•Different calibrators have different impacts on accuracy of quadratic regression.•The lowest & highest concentrations plus their geometric mean are three critical ones.•Using these critical ...concentrations can improve the accuracy of quantitation.•Using these critical concentrations can significantly save time and costs.•Upper limit quality control must be used in each batch for quadratic regression.
Despite the common use of quadratic regression in LC–MS bioanalysis, how calibrator concentrations should be determined is still vague. Both the number and concentrations of calibrators are usually selected arbitrarily to each one's preference. The purposes of this research were to evaluate the impact of calibrator concentrations and to find new approaches with improved accuracy and reduced cost for LC–MS bioanalysis. It was found for the first time that the lower and upper limits of quantitation plus their geometric mean are the three critical concentrations for quadratic regression. When different concentration ranges, different response precisions, and various degrees of downward quadratic responses were simulated, the best accuracy was obtained by including these critical concentrations and using fewer calibrator concentrations with more replicates per concentration, instead of using more calibrator concentrations in duplicate. In many cases, when the aforementioned three concentrations are used, as few as two replicates per concentration are enough for routine use and up to 20% of time and cost can be saved. Furthermore, downward quadratic response should be eliminated or reduced as much as possible and upper limit quality control must be included in each batch to monitor the accuracy at the high concentration end. The retrospective data analysis of published experimental results corroborates the aforementioned findings. Finally, the typical “concerns” and potential applications of the new quadratic regression approaches are discussed.
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.
► Two-concentration linear calibration is better than multi-concentration one. ► One curve may have more failed runs and higher variation at low end than two curves. ► A new in-between approach is ...proposed to take advantage of the two common ones. ► Maximum tolerable quadratic behaviour in linear calibration has been determined. ► Two-concentration linear calibration has potential use in regulated bioanalysis.
Many different calibration approaches are used for linear calibration in LC–MS bioanalysis, such as different numbers of concentration levels and replicates. However, direct comparison of these approaches is rare, particularly using experimental results. The purpose of this research is to compare different linear calibration approaches (existing and new ones) through simulations and experiments. Both simulation and experimental results demonstrate that linear calibration using two concentrations (two true concentrations, not forced through zero) is as good as or even better than that using multiple concentrations (e.g. 8 or 10) in terms of accuracy. Additionally, two-concentration calibration not only significantly saves time and cost, but is also more robust. Furthermore, it has been demonstrated that the extrapolation of a linear curve at the high concentration end to a linearity-known region is acceptable. When multi-concentration calibration is used, the difference between the two commonly used approaches, i.e. singlet (one curve) or duplicate (two curves) standards per concentration level is small when a method is very precise. Otherwise, one curve approach can result in larger variation at the low concentration end and higher batch failure rate. To reduce the variation and unnecessary reassays due to batch failure or possible rejection of the lowest and/or highest calibration standards, a partially duplicate-standard approach is proposed, which has duplicate-standard-like performance but still saves time and cost as singlet-standard approach does. Finally, the maximum allowable degrees of quadratic (non-linear) response in linear calibration are determined for different scenarios. Because of its multiple advantages and potential application in regulated bioanalysis, recommendations as how to implement two-concentration linear calibration in practice are given and some typical “concerns” regarding linear calibration using only two concentrations are addressed, e.g. how does one know if the response is truly linear over a given range when only two concentrations are used?.
•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.
Many different calibration approaches are used for linear calibration in LCaMS bioanalysis, such as different numbers of concentration levels and replicates. However, direct comparison of these ...approaches is rare, particularly using experimental results. The purpose of this research is to compare different linear calibration approaches (existing and new ones) through simulations and experiments. Both simulation and experimental results demonstrate that linear calibration using two concentrations (two true concentrations, not forced through zero) is as good as or even better than that using multiple concentrations (e.g. 8 or 10) in terms of accuracy. Additionally, two-concentration calibration not only significantly saves time and cost, but is also more robust. Furthermore, it has been demonstrated that the extrapolation of a linear curve at the high concentration end to a linearity-known region is acceptable. When multi-concentration calibration is used, the difference between the two commonly used approaches, i.e. singlet (one curve) or duplicate (two curves) standards per concentration level is small when a method is very precise. Otherwise, one curve approach can result in larger variation at the low concentration end and higher batch failure rate. To reduce the variation and unnecessary reassays due to batch failure or possible rejection of the lowest and/or highest calibration standards, a partially duplicate-standard approach is proposed, which has duplicate-standard-like performance but still saves time and cost as singlet-standard approach does. Finally, the maximum allowable degrees of quadratic (non-linear) response in linear calibration are determined for different scenarios. Because of its multiple advantages and potential application in regulated bioanalysis, recommendations as how to implement two-concentration linear calibration in practice are given and some typical aconcernsa regarding linear calibration using only two concentrations are addressed, e.g. how does one know if the response is truly linear over a given range when only two concentrations are used?.
•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.
The 12th GCC Closed Forum was held in Philadelphia, PA, USA, on 9 April 2018. Representatives from international bioanalytical Contract Research Organizations were in attendance in order to discuss ...scientific and regulatory issues specific to bioanalysis. The issues discussed at the meeting included: critical reagents; oligonucleotides; certificates of analysis; method transfer; high resolution mass spectrometry; flow cytometry; recent regulatory findings and case studies involving stability and nonclinical immunogenicity. Conclusions and consensus from discussions of these topics are included in this article.
The topic of incurred sample stability (ISS) has generated considerable discussion within the bioanalytical community in recent years. The subject was an integral part of the seventh annual Workshop ...on Recent Issues in Bioanalysis (WRIB) held in Long Beach, CA, USA, in April 2013, and at the Global CRO Council for Bioanalysis (GCC) meeting preceding it. Discussion at both events focused on the use of incurred samples for ISS purposes in light of results from a recent GCC survey completed by member companies. This paper reports the consensus resulting from these discussions and serves as a useful reference for depicting ISS issues and concerns, summarizing the GCC survey results and providing helpful recommendations on ISS in the context of bioanalytical method development and application.
The 10th Global CRO Council (GCC) Closed Forum was held in Orlando, FL, USA on 18 April 2016. In attendance were decision makers from international CRO member companies offering bioanalytical ...services. The objective of this meeting was for GCC members to meet and discuss scientific and regulatory issues specific to bioanalysis. The issues discussed at this closed forum included reporting data from failed method validation runs, GCP for clinical sample bioanalysis, extracted sample stability, biomarker assay validation, processed batch acceptance criteria, electronic laboratory notebooks and data integrity, Health Canada's Notice regarding replicates in matrix stability evaluations, critical reagents and regulatory approaches to counteract fraud. In order to obtain the pharma perspectives on some of these topics, the first joint CRO-Pharma Scientific Interchange Meeting was held on 12 November 2016, in Denver, Colorado, USA. The five topics discussed at this Interchange meeting were reporting data from failed method validation runs, GCP for clinical sample bioanalysis, extracted sample stability, processed batch acceptance criteria and electronic laboratory notebooks and data integrity. The conclusions from the discussions of these topics at both meetings are included in this report.
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