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. 2024 Apr;16(7):149-163.
doi: 10.4155/bio-2023-0225. Epub 2024 Feb 22.

Comprehensive performance evaluation of ligand-binding assay-LC-MS/MS method for co-dosed monoclonal anti-SARS-CoV-2 antibodies (AZD7442)

Affiliations

Comprehensive performance evaluation of ligand-binding assay-LC-MS/MS method for co-dosed monoclonal anti-SARS-CoV-2 antibodies (AZD7442)

Yue Huang et al. Bioanalysis. 2024 Apr.

Abstract

Aims: AZD7442 is a combination SARS-CoV-2 therapy comprising two co-dosed monoclonal antibodies. Materials & methods: The authors validated a hybrid ligand-binding assay-LC-MS/MS method for pharmacokinetic assessment of AZD7442 in human serum with nominal concentration range of each analyte of 0.300-30.0 μg/ml. Results: Validation results met current regulatory acceptance criteria. The validated method supported three clinical trials that spanned more than 17 months and ≥720 analytical runs (∼30,000 samples and ∼3000 incurred sample reanalyses per analyte). The data generated supported multiple health authority interactions, across the globe. AZD7442 (EVUSHELD) was approved in 12 countries for pre-exposure prophylaxis of COVID-19. Conclusion: The results reported here demonstrate the robust, high-throughput capability of the hybrid ligand-binding assay-LC-MS/MS approach being employed to support-next generation versions of EVUSHELD, AZD3152.

Keywords: SARS-CoV-2; hybrid IA–LC–MS/MS; hybrid LBA–LC–MS/MS; incurred sample reanalysis; monoclonal antibody; pharmacokinetics; tixagevimab and cilgavimab; validation.

Plain language summary

The measurement of antibodies in human body fluids (e.g., blood, serum) has historically been tied to laboratory tests that may face operational limitations, including susceptibility to interference from other blood components and a reliance on unique reagents that can take months to produce. As such, there is a pursuit of alternative analytical methods to more accurately detect and measure antibody drugs from complex matrices. In the method, the authors describe different techniques that once combined were used to capture, separate, filter, fragment and then detect and measure the co-dosed antibody drugs. This method has been validated in accordance with current health authority guidelines and has been used to support three clinical trials that spanned more than 17 months; that is, the validated method was used to analyze nearly 30,000 serum samples from more than 2000 patients. Collectively, the results reported here demonstrate the robustness and high-throughput capability of this analytical approach.

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Conflict of interest statement

Authors Y Huang, CC Wang and AI Rosenbaum performed work within this manuscript while employed by AstraZeneca. The authors’ employment and stock investments in AstraZeneca do not affect the authenticity and objectivity of the experimental result detailed in this manuscript. Authors MS Woolf, SM Naser, AM Wheeler, WR Mylott and E Ma performed work within this manuscript while employed by PPD, a part of Thermo Fisher Scientific. The authors’ employment and stock investments in Thermo Fisher Scientific do not affect the authenticity and objectivity of the experimental result detailed in this manuscript. Responsibility for opinions, conclusions and data interpretation lies with the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the US government. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figures

Figure 1.
Figure 1.. Accuracy and precision for AZD1061 and AZD8895.
QCs were prepared at LLOQ, low-, mid- and high-QC levels; n = 6 replicates for each QC level per run (n = 3 validation runs). (A) Individual accuracy values for all QC replicates are depicted in raincloud plots consisting of jittered univariate dot plots, Tukey box-and-whisker plots and density (half-eye) plots. (B) Assay accuracy. Intra- and inter-run accuracy values: colored diamonds and solid black line, respectively. (C) Assay precision. Intra- and inter-run precision values: white diamonds and solid bars, respectively. LLOQ: Lower limit of quantification; QC: Quality control; DT: Difference from theoretical.
Figure 2.
Figure 2.. Stability and dilutional study results.
Stability data at low (0.600 μg/ml), mid (22.5 μg/ml) and over-the-curve (900 μg/ml) concentrations. Shaded grey bands denote the acceptance ranges for these studies (±20% from theoretical). (A–C) Stability stress conditions for samples that were thawed and stored on ice for 24 h, exposed to five freeze–thaw cycles from -25°C or -80°C, stored for 6 days at either -25°C or -80°C or refrigerated at 2–8°C for prolonged periods (postprep). (B) Data points for insufficient volume at the mid-QC level, 14 μg/ml diluted twofold. (C) Data points for dilutional linearity at the over-the-curve quality control level, 900 ng/ml diluted 40-fold. Post-prep: Post-preparation; QC: Quality Control.
Figure 3.
Figure 3.. Details of analytical runs and incurred sample reanalysis trends across the three clinical trials.
(A) Distribution of analytical runs across all three clinical trials. Stacked bar plots showing run performance (pass/fail) by clinical trial. (B) Distribution of ISR samples across all three clinical trials. Stacked bar plots showing ISR performance (pass/fail) by clinical trial. ISR: Incurred sample reanalysis.
Figure 4.
Figure 4.. Quality control performance data trends.
QC performance datasets for (A) AZD1061 and (B) AZD8895 include low, mid and high QCs for 676 analytical runs: 0.600, 14.00 and 22.50 µg/ml, respectively. For each analytical run, both replicates at each QC level are plotted. Horizontal dashed lines represent acceptance limits for these QCs (±20% difference from theoretical). Symbols with solid, black fill denote data points that fall outside acceptance limits. QC: Quality control.
Figure 5.
Figure 5.. Sequence-dependent (time-dependent) incurred sample reanalysis trend analysis.
Relative percent difference as a function of ISR sample index. As of October 2022, ISR pass rates for (A) AZD1061 and (B) AZD8895 were 98.17 and 98.27%, respectively. Vertical dashed lines appear at 3-month intervals. Black diamonds indicate ISR samples that fell outside acceptance limits. ISR: Incurred sample reanalysis.
Figure 6.
Figure 6.. Concentration-dependent incurred sample reanalysis trend analysis.
(A & B) Modified Bland–Altman plots of relative percent difference as a function of mean reportable concentration. Symbols with solid black fill denote data points that fall outside the acceptance limits. (C & D) Frequency distributions of mean reportable ISR sample concentrations: bin width fixed at 2 with first bin centered on 1.0 μg/ml. For AZD1061 and AZD8895, bin count dropped below 15 after 23.0 and 25.0 μg/ml, respectively (right y-axis). Binwise ISR pass rates for AZD1061 and AZD8895 are presented as point-to-point line graphs (right y-axis). (E & F) Histograms of ISR mean concentration were reconstructed as uniform frequency distributions with variable bin widths and equal, or roughly equal, bin counts (right y-axis). Binwise ISR pass rates for these reconstructed distributions are presented as point-to-point line graphs (left y-axis). (E) The histogram depicting AZD1061 ISR frequencies comprises 2997 observations that are split between 37 equally sized bins (81 observations each). (F) The histogram depicting AZD8895 ISR frequencies comprises 2938 observations that are split between 33 bins. In this histogram, the first 32 bins contain 89 observations each, while the last bin contains 90 observations. ISR: Incurred sample reanalysis.

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