioanalytical method development and validation using incurred samples—Simultaneous quantitation of ramipril and ramiprilat in human EDTA plasma by LC–MS/MS

A new method development and validation approach is proposed in order to develop a reliable method for the simultaneous quantitation of ramipril and ramiprilat in the presence of numerous labile metabolites. This new approach involves the usage of a synthesized labile acyl glucuronide of ramipril as well as individual and pooled incurred (study) samples in the development and validation process. Following the method validation and prior to its application to a large clinical study, a mini pilot study was performed to evaluate the performance of the method. When the samples from the mini pilot study were analyzed by two different scientists, 100% of the results from incurred sample reanalysis (ISR) matched within 8% of difference and the mean differences were 0.21% and 1.40% for ramipril and ramiprilat, respectively. The validated concentration range reported in this article is 0.2-80 ng/mL for both analytes. Various stabilities, such as bench-top, autosampler, freeze/thaw, and long-term, were also successfully evaluated. The key to the success were low sample processing temperature (4 degrees C), proper choice of sample extraction procedure, and adequate chromatographic conditions to obtain good peak shape without the need of derivatization and baseline separation between the analytes and their glucuronide metabolites.

Automated liquid–liquid extraction method for high-throughput analysis of rosuvastatin in human EDTA K2 plasma by LC–MS/MS

BACKGROUND Liquid-liquid extraction has been widely used for the analysis of rosuvastatin due to its many attractive features, such as low cost and clean extract. However, manual transfer of the organic phase poses a challenge, particularly when a batch size is large. To overcome the challenge, a simple automated high-throughput (192 samples per batch) liquid-liquid extraction method with short (3.0-min) chromatographic run time was proposed. Rosuvastatin was separated using a gradient on a reversed-phase C18 column and detected in the multiple reaction monitoring made with a mass transition of m/z 482.3→258.2 amu. RESULTS The assay exhibited a linear range from 50 to 25000 pg/ml (r ≥ 0.9976). The intra- and inter-day accuracy ranged from 98.16 to 103.84% and 101.18 to 103.95%, respectively. The intra- and inter-day precision ranged from 0.70 to 6.17% and 2.19 to 5.07%, respectively. CONCLUSION Finally, the validated method was successfully applied to bioequivalence studies.

Impact of hemolysis during sample collection: how different is drug concentration in hemolyzed plasma from that of normal plasma?

Hemolysis is a common phenomenon in clinical studies. Despite the growing interest in hemolysis matrix effect, how hemolysis impacts the representability of hemolyzed plasma samples was rarely evaluated. The purpose of this research is to perform such an evaluation by theoretical consideration and experiment. A formula for estimating the impact is proposed, which includes the degree of hemolysis and the drugs red blood cell (RBC): plasma concentration ratio. The impact of hemolysis on the representability of hemolyzed plasma samples is compound-dependant. Given the same degree of hemolysis, the stronger a drug binds to RBCs, the more significant the impact of hemolysis. For a drug with high affinity to RBCs, the results of hemolyzed plasma samples may not be useful even though they are accurate. There is an overall agreement between theoretical predication and experimental results. Among the ten different drug compounds tested, only methazolamide, which binds strongly to RBCs, showed significant change in plasma concentration due to hemolysis.

Beyond successful ISR: case-by-case investigations for unmatched reassay results when ISR passed

Incurred sample reanalysis (ISR) is now commonly practiced in regulated bioanalytical laboratories. With an average ISR success rate of 95% or higher and an increasing number of ISR tests being conducted, more and more situations deserve scientific evaluation or investigation for the unmatched reassay results revealed in ISR tests even though they meet the acceptance criteria. First, should an investigation be initiated when an ISR test is acceptable? How large a discrepancy or what situation would warrant an investigation? What would be the impact on a study? How would investigations regarding unmatched reassay results be conducted? What are the main root causes identified? Can normal random errors cause a large discrepancy in unfavorable combinations? How could the timeline and cost be affected? All these questions are addressed in this paper with five real case examples.

Some unnecessary or inadequate common practices in regulated LC–MS bioanalysis

The global bioanalytical community increasingly craves scientifically sound practices and guidance where the rationale is given for each requirement. To this end, it is critical to first evaluate all the existing practices and requirements based on scientific findings and critical thinking. Here we are challenging several important common practices in regulated LC-MS bioanalysis, from the requirement of at least six different calibration concentrations, no extrapolation, use of blank and zero standard in each batch, selection of quality controls, to the way matrix effect and dilution integrity are being validated. Both the reasons why these common practices are unnecessary or inadequate and the potential solutions are presented.

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC–MS bioanalytical projects

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well.

Internal Standards for Quantitative LC-MS Bioanalysis

Internal standards play critical roles in ensuring the accuracy of reported concentrations in LC-MS bioanalysis. How do you find an appropriate internal standard so that analyte losses and experimental variations during sample preparation, chromatographic separation, and mass spectrometric detection could be corrected? How is the concentration of an internal standard determined? Should internal standard responses be monitored during the analysis of incurred samples? What are the main causes for internal standard response variations? How do they impact the quantitation? Why are stable isotope labeled internal standards preferred? And yet one should still have an open-mind in their usage for the analysis of incurred samples. All these questions are addressed in this chapter supported by theoretical considerations and practical examples.

Large-scale retrospective evaluation of regulated liquid chromatography-mass spectrometry bioanalysis projects using different total error approaches.

The current approach in regulated LC-MS bioanalysis, which evaluates the precision and trueness of an assay separately, has long been criticized for inadequate balancing of lab-customer risks. Accordingly, different total error approaches have been proposed. The aims of this research were to evaluate the aforementioned risks in reality and the difference among four common total error approaches (β-expectation, β-content, uncertainty, and risk profile) through retrospective analysis of regulated LC-MS projects. Twenty-eight projects (14 validations and 14 productions) were randomly selected from two GLP bioanalytical laboratories, which represent a wide variety of assays. The results show that the risk of accepting unacceptable batches did exist with the current approach (9% and 4% of the evaluated QC levels failed for validation and production, respectively). The fact that the risk was not wide-spread was only because the precision and bias of modern LC-MS assays are usually much better than the minimum regulatory requirements. Despite minor differences in magnitude, very similar accuracy profiles and/or conclusions were obtained from the four different total error approaches. High correlation was even observed in the width of bias intervals. For example, the mean width of SFSTPs β-expectation is 1.10-fold (CV=7.6%) of that of Saffaj-Ihssanes uncertainty approach, while the latter is 1.13-fold (CV=6.0%) of that of Hoffman-Kringles β-content approach. To conclude, the risk of accepting unacceptable batches was real with the current approach, suggesting that total error approaches should be used instead. Moreover, any of the four total error approaches may be used because of their overall similarity. Lastly, the difficulties/obstacles associated with the application of total error approaches in routine analysis and their desirable future improvements are discussed.