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.
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
°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.
The 2014 8th Workshop on Recent Issues in Bioanalysis (8th WRIB), a 5-day full immersion in the evolving field of bioanalysis, took place in Universal City, California, USA. Close to 500 ...professionals from pharmaceutical and biopharmaceutical companies, contract research organizations and regulatory agencies worldwide convened to share, review, discuss and agree on approaches to address current issues of interest in bioanalysis. The topics covered included both small and large molecules, and involved LCMS, hybrid LBA/LCMS, LBA approaches and immunogenicity. From the prolific discussions held during the workshop, specific recommendations are presented in this 2014 White Paper. As with the previous years' editions, this paper acts as a practical tool to help the bioanalytical community continue advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2014 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 1) covers the recommendations for small molecule bioanalysis using LCMS. Part 2 (Hybrid LBA/LCMS, Electronic Laboratory Notebook and Regulatory Agencies' input) and Part 3 (Large molecules bioanalysis using LBA and Immunogenicity) will be published in the upcoming issues of Bioanalysis.
•Two-concentration linear regression is reliable for regulated LC–MS bioanalysis.•Two-concentration linear regression can typically save 15–20% of time and cost.•A minimum of six different ...concentrations is not necessary for linear regression.
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.
Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LCaMS 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 a0.3% to 0.7% and 0.1a0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of a0.6% to 1.8% and a0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of a0.7% to 0.9% and a0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of a0.8% 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 LCaMS bioanalysis and it significantly saves time and cost as well.
The 2017 11th Workshop on Recent Issues in Bioanalysis (11th WRIB) took place in Los Angeles/Universal City, California from 3 April 2017 to 7 April 2017 with participation of close to 750 ...professionals from pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day, weeklong event - A Full Immersion Week of Bioanalysis, Biomarkers and Immunogenicity. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small and large molecule analysis involving LCMS, hybrid LBA/LCMS and ligand-binding assay (LBA) approaches. This 2017 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2017 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 1) covers the recommendations for Small Molecules, Peptides and Small Molecule Biomarkers using LCMS. Part 2 (Biotherapeutics, Biomarkers and Immunogenicity Assays using Hybrid LBA/LCMS and Regulatory Agencies' Inputs) and Part 3 (LBA: Immunogenicity, Biomarkers and PK Assays) are published in volume 9 of Bioanalysis, issues 23 and 24 (2017), respectively.
The 2018 12
Workshop on Recent Issues in Bioanalysis (12th WRIB) took place in Philadelphia, PA, USA on April 9-13, 2018 with an attendance of over 900 representatives from ...pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day full immersion in bioanalysis, biomarkers and immunogenicity. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small- and large-molecule bioanalysis involving LC-MS, hybrid ligand binding assay (LBA)/LC-MS and LBA/cell-based assays approaches. This 2018 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2018 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 1) covers the recommendations for LC-MS for small molecules, peptides, oligonucleotides and small molecule biomarkers. Part 2 (hybrid LBA/LC-MS for biotherapeutics and regulatory agencies' inputs) and Part 3 (large molecule bioanalysis, biomarkers and immunogenicity using LBA and cell-based assays) are published in volume 10 of Bioanalysis, issues 23 and 24 (2018), respectively.
•Large-scale retrospective evaluation of GLP projects using total error approaches.•Comparison of different total error approaches using real data of GLP projects.•Risks of accepting unacceptable ...batches existed in regulated LC–MS bioanalysis.•Total error approach should be adopted in regulated LC–MS bioanalysis.•Different total error approaches usually led to similar conclusion.
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 SFSTP's β-expectation is 1.10-fold (CV=7.6%) of that of Saffaj–Ihssane's uncertainty approach, while the latter is 1.13-fold (CV=6.0%) of that of Hoffman–Kringle's 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.
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