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Quantification of Intact Monoclonal Antibody with LC-DAD

Ricardo Cunha

13 June, 2025

Source: vignettes/articles/demo_lc_dad_quantification_antibody.Rmd
demo_lc_dad_quantification_antibody.Rmd



Introduction

This document demonstrates the quantification of the intact monoclonal antibody Bevacizumab using Liquid Chromatography with Diode Array Detection (LC-DAD). The files include a blank, a calibration set with 10 points from 0.5 to 5 mg/L, a QC triplicate with 2.5 mg/L, and a sample in triplicate to be quantified. The data was acquired with an Agilent 1260 Infinity II system coupled to a DAD detecter. The raw data files are in Agilent’s .d format.

basename(files)
 [1] "0.5.d"                    "1.5.d"                   
 [3] "1.d"                      "2.5.d"                   
 [5] "2.d"                      "20240815_67_BVCZ_1.d"    
 [7] "20240815_67_BVCZ_2.d"     "20240815_67_BVCZ_3.d"    
 [9] "3.5.d"                    "3.d"                     
[11] "4.5.d"                    "4.d"                     
[13] "5.d"                      "Blank.d"                 
[15] "QC_BVCZ_1608_2_5mgmL_1.d" "QC_BVCZ_1608_2_5mgmL_2.d"
[17] "QC_BVCZ_1608_2_5mgmL_3.d"

Processing Engine

The StreamFind package provides a flexible and powerful framework for processing mass spectrometry data. In this example, we will use the MassSpecEngine class to handle the LC-DAD data. The engine will be initialized with the raw files, and we will specify that the data should be centroided and processed at level 1. Note that The msconvert from ProteoWizard was used in the background to convert the files to the open format mzML.

# Starts engine for quantification
ms <- StreamFind::MassSpecEngine$new(
  metadata = list(name = "Quantification of Bevacizumab"),
  analyses = files,
  centroid = TRUE,
  levels = 1
)

# Edit to assign analysis replicate groups
ms$add_replicate_names(
  c(
    "cal_0.5",
    "cal_1",
    "cal_1.5",
    "cal_2",
    "cal_2.5",
    rep("Sample", 3),
    "cal_3",
    "cal_3.5",
    "cal_4",
    "cal_4.5",
    "cal_5",
    "Blank",
    rep("QC_2.5", 3)
  )
)

# Concentration was obtained by attempting to convert the file names to numeric
# Alternatively, you can use the setter method ms$Analyses$concentrations to manually add the
# concentration values with NA_Real_ when the concentration is to be calculated
ms$Analyses$info[, c(1:2, 4, 8)]
                  analysis replicate   type concentration
                    <char>    <char> <char>         <num>
 1:                    0.5   cal_0.5     MS           0.5
 2:                      1     cal_1     MS           1.0
 3:                    1.5   cal_1.5     MS           1.5
 4:                      2     cal_2     MS           2.0
 5:                    2.5   cal_2.5     MS           2.5
 6:     20240815_67_BVCZ_1    Sample     MS            NA
 7:     20240815_67_BVCZ_2    Sample     MS            NA
 8:     20240815_67_BVCZ_3    Sample     MS            NA
 9:                      3     cal_3     MS           3.0
10:                    3.5   cal_3.5     MS           3.5
11:                      4     cal_4     MS           4.0
12:                    4.5   cal_4.5     MS           4.5
13:                      5     cal_5     MS           5.0
14:                  Blank     Blank     MS            NA
15: QC_BVCZ_1608_2_5mgmL_1    QC_2.5     MS            NA
16: QC_BVCZ_1608_2_5mgmL_2    QC_2.5     MS            NA
17: QC_BVCZ_1608_2_5mgmL_3    QC_2.5     MS            NA

Available Chromatograms 

chroms = data.frame("name" = unique(ms$get_chromatograms_headers()[["id"]]))
chroms
                        name
1                        TIC
2 DAD1 A: Sig=214,4  Ref=off
3 DAD1 B: Sig=254,4  Ref=off
4 DAD1 C: Sig=280,4  Ref=off
5 DAD1 D: Sig=194,4  Ref=off
6     QuatPump1 A:  Pressure
7         QuatPump1 B:  Flow

The chromatogram of interest is the DAD1 A: Sig=214,4 Ref=off, which is the second, and we know that our analyte elutes between 250 and 400 seconds.

Workflow 

ms$run(
  MassSpecMethod_LoadChromatograms_native(
    chromatograms = chroms$name[2],
    rtmin = 250,
    rtmax = 400
  )
)
ms$plot_chromatograms(colorBy = "replicates", normalized = FALSE)
ms$run(
  MassSpecMethod_SmoothChromatograms_movingaverage(
    windowSize = 3
  )
)

ms$run(
  MassSpecMethod_CorrectChromatogramsBaseline_airpls(
    lambda = 25,
    differences = 1,
    itermax = 20
  )
)
ms$plot_chromatograms(colorBy = "replicates", normalized = FALSE)
ms$run(
  MassSpecMethod_IntegrateChromatograms_pracma(
    merge = TRUE,
    closeByThreshold = 5,
    minPeakHeight = 10,
    minPeakDistance = 3,
    minPeakWidth = 5,
    maxPeakWidth = 50,
    minSN = 1
  )
)
ms$plot_chromatograms_peaks(colorBy = "replicates+targets")
# Note that concentration values of the calibration curve
# were automatically obtained by the file name
ms$run(
  MassSpecMethod_QuantifyChromatographicPeaks_native(
    calibration = NA_real_,
    value = "area",
    model = "linear"
  )
)
model <- ms$Chromatograms$calibration_model

fitted_values <- stats::predict(model)
r_squared <- summary(model)$r.squared

dt_cal <- data.table::data.table(
  fitted_values = fitted_values,
  calibration_values = model$model$calibration,
  values = model$model$values
)

fig <- plotly::plot_ly(
  model$model,
  x = ~calibration_values,
  y = ~values,
  type = 'scatter',
  mode = 'markers',
  name = "Data"
) %>%
  add_lines(x =  ~fitted_values, y = ~values, name = "Fitted Line") %>%
  layout(
    title = paste("Linear Regression (R² =", round(r_squared, 4), ")"),
    xaxis = list(title = "mg/L"),
    yaxis = list(title = "Intensity / counts"),
    showlegend = TRUE
  )

fig
peaks <- ms$get_chromatograms_peaks()
peaks[, c(1, 5, 17, 18, 19)]
                  analysis        peak     sn calibration concentration
                    <char>      <char>  <num>       <num>         <num>
 1:                    0.5 C1_P1_RT344  163.7         0.5     0.4694340
 2:                      1 C1_P1_RT344  182.9         1.0     0.9553192
 3:                    1.5 C1_P1_RT343  165.5         1.5     1.4402263
 4:                      2 C1_P1_RT343 1152.3         2.0     2.1156837
 5:                    2.5 C1_P1_RT343  632.0         2.5     2.5065439
 6:     20240815_67_BVCZ_1 C1_P1_RT343  911.3          NA     2.5607074
 7:     20240815_67_BVCZ_2 C1_P1_RT343  942.3          NA     2.6073833
 8:     20240815_67_BVCZ_3 C1_P1_RT343  772.7          NA     2.5706359
 9:                      3 C1_P1_RT343  692.9         3.0     3.0291738
10:                    3.5 C1_P1_RT343  713.8         3.5     3.6045620
11:                      4 C1_P1_RT342  841.4         4.0     4.0153155
12:                    4.5 C1_P1_RT342  724.4         4.5     4.4464318
13:                      5 C1_P1_RT342  670.5         5.0     4.9173097
14: QC_BVCZ_1608_2_5mgmL_1 C1_P1_RT343  675.3          NA     2.5316959
15: QC_BVCZ_1608_2_5mgmL_2 C1_P1_RT343  771.4          NA     2.7308170
16: QC_BVCZ_1608_2_5mgmL_3 C1_P1_RT343  988.2          NA     2.5849270