Inclusion of dilution quality control samples in quantitative LC-MS bioanalysis

ABSTRACT

In LC-MS bioanalysis, sample dilution plays various roles, including bringing analyte concentrations within the validated/qualified dynamic range or alleviating matrix effect for accurate determination of the target analyte(s) in the intended study samples. Adherence to health authority requirements, incorporating good dilution practices, and timely demonstration of dilution integrity whenever samples are diluted in an analytical run are essential to ensure the reliability of bioanalytical results.

LC-MS bioanalysis has been widely employed for quantification of drugs and/or their metabolites in various biological matrices in support of toxicokinetic and pharmacokinetic assessment of drug candidates in development. In LC-MS bioanalysis, the calibration standards (Cs) and quality control samples (QCs) are ideally prepared in pre-screened species matrix blank to cover the intended dynamic range of the target analyte(s) in the study samples. In addition to regular QCs (i.e., QC low, QC mid, and QC high), dilution QCs (DQCs) are prepared at concentrations greater than the upper limit of quantification (ULOQ) of the respective LC-MS method.

Dilution integrity assessment is the evaluation of sample dilution procedure, when required, to demonstrate that it does not impact the accuracy of the measured analyte concentration(s) [1,2]. Therefore, adherence to the best practices is paramount to ensure reliable bioanalytical results. The current editorial is focused on the application and the best practices of inclusion of DQCs in LC-MS bioanalysis.

1. Sample dilution in LC-MS bioanalysis

Dilution of study samples is a widespread practice in LC-MS bioanalysis for various purposes:

1.1. Bring analyte concentration within the established dynamic range

The primary application of sample dilution in LC-MS bioanalysis is to bring the concentration of the target analyte(s) within the validated/qualified dynamic range prior to sample extraction for accurate quantification. This dilution can take place prior to initial sample analysis (pre-dilution) if the anticipated analyte concentrations in the study samples are higher than the established ULOQ (e.g., for studies with intravenous (iv.) administration, particularly rodent studies with limited sample volume) or in repeat analysis for the samples with initially measured concentrations above the ULOQ [3,4]. Good DQC results in this analysis/reanalysis help ascertain the accuracy of the measured results for the study samples after dilution. The demonstration of dilution integrity is extremely important for bioanalysts to address the potential query on the high analyte concentrations that are totally not expected based on historical data [3].

1.2. Mitigate matrix effect due to dosing vehicle

In drug development, dosing vehicles or solubilizing agents, such as polyethylene glycol or polysorbate 80, are often used in formulation of drug candidates in in vivo ADME or toxicity studies, especially for studies with iv. administration. Due to the surfactant nature, the solubilizing-agents can cause significant signal suppression for the target analyte and/or internal standard (IS) if necessary sample cleanup is not implemented and/or optimal chromatographic separation is not achieved. Unfortunately, stable isotope labeled IS is often not readily available at early stage of drug development to compensate for the signal suppression of the analyte. To mitigate the potential matrix effect due to solubilizing-agents, samples collected at early non-zero time points of iv. studies are often pre-diluted using control species matrix blank that does not contain solubilizing-agents [5].

1.3. Troubleshoot unexpected internal standard response

Dilution of study samples has been often employed to troubleshoot the ‘unexpected’ IS responses observed in the study samples vs those in Cs/QCs. In this exercise, the study samples with ‘unexpected’ IS responses are diluted with pre-screened control species matrix blank and reanalyzed, for which a dilution factor different from that employed in the initial analysis is highly recommended. If the IS responses of the diluted samples come back to normal and the analyte concentrations from the reanalysis are similar to the original values (e.g., within ±20%), the ‘unexpected’ IS responses in the original analysis can be judged to have no impact on the bioanalytical results. If the recorded IS responses of the diluted samples are still abnormal, additional analysis with a higher dilution factor should be conducted until the observed IS responses of the diluted samples becoming close to those of Cs/QCs. This is followed by examination of the repeated results vs. initial values before any further action(s) [6–9].

2. Demonstration of dilution integrity

The health authority (HA) requirements on dilution integrity have been evolved in the past two decades. The 2001 FDA guidance on bioanalytical method validation requests that the ability to dilute study samples originally above the ULOQ should be demonstrated by accuracy and precision parameters in validation [10]. This requirement was further reflected in the 2012 EMA guideline on bioanalytical method validation which states that dilution of samples should not affect the accuracy and precision. If applicable, dilution integrity should be demonstrated by spiking the matrix with an analyte concentration above the ULOQ and diluting this sample with blank matrix (at least five determinations per dilution factor). Accuracy and precision should be within the set criteria (i.e., within ±15% bias and ≤15% CV). Dilution integrity should cover the dilution applied to the study samples. Evaluation of dilution integrity may be covered by partial validation [11]. Late on, the 2018 FDA guidance on bioanalytical method validation specifies that DQCs should be included in the study sample analysis if not validated during method validation [12].

To comply with the above HA requirements, the industry has been diligently performing dilution integrity assessment during method validation (pre-study method validation). With release of the ICH M10 guidance in 2022, demonstration of dilution integrity in sample analysis for regulated studies has become mandatory, in other words, analytical runs containing samples that are diluted should include DQCs to verify the accuracy and precision of the dilution method during study sample analysis. The concentration of the DQCs should exceed that of the study samples being diluted (or of the ULOQ) and they should be diluted using the same dilution factor (as validated during method validation). If multiple dilution factors are used in one analytical run, then DQCs need only be diluted by the highest and lowest dilution factors [1]. The ICH M10 also highlights that the within-run acceptance criteria of the DQC(s) will only affect the acceptance of the diluted study samples and not the outcome of the analytical run [1].

In practice, whenever a sample or a group of samples from a regulated study needs to be diluted in an analytical run, DQCs with the same dilution factor(s) (e.g., 50-fold) and dilution scheme(s) (e.g., tenfold dilution followed by fivefold dilution) as employed for study sample dilution should be analyzed at least in triplicate. At least two out of three DQC results should meet the acceptance criteria (i.e., within ±15% bias). If a higher dilution factor than that validated during method validation is required, dilution integrity for this dilution factor must be assessed by including at least five replicates of the DQCs with the intended dilution factor in the respective sample analytical run or in a standalone method validation run. The same acceptance criteria as above (i.e., within ±15% bias and ≤15% CV) should be applied [1,11,12]. The outcome of this assessment should be documented in bioanalytical data report and/or method validation report. If more than one dilution factors are employed in an analytical run, dilution integrity should be, ideally, demonstrated by assessing DQCs at least in triplicate for each dilution factor. If too many samples must be diluted, the bioanalyst should consider extending the assay dynamic range (e.g., from 1.00–1000 ng/ml to 1.00–2000 ng/ml), for which new high QCs that appropriately reflect the study samples should be validated [1,11,12]. This is particularly important when supporting phase IIb trial and beyond, where drug candidate is tested clinically at the therapeutic dose level(s). If assay dynamic range could not be extended due to technical reason (e.g., carryover), re-validating the assay with a higher curve range (e.g., from 1.00–1000 ng/ml to 10.0–10,000 ng/ml) should be considered with new high QCs appropriately reflecting the study samples. Alternatively, DQCs covering the maximum analyte concentrations in study samples should be prepared and diluted with the same dilution factor(s) and scheme(s) as the study samples in sample analysis runs. As best practice, the stability of the target analyte(s), including freeze-thaw, bench-top and long-term stability, in the DQCs should be established [13].

It is worth noting that human error is still the main cause of bioanalytical failure [14]. Inclusion of DQCs provides additional quality control during sample analysis, particularly when large numbers of samples need to be diluted and/or there are high dilution factor(s) in an analytical run. Finally, it is recommended to expand assessment of dilution integrity to studies where qualified method is employed in support of non-regulated study or study portion, for which at least duplicate of DQCs should be assessed for each dilution factor and at least 50% of the DQC results should meet a wider acceptance criterion (e.g., ±20 or ±25% bias) [5].

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

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