Method for monitoring parameters in turbid solutions

Abstract

The present invention relates to a method for determining the concentration of an analyte in a sample obtained from a treated blood-derived plasma, the method comprising applying a light source in near infrared spectroscopy to a test sample obtained from a treated blood-derived plasma; the reflectance, transmittance, or translucency of the test sample in the range of near infrared wavelengths is measured to produce a test wavelength spectrum, which is compared to a reference wavelength spectrum obtained from a reference sample having a known concentration of the analyte to determine the concentration of the analyte in the sample. The invention also relates to the development of a multivariable model for determining the concentration of an analyte, such as total protein or alcohol, such as ethanol.

Description

Field of the Invention

[0001] The present invention relates to a method for in-line, at-line, off-line and / or on-line monitoring of parameters in turbid solutions or suspensions, and its use in a method for purifying solutions comprising proteins and other components.

[0002] Cross-reference to prior applications

[0003] This application claims priority from Australian provisional applications 2022901797 and 2022903894, the entire contents of each of which are incorporated herein by reference. Background of the Invention

[0005] The demand for purified proteins such as specific antibodies has increased considerably. Such purified proteins can be used for therapeutic and / or diagnostic purposes.

[0006] Human plasma has been used in industry for decades, for the preparation of plasma protein products widely established and accepted, such as human albumin (HSA), immunoglobulin (IgG), coagulation factor concentrates (coagulation factor VIII, coagulation factor IX, prothrombin complex, etc.) and inhibitors (antithrombin, C1 inhibitor, etc.). In the development process of such plasma-derived medicines, a plasma fractionation method has been established, resulting in an intermediate product enriched in certain protein fractions, which then serves as the starting composition of one or more plasma protein products. Common methods are summarized in, for example, Molecular Biology of Human Proteins (Schultze HE, Heremans JF; Volume 1: Nature and Metabolism of Extracellular Proteins 1966, Elsevier Publishing Company; Pages 236-317). These types of separation techniques allow the preparation of several therapeutic plasma protein products from the same plasma donor pool. This is economically advantageous compared to only preparing a plasma protein product from a donor pool, and has therefore been used as the industrial standard in plasma fractionation.

[0007] An example of this type of fractionation method is the cold ethanol fractionation of plasma, which was pioneered by E.J Cohn and his team during World War II and was mainly used for the purification of albumin (Cohn EJ, et al. 1946, J. Am. Chem. Soc. 62: 459-475). The Cohn fractionation method involves increasing the ethanol concentration from 0% to 40% in stages while reducing the pH from neutral (pH 7) to about 4.8, resulting in albumin precipitation. Although the Cohn fractionation has been developed over the past approximately 70 years, most commercial plasma fractionation methods are based on the original method or its variants (e.g., Kistler / Nitschmann), using differences in pH, ionic strength, solvent polarity, and alcohol concentration to separate plasma into a series of major precipitated protein fractions (e.g., fractions I to V in Cohn).

[0008] Variants of the Cohn fractionation method have been developed with the goal of increasing the polyvalent IgG recovery rate. For example, Oncley and colleagues used Cohn fraction II+III as starting material, using a combination of cold ethanol, pH, temperature and protein concentration different from those described by Cohn, to prepare active immunoglobulin serum fractions (Oncley et al., (1949) J.Am.Chem.Soc.71,541-550). Currently, the Oncley method is a classical method for preparing polyvalent IgG. However, it is known that approximately 5% of the γ-globulin (the antibody-enriched portion) is co-precipitated with fraction I, and approximately 15% of the total γ-globulin present in plasma is lost by the fraction II+III step (see Table III, Cohn EJ et al., 1946, J.Am.Chem.Soc.62:459-475). The Kistler / Nitschmann method aims to increase the IgG recovery rate by reducing the ethanol content of some precipitation steps (precipitate B vs fraction III). However, the increased yield comes at the expense of purity (Kistler & Nitschmann, (1962) Vox Sang. 7, 414-424).

[0009] Initially, the immunoglobulin G (IgG) products derived from these fractionation methods were successfully used in the prevention and treatment of various infectious diseases. However, because the ethanol fractionation is a relatively rough method, the IgG product contains impurities and aggregation, reaching the degree to which the IgG product can only be administered intramuscularly. Since then, other improvements in purification process have produced IgG products that are suitable for intravenous (being called IVIg) and subcutaneous (being called SCIg) administration.

[0010] It is estimated that in 2010, about 30 million liters of plasma were processed worldwide, providing a range of therapeutic products, including about 500 tons of albumin and 100 tons of IVIg. The IVIg market accounts for about 40-50% of the entire plasma fractionation market (P. Robert, Worldwide supply and demand of plasma and plasma derived medicines (2011) J. Blood and Cancer, 3, 111-120). Therefore, as the demand for IVIg remains strong (and the demand for SCIg increases), it is still necessary to improve the recovery rate of immunoglobulins from plasma and related fractions. Preferably, this must be achieved in a way that ensures that the recovery rate of therapeutic proteins from other plasma sources will not be adversely affected.

[0011] From a commercial perspective, the initial fractionation process is critical to the overall production time and cost associated with the production of therapeutic proteins, particularly plasma-derived proteins, since subsequent purification steps will depend on the yield and purity of the protein or proteins of interest within these initial fractions. Although several variations of the cold ethanol fractionation method have been developed for plasma-derived proteins to increase protein yield at lower operating costs, higher protein yields are generally associated with lower purity.

[0012] New and / or improved methods for determining the concentration of various analytes in complex solutions during plasma processing are needed to improve downstream efficiencies, reduce waste and / or increase final product yields. Due to the heterogeneity of plasma-derived product solutions and suspensions, quantification of key chemical components, such as proteins, is complicated and, to date, has only been achieved using off-line analytical methods that require sampling effort and typically several days of analytical lead time. Therefore, new analytical methods are needed to determine the concentration of analytes in turbid, especially highly turbid, solutions or suspensions.

[0013] The reference to any prior art in this specification is not an acknowledgement or suggestion that the prior art forms part of the common general knowledge in any jurisdiction, or that the prior art could reasonably be expected to be understood by a person skilled in the art to be relevant and / or combinable with other prior art. SUMMARY OF THE INVENTION

[0015] In one aspect, the invention provides a method for determining the concentration of an analyte in a sample obtained from processing blood-derived plasma, the method comprising:

[0016] - Application of light sources in the near infrared spectrum to measurements obtained from processed blood-derived plasma

[0017] Test samples;

[0018] -Measure the reflectance, transmittance or transflectance of the test sample in the near infrared wavelength range to generate a test wavelength spectrum,

[0019] -Comparing the test wavelength spectrum to a reference wavelength spectrum obtained from a reference sample having a known concentration of the analyte to determine the concentration of the analyte in the sample.

[0020] Typically, NIR spectra contain hundreds of variables, and therefore it is preferred to use some form of multivariate data analysis method to analyze the raw data from the measurements. Such multivariate data analysis methods are well known in the art and include partial least squares regression (PLS); PLS discriminant analysis (PLS-DA); ordinary least squares (OLS) regression; MLR (multivariate linear regression); OPLS (orthogonal-PLS); SVM (support vector machine); GLD (general discriminant analysis); GLMC (generalized linear models); GLZ (generalized linear and nonlinear models); LDA (linear discriminant analysis); classification trees; cluster analysis; neural networks; and Pearson correlation.

[0021] In one aspect, the invention provides a method for determining the concentration of an analyte in a sample obtained from processing blood-derived plasma, the method comprising:

[0022] - Application of light sources in the near infrared spectrum to measurements obtained from processed blood-derived plasma

[0023] Test samples;

[0024] -Measure the reflectance, transmittance or transflectance of the test sample in the near infrared wavelength range to generate a test wavelength spectrum,

[0025] - Comparing the test wavelength spectrum to a reference data set in the form of a model generated using multivariate analysis of processed reference wavelength spectra of reference samples (with known concentrations of analyte) to determine the concentration of the analyte in the sample.

[0026] In any aspect, the analyte is total protein or alcohol (eg, ethanol). In these embodiments, the method can then be used to determine the concentration of total protein or ethanol in a test sample obtained from processing blood-derived plasma.

[0027] In any respect, the preferred measurement mode is transflection.

[0028] In any embodiment, the light source in the near infrared spectrum comprises a light source with a wavelength in the range of about 750 to about 2500nm. In another embodiment, the light source comprises a wavelength of about 800 to 1100nm. In another embodiment, the light source comprises a wavelength of about 1100 to about 2500nm. Preferably, the light source comprises a wavelength of about 1400 to about 2200nm.

[0029] In yet other embodiments, the light source comprises a wavelength expressed in wave numbers, and the wave numbers are from about 4,000 to about 12,500 cm -1 .

[0030] In any embodiment, the wavelength spectrum includes the measured values ​​of reflectivity, transmittance or transreflectivity at the wavelength in the range of about 750 to about 2500nm. In another embodiment, the wavelength spectrum includes the measured values ​​at the wavelength of about 800 to 1100nm. In another embodiment, the wavelength spectrum includes the measured values ​​of reflectivity, transmittance or transreflectivity at the wavelength of about 1100 to about 2500nm. Preferably, the near infrared wavelength spectrum includes the measured values ​​at the wavelength of about 1400 to about 2200nm.

[0031] In yet other embodiments, the wavelength spectrum is expressed in wave numbers and the wave numbers are from about 4,000 to about 12,500 cm -1 .

[0032] In any aspect, model generation can include identifying signal changes in wavenumber regions of the spectrum.

[0033] In one embodiment, particularly when the analyte is total protein, the wavenumber region may include about 9'000 to about 7'500 cm -1 , about 6'900 to about 5'600cm -1 and about 4'935 to about 4'500cm -1 In one embodiment, the wave number region may include 9'000 to 7'500 cm -1 , 6'900 to 5'600cm -1 and 4'935 to 4'500cm -1 Any one or more of .

[0034] In one embodiment, particularly when the analyte is an alcohol such as ethanol, the wavenumber region may include about 9'4005'400 cm -1 or 9'4005'448cm -1 Any one or more of .

[0035] In one embodiment, the main water-derived signals are excluded. Typically, the main water-derived signals appear between 7'500 and 6'900 cm -1 Between 5'600 and 4'935cm -1 between.

[0036] In one embodiment, particularly when the analyte is total protein and a sample obtained from processing of Cohn fraction V (Fr V) or Kistler / Nitschmann precipitate C, the wavenumber region may include about 9'000 to about 7'500 cm -1 and / or about 6'000 to about 5'600 cm -1 In one embodiment, the wavenumber region may include 9'000 to 7'500 cm -1 and / or 6'000 to 5'600cm -1 .

[0037] In any aspect, the model of the processed reference wavelength spectra generated using the methods described herein is a model generated using partial least squares (PLS) regression of the processed wavelength spectra of samples having known concentrations of analytes.

[0038] In another aspect, the present invention provides a method for generating a model to determine the concentration of an analyte in a sample obtained from plasma processing, the method comprising:

[0039] - providing training samples obtained from processing blood-derived plasma, wherein the samples have known concentrations of analytes,

[0040] - apply a light source in the near infrared spectrum to the training samples,

[0041] - measuring the reflectance, transmittance or transflectance of the training sample in the near infrared wavelength range, thereby generating a training wavelength spectrum,

[0042] -selecting a spectral region of interest in the training wavelength spectrum;

[0043] - optionally applying at least one spectral pre-processing;

[0044] - Generate models by applying multivariate analysis to spectra to provide correlations with known concentrations of analytes,

[0045] Thus, a model for determining the concentration of an analyte in a sample obtained from the plasma of a processed blood source is obtained. Optionally, the multivariate analysis is selected from partial least squares regression (PLS); PLS discriminant analysis (PLS-DA); ordinary least squares (OLS) regression; MLR (multivariate linear regression); OPLS (orthogonal-PLS); SVM (support vector machine); GLD (general discriminant analysis); GLMC (generalized linear model); GLZ (generalized linear and nonlinear model); LDA (linear discriminant analysis); classification tree; cluster analysis; neural network; and Pearson correlation.

[0046] In any aspect, the training sample is obtained in the routine preparation of a blood-derived plasma product as further described herein, and such as includes immunoglobulins and other proteins derived from plasma, including albumin and coagulation factors.

[0047] In any aspect, the spectral pre-processing is: first order derivative, vector normalization or a combination of first order derivative, vector normalization. Alternatively, the spectral pre-processing is min-max normalization.

[0048] In any aspect, where the analyte is a protein, the concentration of the protein in a reference or training sample can be determined using any method known in the art, such as the Dumas assay, or any method described herein.

[0049] In any aspect, where the analyte is an alcohol such as ethanol, the concentration of the alcohol (e.g., ethanol) in a reference or training sample can be determined using any method known in the art, or using theoretical values, or any method described herein (e.g., gas chromatography or enzymatic ethanol determination).

[0050] In any aspect, the methods of the invention allow determination of protein concentrations in the range of about 10 g / kg to about 150 g / kg, 10 g / kg to 150 g / kg, about 15 g / kg to about 45 g / kg, 15 g / kg to 45 g / kg, about 20 g / kg to about 35 g / kg, 20 g / kg to 35 g / kg, about 100 g / kg to about 150 g / kg, or 100 g / kg to 150 g / kg. In one embodiment, where the protein in the test sample is primarily IgG or contains a significant amount of IgG, the protein concentration range may be about 15 g / kg to about 40 g / kg, 15 g / kg or 40 g / kg, about 16 g / kg to about 42 g / kg, 16 g / kg to 42 g / kg, about 20 g / kg to about 35 g / kg, or 20 g / kg to 35 g / kg. In one embodiment, where the protein in the test sample is primarily albumin or contains significant amounts of albumin, the protein concentration may range from about 100 g / kg to about 150 g / kg or from 100 g / kg to 150 g / kg.

[0051] In any aspect, the methods of the invention allow for determination of alcohol (e.g., ethanol) concentrations in the range of about 1% v / v to about 65% v / v, or 1% v / v to 65% v / v, or about 8% to about 40% v / v, or 8% to 40% v / v.

[0052] In any aspect, the method of the present invention allows for determination of total protein or ethanol concentrations in a range commonly used during fractionation of plasma, including preparation of any or all of Cohn fractions I, Cohn fractions (I+) II+III, Cohn fractions IV (including Cohn fractions IV1, IV4), and Cohn fractions V, as well as other similar variant fractions or precipitates. In addition, in any aspect, the method of the present invention allows for determination of total protein or ethanol concentrations in a range commonly used during fractionation of plasma, including preparation of any or all of Kistler / Nitschmann precipitate A, Kistler / Nitschmann precipitate B, Kistler / Nitschmann fraction IV, and Kistler / Nitschmann precipitate C, as well as other similar variant fractions or precipitates.

[0053] As used herein, Cohn fraction (I+) II+III includes Cohn fraction I+II+III or Cohn fraction II+III. It is also equivalent to Kistler / Nitschmann precipitate A and other similar variant fractions or precipitates.

[0054] As used herein, Cohn fraction IV includes Cohn fractions IV1 and IV4.

[0055] In any aspect, the methods of the invention allow for determination of a range of total protein or ethanol concentrations that are typically used during fractionation of plasma to prepare Cohn fraction V. Furthermore, in any aspect, the methods of the invention allow for determination of a range of ethanol concentrations that are typically used during fractionation of plasma to prepare one or both of Kistler / Nitschmann precipitates C.

[0056] In any aspect, the method of the present invention allows for determination of total protein or ethanol concentrations in a range typically used during dilution or resuspension of plasma fractions, including any one or all of Cohn fractions I, Cohn fractions (I+) II+III, Cohn fractions IV (including Cohn fractions IV1, IV4) and Cohn fractions V, as well as other similar variant fractions or precipitates. In addition, in any aspect, the method of the present invention allows for determination of total protein or ethanol concentrations in a range typically used during dilution or resuspension of plasma fractions, including any one or all of Kistler / Nitschmann precipitate A, Kistler / Nitschmann precipitate B, Kistler / Nitschmann fractions IV and Kistler / Nitschmann precipitate C, as well as other similar variant fractions or precipitates.

[0057] In one embodiment, total protein or ethanol concentration is measured during resuspension of any or all of Cohn Fraction I, Cohn Fraction II+III, Cohn Fraction I+II+III, or Kistler / Nitschmann precipitate A or other similar variant fractions or precipitates.

[0058] In one embodiment, total protein or ethanol concentration is measured during resuspension of a Cohn Fraction IV paste (including Cohn Fraction IV1, IV4, or other similar variant fractions or pellets).

[0059] In one embodiment, the total protein or ethanol concentration is measured after resuspension of any or all of Cohn Fraction I, Cohn Fraction II+III, Cohn Fraction I+II+III, or Kistler / Nitschmann Precipitate A paste, and before any filtration (e.g., clarification filtration) of the resuspended paste or any significant reduction in the turbidity of the resuspended paste.

[0060] In one embodiment, the total protein or ethanol concentration is measured after resuspension of a Cohn Fraction IV paste (including Cohn Fraction IV1, IV4 or other similar variant fractions or precipitates) and before any filtration (e.g., clarification filtration) of the resuspended paste or any significant reduction in the turbidity of the resuspended paste.

[0061] In one embodiment, Cohn fraction I, Cohn fraction (I+) II+III, Cohn fraction IV paste (including Cohn fraction IV1, IV4 or other similar fractions or precipitates), Kistler / Nitschmann precipitate A, Kistler / Nitschmann fraction IV or Kistler / Nitschmann precipitate B or other similar fractions or precipitates, pastes are resuspended by adding one or more diluents (e.g., distilled water). Typically, the paste is resuspended by adding one or more diluents at a ratio of diluent between 1-7 times the weight of the precipitate paste. In one embodiment, the paste is resuspended at a temperature below 26° C., including 25° C., 24° C., 23° C., 22° C., 21° C., 20° C., 19° C., 18° C., 17° C., 16° C., 15° C., 14° C., 13° C., 12° C., 11° C., 10° C., 9° C., 8° C., 7° C., 6° C., 5° C., 4° C., 3° ​​C., 2° C., 1° C., 0° C., -1° C., -2° C., -3° C., -4° C., -5° C., -6° C., -7° C., or -8° C. In one embodiment, the resuspension temperature is < 21° C.

[0062] In one embodiment, the ethanol concentration in the resuspended Cohn fraction I, Cohn fraction (I+) II+III, Cohn fraction IV paste (including Cohn fraction IV1, IV4 or other similar fractions or precipitates), Kistler / Nitschmann precipitate A, Kistler / Nitschmann fraction IV or Kistler / Nitschmann precipitate B or other similar fractions or precipitates, paste is between about 2% (w / w) to about 30% (w / w), about 2% (w / w) to about 20% (w / w), about 5% (w / w) to about 30% (w / w), about 5% (w / w) to about 20% (w / w), about 5% (w / w) to about 15% (w / w) or about 5% (w / w) to about 10% (w / w).

[0063] In one embodiment, the protein concentration in the resuspended Cohn fraction I, Cohn fraction (I+) II+III, Cohn fraction IV paste (including Cohn fraction IV1, IV4 or other similar fractions or precipitates), Kistler / Nitschmann precipitate A, Kistler / Nitschmann fraction IV or Kistler / Nitschmann precipitate B or other similar fractions or precipitates, paste is between about 5% (w / w) to about 15% (w / w), typically about 10% (w / w) to about 15% (w / w).

[0064] In one embodiment, a filter aid is optionally added to the resuspended Cohn fraction I, Cohn fraction (I+) II+III, Cohn fraction IV paste (including Cohn fraction IV1, IV4 or other similar fractions or precipitates), Kistler / Nitschmann precipitate A, Kistler / Nitschmann fraction IV or Kistler / Nitschmann precipitate B or other similar fractions or precipitates, paste before any filtration (e.g., clarification filtration) step or before any significant reduction in the turbidity of the resuspended paste.

[0065] In any aspect, the methods of the present invention allow for determination of a range of total protein or ethanol concentrations that are typically used during dilution or resuspension of Cohn fraction V. Furthermore, in any aspect, the methods of the present invention allow for determination of a range of ethanol concentrations that are typically used during dilution or resuspension of Kistler / Nitschmann precipitate C.

[0066] In one embodiment, total protein or ethanol concentration is measured during resuspension of the Cohn Fraction V paste or the Kistler / Nitschmann pellet C paste.

[0067] In one embodiment, the total protein or ethanol concentration is measured after resuspension of the Cohn fraction V paste or Kistler / Nitschmann precipitate C paste and before any filtration (eg, clarification filtration) of the resuspended paste or any significant reduction in the turbidity of the resuspended paste.

[0068] In one embodiment, the Cohn Fraction V paste or Kistler / Nitschmann Precipitate C paste is resuspended by adding one or more diluents, such as distilled water. Typically, the Cohn Fraction V paste or Kistler / Nitschmann Precipitate C paste is resuspended by adding one or more diluents at a ratio of diluent between 1-3 times the weight of the precipitate paste. In one embodiment, the Cohn Fraction V paste or Kistler / Nitschmann Precipitate C paste is resuspended at a temperature below 26°C, preferably at 25°C or below, at 24°C or below, at 23°C or below, at 22°C or below, at 21°C or below, at 20°C or below, at 19°C or below, at 18°C ​​or below, at 17 ... At or below 16°C, at or below 15°C, at or below 14°C, at or below 13°C, at or below 13°C, at or below 12°C, at or below 11°C, at or below 11°C, at or below 10°C, at or below 9°C, at or below 9°C, at or below 8°C, at or below 8°C, at or below 7°C, at or below 6°C, at or below 5°C, at or below 5°C, at or below 4°C, at or below 3°C, at or below 3°C, at or below 2°C, at or below 1°C or 0°C. In one embodiment, the resuspension temperature is <21°C.

[0069] In one embodiment, the ethanol concentration in the resuspended Cohn fraction V paste or Kistler / Nitschmann precipitate C paste ranges from about 5% (w / w) to about 15% (w / w), typically about 5% (w / w) to about 10% (w / w).

[0070] In one embodiment, the protein concentration in the resuspended Cohn fraction V paste or Kistler / Nitschmann precipitate C paste ranges from about 5% (w / w) to about 15% (w / w), typically about 10% (w / w) to about 15% (w / w).

[0071] In one embodiment, the filter aid is added to the resuspended Cohn Fraction V paste or Kistler / Nitschmann precipitate C paste prior to any filtration (eg, clarification filtration) step or prior to any significant reduction in the turbidity of the resuspended paste.

[0072] In another aspect, the invention provides a method for generating a model for determining the concentration of total protein or immunoglobulin G (IgG) during or after resuspension of Cohn fraction I, Cohn fraction II+III, Cohn fraction I+II+III, Cohn fraction IV paste, Kistler / Nitschmann precipitate A, or Kistler / Nitschmann precipitate B paste, the method comprising:

[0073] - Resuspend Cohn Fraction I, Cohn Fraction II+III, Cohn

[0074] Fraction I+II+III, Cohn Fraction IV paste, Kistler / Nitschmann precipitate A or Kistler / Nitschmann precipitate B paste to prepare a series of training samples containing different total protein or IgG concentrations, wherein the samples have known concentrations of total protein or IgG,

[0075] - apply a light source in the near infrared spectrum to the training samples,

[0076] - measuring the reflectance, transmittance or transflectance of the training sample in the near infrared wavelength range, thereby generating a training wavelength spectrum,

[0077] -selecting a spectral region of interest in the training wavelength spectrum;

[0078] - optionally applying at least one spectral pre-processing;

[0079] -By applying multivariate analysis to spectra to provide correlations with known concentrations of total protein or

[0080] The correlation of IgG was used to generate the model.

[0081] In another aspect, the invention provides a method for generating a model for determining the concentration of ethanol during or after resuspension of Cohn Fraction I, Cohn Fraction II+III, Cohn Fraction I+II+III, Cohn Fraction IV paste, Kistler / Nitschmann Precipitate A, or Kistler / Nitschmann Precipitate B paste, the method comprising:

[0082] - Resuspend Cohn Fraction I, Cohn Fraction II+III, Cohn

[0083] Fraction I+II+III, Cohn fraction IV paste, Kistler / Nitschmann precipitate A or Kistler / Nitschmann precipitate B paste and stir until the precipitate is dissolved,

[0084] - optionally adding ethanol stepwise to the resuspension and taking a training sample after each stepwise addition to prepare a series of training samples comprising different ethanol concentrations, wherein the samples have a known concentration of ethanol,

[0085] - apply a light source in the near infrared spectrum to the training samples,

[0086] -Measure the reflectance, transmittance or transflectance of the training sample in the near-infrared wavelength range to generate training waves

[0087] Long spectrum;

[0088] -selecting a spectral region of interest in the training wavelength spectrum;

[0089] - optionally applying at least one spectral pre-processing;

[0090] - A model was generated by applying multivariate analysis to the spectra to provide correlations with known concentrations of ethanol.

[0091] In another aspect, the invention provides a method for generating a model for determining the concentration of ethanol during or after resuspension of Cohn Fraction I, Cohn Fraction II+III, Cohn Fraction I+II+III, Cohn Fraction IV paste, Kistler / Nitschmann Precipitate A, or Kistler / Nitschmann Precipitate B paste, the method comprising:

[0092] - Resuspend Cohn Fraction I, Cohn Fraction II+III, Cohn

[0093] Fraction I+II+III, Cohn fraction IV paste, Kistler / Nitschmann precipitate A or Kistler / Nitschmann precipitate B paste and stir until the precipitate is dissolved,

[0094] - Applying a light source in the near infrared spectrum to the resuspension;

[0095] - optionally, adding ethanol stepwise to the resuspension and measuring the reflectance, transmission or transflectance in the near infrared wavelength range after each step of ethanol addition, thereby generating a training wavelength spectrum,

[0096] -selecting a spectral region of interest in the training wavelength spectrum;

[0097] - optionally applying at least one spectral pre-processing;

[0098] - A model was generated by applying multivariate analysis to the spectra to provide correlations with known concentrations of ethanol.

[0099] In one aspect, the invention provides a method for determining the concentration of total protein, IgG or ethanol during or after resuspension of Cohn fraction I, Cohn fraction II+III, Cohn fraction I+II+III, Cohn fraction IV paste, Kistler / Nitschmann precipitate A or Kistler / Nitschmann precipitate B paste for purifying albumin, the method comprising:

[0100] - applying a light source in the near infrared spectrum to test samples obtained at a time point during or upon completion of the resuspension of the paste in a suitable diluent,

[0101] - measuring the reflectance, transmittance or transflectance of the test sample in the near infrared wavelength range, thereby generating a test wavelength spectrum,

[0102] - Comparing the test wavelength spectrum to a reference data set in the form of a model generated using multivariate analysis of processed reference wavelength spectra of reference samples with known concentrations of total protein, albumin or ethanol to determine the concentration of total protein, albumin or ethanol in the sample.

[0103] In another aspect, the invention provides a method for generating a model for determining the concentration of total protein or albumin during or after resuspension of a Cohn fraction V paste or a Kistler / Nitschmann precipitate C paste, the method comprising:

[0104] - Resuspend the Cohn Fraction V paste with a suitable diluent or

[0105] Kistler / Nitschmann precipitate C paste to prepare a series of training samples containing different total protein or albumin concentrations, where the samples have known concentrations of total protein or albumin,

[0106] - apply a light source in the near infrared spectrum to the training samples,

[0107] - measuring the reflectance, transmittance or transflectance of the training sample in the near infrared wavelength range, thereby generating a training wavelength spectrum,

[0108] -selecting a spectral region of interest in the training wavelength spectrum;

[0109] - optionally applying at least one spectral pre-processing;

[0110] - Models were generated by applying multivariate analysis to the spectra to provide correlations with known concentrations of total protein or albumin.

[0111] In another aspect, the invention provides a method for generating a model for determining the concentration of ethanol during or after resuspension of a Cohn fraction V paste or a Kistler / Nitschmann precipitate C paste, the method comprising:

[0112] - Resuspend the Cohn Fraction V paste with a suitable diluent or

[0113] Kistler / Nitschmann precipitate C paste and stir until the precipitate is dissolved,

[0114] - optionally adding ethanol stepwise to the resuspension and taking a training sample after each stepwise addition to prepare a series of training samples comprising different ethanol concentrations, wherein the samples have a known concentration of ethanol,

[0115] - apply a light source in the near infrared spectrum to the training samples,

[0116] - measuring the reflectance, transmittance or transflectance of the training sample in the near infrared wavelength range, thereby generating a training wavelength spectrum,

[0117] -selecting a spectral region of interest in the training wavelength spectrum;

[0118] - optionally applying at least one spectral pre-processing;

[0119] - A model was generated by applying multivariate analysis to the spectra to provide correlations with known concentrations of ethanol.

[0120] In another aspect, the invention provides a method for generating a model for determining the concentration of ethanol during or after resuspension of a Cohn fraction V paste or a Kistler / Nitschmann precipitate C paste, the method comprising:

[0121] - Resuspend the Cohn Fraction V paste with a suitable diluent or

[0122] Kistler / Nitschmann precipitate C paste and stir until the precipitate is dissolved,

[0123] - Applying a light source in the near infrared spectrum to the resuspension;

[0124] - Optionally, ethanol is added stepwise to the resuspension and the reflectance, transmittance and transmittance in the near infrared wavelength range are measured after each step of ethanol addition.

[0125] (transmission) or transflectance, thereby generating a training wavelength spectrum,

[0126] -selecting a spectral region of interest in the training wavelength spectrum;

[0127] - optionally applying at least one spectral pre-processing;

[0128] - A model was generated by applying multivariate analysis to the spectra to provide correlations with known concentrations of ethanol.

[0129] In one aspect, the present invention provides a method for determining the concentration of total protein, albumin or ethanol during or after resuspension of a Cohn fraction V paste or a Kistler / Nitschmann precipitate C paste for purification of albumin, the method comprising:

[0130] - applying a light source in the near infrared spectrum to test samples obtained at a time point during or upon completion of the resuspension of the paste in a suitable diluent,

[0131] - measuring the reflectance, transmittance or transflectance of the test sample in the near infrared wavelength range, thereby generating a test wavelength spectrum,

[0132] - Comparing the test wavelength spectrum to a reference data set in the form of a model generated using multivariate analysis of processed reference wavelength spectra of reference samples with known concentrations of total protein, albumin or ethanol to determine the concentration of total protein, albumin or ethanol in the sample.

[0133] In any aspect, the training samples include concentrations of the analyte within the concentration range determined for the test sample. For example, if the possible concentration of the analyte in the test sample is in the range of X g / kg to Y g / kg, the training samples include concentrations of the analyte between X g / kg and Y g / kg and between X g / kg and Y g / kg.

[0134] In any aspect, the training sample and the test sample are exposed to a light source in the near infrared spectrum at a temperature in the range of about -8°C to about 37°C or -8°C to 37°C. Typically, the temperature is in the range of about 10°C to about 37°C, preferably in the range of about 15°C to about 30°C. The temperature can be about 15°C, about 16°C, about 17°C, about 18°C, about 19°C, about 20°C, about 21°C, about 22°C, about 23°C, about 24°C, about 25°C, about 26°C, about 27°C, about 28°C, about 29°C, or about 30°C. In any embodiment, the temperature is 18°C, 19°C, 20°C, 21°C, 22°C, 23°C, or 24°C.

[0135] In any aspect, a light source in the near infrared range is applied to a training sample and / or a test sample using a probe adapted to emit light having a wavelength in the near infrared range. Optionally, the probe is configured for inclusion in an industrial protein mixing, filtration or purification apparatus, including for online measurement of reflectance, transmittance or transflectance of a training sample in the near infrared wavelength range.

[0136] In any aspect, a light source in the near infrared range is applied to the training sample and / or the test sample during mixing of the samples. The light source may be applied to the one or more samples at an angle parallel to the direction of fluid flow during mixing of the one or more samples. Alternatively, the light source may be applied to the one or more samples at an angle not parallel to the direction of fluid flow during mixing of the one or more samples, for example, the light source may be applied to the one or more samples at 45° or about 45° to the direction of fluid flow during mixing of the one or more samples.

[0137] In any case, the quality of the generated model can be judged using the following statistical parameters:

[0138] the number of latent variables (PLS factors) in the model,

[0139] ·deviation,

[0140] RMSECV,

[0141] RMSEP of independent test samples,

[0142] ·R 2 , and / or

[0143] RPD value.

[0144] In any aspect, the sample containing the analyte is obtained from the plasma of the processing blood source, and the plasma is obtained from any plasma sample including blood, preferably human blood source. In certain embodiments, the sample is obtained or derived from the plasma of the processing blood source, and the plasma includes fresh plasma, cryo-poor plasma or cryo-rich plasma. In other words, the source of the plasma can be blood, preferably human blood, preferably fresh plasma, cryo-poor plasma or cryo-rich plasma. The plasma can be obtained from a large number of donors and / or subjects, and pooled. The plasma can be hyperimmune plasma.

[0145] In any aspect, the sample comprising the analyte is a resuspension of a precipitate or paste obtained from blood-derived plasma, and as further described herein.

[0146] In any aspect, the sample contains caprylic acid. Thus, the sample containing caprylic acid also contains blood-derived plasma, or is obtained or derived from processing blood-derived plasma.

[0147] In any aspect of the present invention, the sample containing the analyte is a plasma fraction (intermediate). In a specific embodiment, the fraction is a Cohn fraction. In a particularly preferred embodiment, the plasma fraction is selected from the group consisting of Cohn fraction I (Fr I), Cohn fraction II+III (Fr II+III), Cohn fraction I+II+III (Fr I+II+III), Cohn fraction II (Fr II), Cohn fraction III (Fr III), Cohn fraction IV (Fr IV), Cohn fraction V (Fr V), Kistler / Nitschmann precipitate A, Kistler / Nitschmann precipitate B, Kistler / Nitschmann precipitate C. In another embodiment, the plasma fraction is selected from the group consisting of Cohn fraction I (Fr I), Cohn fraction II+III (Fr II+III), Cohn fraction I+II+III (Fr I+II+III), or Kistler / Nitschmann precipitate A (KNA, PPT A or FrA). The plasma fraction can be a combination of different fractions. For example, the plasma fraction can be a combination of KNA and one or more of Fr I, Fr II+III, and Fr I+II+III.

[0148] In any aspect, the sample comprising the analyte may comprise a filter aid (eg, diatomaceous earth and perlite; or cellulose or silica gel).

[0149] In any aspect, the sample comprising the analyte is a turbid solution or suspension. In any embodiment, the turbid solution or suspension may have the following nephelometric turbidity units (NTU): equal to or greater than 10 NTU, equal to or greater than 15 NTU, equal to or greater than 20 NTU, equal to or greater than 25 NTU, equal to or greater than 30 NTU, equal to or greater than 35 NTU, equal to or greater than 40 NTU, equal to or greater than 45 NTU, equal to or greater than 50 NTU, equal to or greater than 55 NTU, equal to or greater than 60 NTU, equal to or greater than 65 NTU, equal to or greater than 70 NTU. , equal to or greater than 75NTU, equal to or greater than 80NTU, equal to or greater than 85NTU, equal to or greater than 90NTU, equal to or greater than 95NTU, equal to or greater than 100NTU, equal to or greater than 150NTU, equal to or greater than 200NTU, equal to or greater than 250NTU, equal to or greater than 300NTU, equal to or greater than 350NTU, equal to or greater than 400NTU, equal to or greater than 450NTU, equal to or greater than 500NTU, equal to or greater than 550NTU, equal to or greater than 600NTU or more, 650NTU or more, 700NTU or more, 750NTU or more, 800NTU or more, 850NTU or more, 900NTU or more, 950NTU or more, 1,000NTU or more, 1,500NTU or more, 2,000NTU or more, 2,500NTU or more, 3,000NTU or more, 3,500NTU or more, 4,600NTU or more, 5,700NTU or more, 6,800NTU or more, 7,900NTU or more, 8,100NTU or more, 9,100NTU or more, 1,200NTU or more, 1,300NTU or more, 1,400NTU or more, 1,500NTU or more, 1,600NTU or more, 1,700NTU or more, 1,800NTU or more, 1,900NTU or more, 2,000NTU or more, 2,500NTU or more, 3,000NTU or more, 3,500NTU or more, 4,600NTU or more, 5,700NTU or more, 6,800NTU or more, 7,900NTU or more, 8,1 ... or more than 4,000 NTU, 4,500 NTU, 5,000 NTU, 5,500 NTU, 6,000 NTU, 6,500 NTU, 7,000 NTU, 7,500 NTU, 8,000 NTU, 8,500 NTU, 9,000 NTU, 9,500 NTU, or 10,000 NTU. In any embodiment, the turbid solution or suspension may have an NTU of 10 to 100 NTU, 10 to 90 NTU, 10 to 80 NTU, 10 to 70 NTU, 10 to 60 NTU, 10 to 50 NTU, 10 to 40 NTU, 10 to 30 NTU, 10 to 20 NTU, 20 to 100 NTU, 30 to 100 NTU, 40 to 100 NTU, 50 to 100 NTU, 60 to 100 NTU, 70 to 100 NTU, 80 to 100 NTU, or 90 to 100 NTU.In any embodiment, the turbid solution can have a maximum NTU of 10,000 NTU, 9,500 NTU, 9,000 NTU, 8,500 NTU, 8,000 NTU, 7,500 NTU, 7,000 NTU, 6,500 NTU, 6,000 NTU, 5,500 NTU, 5,000 NTU, 4,500 NTU, 4,000 NTU, 3,500 NTU, 3,000 NTU, 2, 500NTU, 2,000NTU, 1,500NTU, 1000NTU, 950NTU, 900NTU, 850NTU, 800NTU, 750NTU, 700NTU, 650NTU, 600NTU, 550NTU, 500NTU, 450NTU, 400NTU, 350NTU, 300NTU, 250NTU, 200NTU, 150NTU, 100NTU, or 50NTU.

[0150] In any aspect or embodiment, any or all steps of the method are performed in-line, at-line, off-line and / or in-line.

[0151] Those skilled in the art will understand and appreciate that the plasma fractionation process has some adaptability and has been optimized and changed over the years, for example, to accommodate different manufacturers and different product performance targets. An example of such a modification is the presence or absence of a Cohn fractionation step IV-1 that can be used to extract alpha-1-antitrypsin. Therefore, it should be understood that the methods and products described herein can be practiced with modifications and variations to the human plasma fractionation process, and such modifications and variations are included within the scope of the present disclosure.

[0152] In a preferred embodiment, the training samples include a set of representative samples covering variables such as different paste types, sample temperatures, instrument variability, operator handling, raw materials, and plasma sources.

[0153] As used herein, the term "comprise" and variations of the term such as "comprising," "comprises," and "comprised" are not intended to exclude additional additives, components, integers or steps, unless the context requires otherwise.

[0154] Further aspects of the invention and further embodiments of the aspects described in the preceding paragraphs will become apparent from the following description, which is given by way of example and with reference to the accompanying drawings.

[0155] BRIEF DESCRIPTION OF THE DRAWINGS

[0156] Figure 1: Top: Raw NIR spectra of a training set measured with transflectance (black) and reflection (grey) probes. Bottom: Preprocessed (vector normalization + first derivative) spectra.

[0157] Figure 2a :Mod gen Figure 2. Graphical overview of protein concentration predicted by NIR (y-axis) vs. reference protein concentration (Dumas; x-axis) in g / kg. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​br represent a range of 16 g / kg. Y-axis values ​​bk represent a range of 18 g / kg.

[0158] Figure 2b :Mod lab Figure 1. Graphical overview of protein concentration predicted by NIR (y-axis) vs. reference protein concentration (Dumas; x-axis) in g / kg. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​bo represent a range of 26 g / kg. Y-axis values ​​ag represent a range of 30 g / kg.

[0159] Figure 3 : By Mod gen Predicted mean protein concentration of samples in the test set in g / kg, with X-axis values ​​ah representing a range of 14 g / kg and Y-axis values ​​ad representing a range of 15 g / kg.

[0160] Figure 4: NIR spectra when resuspending sediment A (Fr A or PPT A) at 20°C. A. Original spectra. B. Spectra preprocessed with first derivative + vector normalization. Grey: addition of paste until the temperature reaches 20°C; black: resuspension. C. Protein concentrations [in g / kg] predicted using the NIR-based model, compared with protein concentrations determined according to Dumas, between 2 and 24 hours after resuspension.

[0161] Figure 5: NIR spectra when resuspending Cohn fraction I+II+III (Fr I+II+III) at 20°C. A. Original spectra. B. Spectra preprocessed with first derivative + vector normalization. Grey: addition of paste until the temperature reaches 20°C; black: resuspension. C. Protein concentrations [in g / kg] predicted using the NIR-based model, compared to protein concentrations determined according to Dumas, between 2 and 24 hours after resuspension.

[0162] Figure 6: NIR spectra when resuspending Cohn fraction II+III (Fr II+III) at 20°C. A. Original spectra. B. Spectra preprocessed with first derivative + vector normalization. Grey: addition of paste until the temperature reaches 20°C; black: resuspension. C. Protein concentrations [in g / kg] predicted using the NIR-based model, compared to protein concentrations determined according to Dumas, between 2 and 24 hours after resuspension.

[0163] Figure 7 : For preparative scale plasma fractionation of Cohn fractions (I+) II+III and Cohn fractions IV, Model comp First derivative + vector normalization of preprocessed spectra.

[0164] Figure 8 :Use NIR-based Model comp Predicted ethanol concentrations compared to theoretical ethanol concentrations [in % v / v]. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​aj represent 45% of the range. Y-axis values ​​bg represent 50% of the range.

[0165] Fig. 9 :For the preparative scale plasma fractionation Cohn fraction (I+) II+III, Model I+II+III First derivative + vector normalization of preprocessed spectra.

[0166] Fig.10 :Use NIR-based Model I+II+III Predicted ethanol concentrations compared to theoretical ethanol concentrations [in % v / v]. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​ag represent a 30% range. Y-axis values ​​bh represent a 30% range.

[0167] Fig.11 : For preparative scale plasma fractionation Cohn Fraction IV, Model IV First derivative + vector normalization of preprocessed spectra.

[0168] Fig.12 :Use NIR-based Model IV Predicted ethanol concentrations compared to theoretical ethanol concentrations [in % v / v]. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​aj represent a range of 18%. Y-axis values ​​aj represent a range of 18%.

[0169] Fig.13 :For ModelAlbresusp NIR raw spectra of the training set.

[0170] Fig.14 :Model Albresusp Graph overview of; protein concentration predicted by NIR (y-axis) vs. reference protein concentration (Dumas; x-axis) in g / kg. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​bk represent a range of 45 g / kg. Y-axis values ​​bk represent a range of 45 g / kg.

[0171] Fig.15 : Use Model Albresusp Predicted protein concentrations of the independent test set (y-axis), compared to the reference protein concentration (Dumas; x-axis), in g / kg. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​ae represent a range of 20 g / kg. Y-axis values ​​be represent a range of 15 g / kg.

[0172] Fig.16 :For Model EtOH Preprocessed (first derivative) NIR raw spectra of the training set.

[0173] Fig.17 :A) Model EtOH Figure 2. Graphical overview of the results; ethanol concentration predicted by NIR (y-axis) vs. reference ethanol concentration (x-axis) in % w / w. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​bf represent a range of 4% w / w. Y-axis values ​​bf represent a range of 4% w / w. B) Using Model EtOH Predicted ethanol concentrations for an independent test set (y-axis) compared to a reference ethanol concentration (x-axis) in % w / w. Each spectrum is represented by a single data point. Solid black line: predicted value = true value (slope = 1). X-axis values ​​bh represent a range of 6% w / w. Y-axis values ​​cg represent a range of 4% w / w.

[0174] Fig.18 : During the resuspension of Kistler-Nitschmann pellet C (squares, 2 runs) and Cohn pellet V (crosses), the EtOH Real-time ethanol concentration prediction by online NIRS. Y-axis values ​​ad represent the range of 6% w / w.

[0175] Detailed description of implementation plan

[0176] Reference will now be made in detail to certain embodiments of the present invention. Although the present invention will be described in conjunction with the embodiments, it will be appreciated that the present invention is not limited to those embodiments. On the contrary, the present invention is intended to cover all substitutions, modifications and equivalents that may be included within the scope of the present invention as defined in the claims.

[0177] Those skilled in the art will recognize that many methods and materials similar or equivalent to those described herein can be used to implement the present invention. The present invention is by no means limited to the methods and materials described. It should be understood that the present invention disclosed and defined in this specification extends to all combinations of two or more individual features mentioned or clearly visible from the text or drawings for selection. All these different combinations constitute various aspects of the present invention for selection.

[0178] All patents and publications cited herein are incorporated by reference in their entirety.

[0179] For the purpose of interpreting this specification, terms used in the singular will also include the plural and vice versa.

[0180] Due to the heterogeneity of plasma-derived product solutions and suspensions, quantification of key chemical components such as proteins or alcohols (e.g. ethanol) is complicated and, to date, has only been achieved by using offline analytical methods. This can significantly impact process efficiency.

[0181] The present invention seeks to address some of the deficiencies of existing methods for processing plasma-derived products by providing an on-line system for determining the concentration of various analytes in complex solutions during plasma processing. The methods of the present invention have the advantages of increasing downstream efficiency, reducing waste and / or increasing final product yield.

[0182] The method is also capable of quantifying analytes in various starting materials used during the preparation of blood-plasma derived products without the need for prior sample preparation as required by current online procedures. An additional benefit of the method of the present invention is the ability to monitor the progress of product processing (such as resuspension) and other reactions in real time, resulting in reduced cycle time. The invention defined herein has been applied to manufacturing scale production to determine analyte concentrations.

[0183] definition

[0184] The term "sample obtained from the treatment of blood-derived plasma" is intended to refer to any material, especially protein-containing material, derived from fractionated or treated plasma. The sample may be a suspension or a concentrate or a filtrate of a "precipitate containing protein", wherein the "precipitate containing protein" is derived from plasma.

[0185] It should be understood that samples (including test samples) analyzed according to the methods described herein do not need to be "separated" samples. In other words, the term "sample" is intended to represent only a small portion or a small amount of a larger whole or body. Therefore, the methods of the present invention are intended to include at-line, in-line, and off-line methods, whereby a light source in the near infrared spectrum is applied to a small portion of a larger body solution, and wherein the light source can be applied in situ to a small portion of the body solution, or to an aliquot of a solution that has been removed (separated) from the larger body.

[0186] As used herein, the term "in-line" refers to an analytical method in which a probe or sampling interface or sensor (e.g., for providing a light source in the near infrared spectrum) can be placed directly in a processing vessel or in-line with a flowing material stream for analysis. The method can include placing the probe in a flow system or bioreactor. This method can allow analysis without having to remove the probe or any material or sample from the batch (i.e., the sample remains "in situ" for analysis).

[0187] As used herein, "on-line" refers to analytical methods that do not necessarily remove material or samples from a batch. However, it may include separation from the main process line and measuring only a portion of the batch. This may be accomplished by incorporating a sampling loop that directs a sample of the batch material toward a probe or sensor, and whereby the diverted sample may be reintroduced into the process stream, material stream, or batch of material, or discarded, depending on the application.

[0188] As used herein, the term "at-line" refers to a method that includes manual sampling followed by discrete sample preparation, measurement, and evaluation. When measuring at-line, the analysis is typically completed at or near the process stream, material stream, or material batch.

[0189] As used herein, the term "off-line" refers to methods that include the largest physical difference between a process flow, material flow, or batch of material and the sample analysis. Similar to at-line measurements, off-line measurements involve removing an analytical sample from a larger batch of material. Off-line analysis typically involves removing a sample or sometimes multiple samples for analysis in a formal laboratory setting.

[0190] The term "precipitate containing proteins" is intended to refer to any precipitated material containing proteins and derived from plasma. The term may refer to plasma, serum, a precipitate produced by plasma or serum. Typically, in the context of the present invention, it refers to a precipitate from plasma, such as a Cohn or Oncley ethanol precipitate, or a Kistler-Nitschmann precipitate.

[0191] As used herein, the coefficient of determination "R 2 ” represents the percentage of variance explained by the prediction model. The higher the coefficient, the better the correlation between the reference data and the spectral data.

[0192] As used herein, "bias" is the systematic average deviation between a reference value and a predicted value.

[0193]

[0194] As used herein, for "cross validation" (also called internal validation), a single retained sample (defined by the user) is taken from the calibration or training set. Using the remaining samples, a chemometric model is built and used to predict previously extracted samples. Comparison of the predicted values ​​with the actual values ​​determined by the reference method shows how well the model predicts the sample.

[0195] "Partial least squares" (regression) is a statistical technique that reduces the predictor variables to a smaller set of uncorrelated components and performs the least squares regression on these components rather than on the original data.

[0196] As used herein, "RMSECV" is the "root mean square error of cross validation" and is a quantitative measure of the predictive power of a model during cross validation. RMSECV is comparable to RMSEP for external validation using an independent test sample set.

[0197]

[0198] As used herein, "RMSEP" is the "root mean square error of prediction", which is a quantitative measure of the predictive ability of a model during external validation using an independent test sample set. RMSEP is comparable to RMSECV of cross validation.

[0199]

[0200] As used herein, "RPD" is the ratio of the standard deviation (SD) and the standard error of the prediction (SEP).

[0201]

[0202] The standard deviation can be determined by the following formula:

[0203]

[0204] As used herein, "SEP" is "Standard Error of Prediction" and is the bias-corrected RMSEP.

[0205]

[0206] Sample containing analyte

[0207] The methods of the present invention relate to determining the amount or concentration of various analytes present in plasma, fractions, derivatives of precipitated material derived therefrom, or resuspensions. Typically, the analytes determined include total protein, but may also include alternative components present in the sample, including alcohols (e.g., ethanol). The analytes may be additives, i.e., exogenous components added during the process, and not naturally present in plasma.

[0208] Plasma can be fresh plasma, "normal" plasma, "hyperimmune" plasma, depleted cold plasma (also called frozen supernatant), or enriched cold plasma. Optionally, the plasma has been treated to remove components such as C1 inhibitor, PCC (prothrombin complex concentrate), and / or AT-III. Plasma can be obtained from many donors and / or individuals and pooled.

[0209] The term "frozen supernatant" (also referred to as depleted cold plasma, cryoprecipitate-depleted plasma, etc.) refers to plasma (derived from whole blood donors or plasma exchange) from which cryoprecipitate has been removed. Cryoprecipitation is the first step in most plasma protein fractionation methods currently used for large-scale preparation of plasma protein therapeutics. The method typically involves pooling frozen plasma thawed under controlled conditions (e.g., at or below 6°C) and then collecting the precipitate by filtration or centrifugation. The supernatant fraction, referred to as "frozen supernatant" by those skilled in the art, is typically retained for use. The resulting depleted cold plasma has reduced levels of factor VIII (FVIII), von Willebrand factor (VWF), factor XIII (FXIII), fibronectin, and fibrinogen. The frozen supernatant provides a common raw material for the preparation of a range of therapeutic proteins, including α1-antitrypsin (AAT), apolipoprotein A-1 (APO), antithrombin III (ATIII), prothrombin complex containing coagulation factors (II, VII, IX and X), albumin (ALB) and immunoglobulins such as immunoglobulin G (IgG).

[0210] The term "cold-rich plasma" refers to plasma (derived from whole blood donors or plasma exchange) that has been frozen and then thawed, but from which cryoprecipitate has not been removed.

[0211] In the case where the plasma has been frozen for transport from the collection location, the frozen plasma is thawed and then collected in a collection tank and then centrifuged. The cryoprecipitate is removed by continuous centrifugation. The cryoprecipitate-depleted plasma can be pumped into a stainless steel fractionation tank and sampled for process control.

[0212] Plasma, whether pooled from more than one or hundreds of individuals, or obtained from a single individual, can be hyperimmune plasma. For example, plasma can be obtained from the blood of one or more individuals (and therefore also healthy individuals) who have mounted an immune response to an infection and have recovered.

[0213] The sample comprising the analyte of interest can be a precipitate or fraction derived from the processing of blood plasma. Many different methods can be used to selectively precipitate protein from solution, for example, by adding salt, alcohol and / or polyethylene glycol, combining pH adjustment and / or cooling step simultaneously. Therefore, it is expected that the present invention will be applicable to most protein precipitates, such as protein precipitates containing immunoglobulin G, regardless of how they are initially prepared. It should be noted that the present invention can also be implemented in separating other types of proteins, and the protein includes albumin, immunoglobulin (Ig) (such as IgA, IgD, IgE or IgM, immunoglobulin of each type separately or its mixture). It can be foreseen that recombinant protein is also suitable in this respect.

[0214] The sample can be any material containing IgG or albumin (e.g., in the form of a paste, precipitate, or inclusion body), or derived from a starting material, such as a solution from which IgG or albumin can be precipitated by one or more of the methods explained above, whether it is plasma or serum from human or animal sources, fermentation broth, cell culture, protein suspension, milk, or other original sources. The material or solution containing immunoglobulins can contain one or more monoclonal or polyclonal immunoglobulins. In some embodiments, the starting material containing immunoglobulins is a solution comprising polyclonal antibodies. In other embodiments, the starting material comprises monoclonal antibodies or fragments thereof. In other embodiments, the sample can be any material containing alcohol (e.g., ethanol) or derived from the starting material, such as a solution to which alcohol (e.g., ethanol) has been added to promote precipitation.

[0215] To obtain immunoglobulins or albumin from plasma, the plasma is usually subjected to alcohol fractionation, which may be combined with other purification techniques such as chromatography, adsorption or precipitation. However, other processes may also be used. For example, the protein-containing precipitate can be a II+III precipitate according to Cohn's method, such as method 6, Cohn et al. J. Am; Chem. Soc., 68 (3), 459-475 (1946), method 9, Oncley et al. J. Am; Chem. Soc., 71, 541-550 (1946), or a I+II+III precipitate, method 10, Cohn et al. J. Am; Chem. Soc., 72, 465-474 (1950); and the method of Deutsch et al. J. Biol. Chem. 164, 109-118 (1946), or precipitate-A of Nitschmann and Kistler Vox Sang. 7, 414-424 (1962); Helv. Chim. Acta 37, 866-873 (1954). Alternative precipitates containing proteins of interest include, but are not limited to, Oncley fractions, Cohn fractions, ammonium sulfate precipitates containing other immunoglobulin G or albumin from plasma described by Schulze et al. in U.S. Pat. No. 3,301,842. Other alternative precipitates containing proteins of interest include, but are not limited to, caprylic acid precipitates, such as described in, for example, EP893450.

[0216] "Normal plasma," "hyperimmune plasma" (such as hyperimmune anti-D, tetanus or hepatitis B plasma) or any plasma equivalent thereof can be used as the starting material in the cold ethanol fractionation method described herein.

[0217] The supernatant of the 8% ethanol precipitation (method of Cohn et al.; Schultze et al. (supra), p. 251), precipitate II+III (method of Oncley et al.; Schultze et al. (supra), p. 253) or precipitate B or IV (method of Kistler and Nitschmann; Schultze et al. (supra), p. 253) are examples of sources of IgG compatible with industrial scale plasma fractionation. The starting material for the purification process to obtain IgG or albumin in high yield may alternatively be any other suitable material from different sources such as fermentation and cell culture or other protein suspensions.

[0218] In the Cohn fractionation method, the first fractionation step produces a fraction I mainly containing fibrinogen and fibronectin. The supernatant from this step is further treated to precipitate fraction II+III, and then fractions III and II are precipitated. Typically, fraction II+III contains about 60% IgG, as well as impurities such as fibrinogen, IgM and IgA. Most of these impurities in fraction III are then removed, and fraction III is considered a waste fraction and is usually discarded. The supernatant is then treated to precipitate the main IgG-containing fraction II, which may contain more than 90% IgG. The above % values ​​refer to the % purity of IgG. Purity can be measured by any method known in the art, such as gel electrophoresis or immunoturbidimetry. In the Kistler & Nitschmann method, fraction I is equivalent to fraction I of the Cohn method. The next precipitate / fraction is called precipitate A (fraction A). This precipitate is roughly equivalent to Cohn fraction II+III, although not identical. The precipitate is then redissolved and conditions are adjusted to precipitate precipitate B (Fraction B), which is equivalent to Cohn Fraction III. Again, this is considered a waste fraction and is typically discarded. The precipitate B supernatant is then additionally processed to produce precipitate II, which is equivalent to Cohn Fraction II.

[0219] The specific protein-containing precipitate or suspension thereof may comprise plasma proteins, peptide hormones, growth factors, cytokines and polyclonal immunoglobulins, the plasma proteins being selected from the group consisting of human and animal coagulation factors (including fibrinogen, prothrombin, thrombin, prothrombin complex, FX, FXa, FIX, FIXa, FVII, FVIIa, FXI, FXIa, FXII, FXIIa, FXIII and FXIIIa, von Willebrand factor), transport proteins (including albumin, transferrin, ceruloplasmin, haptoglobin, hemoglobin and hemopexin), protease inhibitors (including β-antithrombin, α-antithrombin, α-2-macroglobulin, C1-inhibitor, tissue factor, pathway inhibitor (TFPI), heparin cofactor II, protein C inhibitor (PAI-3), protein C and protein S, alpha-1 esterase inhibitor protein, alpha-1 antitrypsin), antiangiogenic (antiangiogenic) proteins (including latent antithrombin), highly glycosylated proteins (including alpha-1-acid glycoprotein, antichymotrypsin, inter-alpha-trypsin inhibitor, alpha-2-HS glycoprotein and C-reactive protein) and other proteins (including histidine-rich glycoprotein, mannan-binding lectin, C4 binding protein, fibronectin, GC globulin, plasminogen, blood factors (such as erythropoietin), interferon, tumor factor, tPA, yCSF).

[0220] In certain embodiments, the method of the present invention can be applied to determine the concentration of analyte (e.g., total protein) during the resuspension of the precipitate from blood-derived plasma. In particular, the method can be used for real-time assessment of protein concentration during resuspension, and helps determine total protein concentration, to help determine the amount of the subsequent reagent of resuspension. The advantage of the method of the present invention is that the manufacturer does not need to manually sample the protein-containing sample, and then manually calculates the amount of the subsequent reagent to be added. In addition, the progress of protein dissolution during the resuspension of protein-containing precipitate or paste as described herein can be monitored in real time, making it possible to more effectively determine when resuspension is completed, add subsequent reagents or carry out the optimal time of the next step in product processing, and thereby reduce unnecessary cycle time.

[0221] Many core methods for extracting plasma proteins are largely based on cryoprecipitation and ethanol fractionation. Albumin and IgG were the first proteins to be fractionated from human plasma using a multi-step, sequential cold ethanol method. Cohn and his colleagues were pioneers in plasma fractionation by using low temperatures and by adding 8% to 40% v / v ethanol to separate albumin. In Cohn's method, five fractions from fraction I to fraction V can be obtained, each of which is prepared by adjusting parameters such as ethanol concentration, protein concentration, temperature and pH.

[0222] In one embodiment, the method is performed on a large scale. For example, the method is performed on an industrial or commercial scale. Methods performed on an industrial or commercial scale will be apparent to those skilled in the art and / or described herein. For example, methods performed on an industrial scale include large-scale purification of IgG or albumin from plasma or its fractions.

[0223] In one embodiment, at least 500 kg of plasma or its fraction is used for large-scale purification of IgG or albumin. For example, 500 kg to 1000 kg, or 1000 kg to 2500 kg, or 2500 kg to 5000 kg, or 5000 kg to 7500 kg, or 7500 kg, or 10000 kg, or 10000 kg to 12500 kg, or 12500 kg to 15000 kg of plasma or its fraction is used for large-scale purification of IgG or albumin. In one embodiment, at least 1000 kg, or 2500 kg, or 5000 kg, or 7500 kg, or 10000 kg, or 12500 kg, or 15000 kg of plasma or its fraction is used for large-scale purification of IgG or albumin. In one embodiment, at least 1000 kg of plasma or its fraction is used for large-scale purification of IgG or albumin. In one embodiment, at least 2500 kg of plasma or its fraction is used for large-scale purification of IgG or albumin. In one embodiment, at least 5000 kg of plasma or a fraction thereof is used for large-scale purification of IgG or albumin. In one embodiment, at least 7500 kg of plasma or a fraction thereof is used for large-scale purification of IgG or albumin. In one embodiment, at least 10000 kg of plasma or a fraction thereof is used for large-scale purification of IgG or albumin. In one embodiment, at least 12500 kg of plasma or a fraction thereof is used for large-scale purification of IgG or albumin. In one embodiment, at least 15000 kg of plasma or a fraction thereof is used for large-scale purification of IgG or albumin.

[0224] There are multiple stages during the processing of plasma into specific protein-rich fractions that include the use of ethanol, and the present invention can be used to determine the amount of ethanol present in the complex solution, which can then report any adjustments required. In addition, the present invention can be used to determine when a certain concentration of ethanol is reached during the step of ethanol addition.

[0225] As described herein, the sample comprising the analyte can be a turbid solution or suspension, particularly a highly turbid solution or suspension. In any embodiment, (a) the turbid solution or suspension can have an NTU equal to or greater than any value described herein, (b) the turbid solution or suspension can have an NTU of any value described herein, or (c) the turbid solution or suspension can have the maximum NTU of any value described herein. Typically, various photometric methods of turbid media are used to measure turbidity, such as turbidimetry, optometry, nephelometric methods. Turbidity measurements are performed using instruments such as nephelometers or nephelometers. Typically, this is a photodetector measuring light scattered by a liquid. Specifically, the scattering of light by the suspension makes it possible to estimate the concentration of a substance suspended in a liquid. Typically, the device is composed of white light or an infrared light source. In turbidimetry, scattered light is measured at 90 ° and 25 ° relative to the incident light. In nephelometric methods, scattered light is measured using a sensor located on the incident light axis. Such turbidity analysis methods are well known in the art, and a wide range of instruments can be used for turbidity analysis, including handheld and online sensors, such as the Hach TL2360 handheld turbidimeter that measures turbidity in nephelometric turbidity units (NTU) at a 90° angle. In any method of the present invention described herein, the method further provides a step of determining the turbidity of a sample obtained from processing blood-derived plasma. Preferably, the turbidity of the sample is any value or range described herein. Preferably, the step of determining the turbidity of the sample includes using a Hach TL2360 turbidimeter calibrated with NTU primary Formazin solution standards to measure the turbidity in a 10 mL volume of a test sample in an 11 mm glass tube at a 90° angle, the test sample being obtained from processing blood-derived plasma.

[0226] Method for obtaining wavelength spectrum

[0227] Those skilled in the art will be familiar with standard instrumentation that can be used to apply light sources in the near infrared range. In the context of the present invention, and in preferred embodiments involving determination of protein concentration during processing of blood-derived plasma samples, the instrumentation may include the use of a NIR probe adapted for use in a large container containing a sample of interest.

[0228] In one embodiment, the NIR spectroscopy instrument is arranged to analyze the test sample during mixing in a large tank and provide NIR data in real time. In certain embodiments, several probes can be connected to a single spectrometer. In one embodiment, the first probe can therefore be arranged at a first position, and the second probe is arranged at a second position, and if applicable, the third probe is arranged at a third position. All such probes can be connected to the same spectrometer. It will be appreciated by those skilled in the art that the use of multiple NIR probes can help provide a more accurate data range related to a test sample or training sample containing an analyte of interest.

[0229] The probe of the NIR spectroscopy instrument may be in the form of an immersion probe or constitute part of a flow cell.The entire process stream or a side stream of the process stream may be directed through such a flow cell.

[0230] In a preferred embodiment, the NIR probe is configured to measure the NIR spectrum during the mixing sample. During mixing, the optical slit of the NIR probe can be oriented in a direction parallel to the fluid flow. Typically, the optical slit of the NIR probe is oriented so that it does not directly face the flow of the fluid flow during mixing. For example, during mixing, the optical slit can be vertical or at an angle relative to the fluid flow. In other words, the NIR probe can be oriented downward along the wall of the container.

[0231] Methods used to generate model / reference datasets

[0232] Those skilled in the art will be familiar with general methods for preparing reference data sets or models of representative NIR spectra to which spectra from test samples can be compared for the purpose of determining analyte concentration.

[0233] The reference data set may be from one or more samples containing a known concentration of the analyte, wherein the concentration of the analyte has been determined by a suitable method taking into account the composition of the reference and test samples. For example, in the case of a turbid solution or suspension containing protein, the most suitable method for confirming the protein concentration may be the Dumas method based on the determination of the total nitrogen content, rather than other methods for determining protein concentration, such as the biuret assay, the BCA assay, the Bradford assay or absorbance at 280 nm.

[0234] Representative NIR spectra of reference or training samples for which protein concentration has been determined may then be obtained so that the representative NIR spectra may be used to form the basis of a model against which test wavelength spectra may be evaluated.

[0235] Those skilled in the art will appreciate that the more representative NIR spectra or training spectra provided, the higher the accuracy of the model.

[0236] It may be necessary to apply spectral pretreatment to data (whether it is a test spectrum or a reference or training spectrum for deriving a suitable model). These pretreatments can be applied to emphasize spectral changes. Examples of suitable spectral pretreatments include vector normalization, first-order derivatives, minimum-maximum normalization, straight line subtraction, multivariate scatter correction, second-order derivatives and combinations thereof. Preferably, the pretreatment applied to the test spectrum or the reference or training spectrum for deriving a suitable model is vector normalization or first-order derivatives. In one embodiment, the pretreatment applied to the test spectrum or the reference or training spectrum for deriving a suitable model is a combination of vector normalization and first-order derivatives.

[0237] A multivariate calibration algorithm such as multiple linear regression (MLR), principal component regression (PCR) or partial least squares (PLS) regression can be used to generate the model. Preferably, the model is generated using partial least squares (PLS) regression as described herein. The PLS algorithm is described in (Haaland, Thomas, Anal. Chem 60 (1998) 1193; Martens, Naes, Multivariate Calibration, J. Wiley & Sons, New York (189): Chapter 3.5; Brown, Apply. Spectosc. 49, No. 12 (1995) 14A; and Bouveresse, Hartmann, Massard, Last, Prebble, Anal. Chem. 68, No. 6 (1996) 982).

[0238] This article describes methods for assessing the quality of a given model (including then determining whether additional training data is needed to further develop the model).

[0239] In certain embodiments, when evaluating the model quality of different chemometric models or multivariate models, criteria that may be considered include:

[0240] Rank: corresponds to the number of factors of the stoichiometric model. A lower rank generally leads to improved model stability.

[0241] • Root mean square error of cross validation (RMSECV): RMSECV should be minimized.

[0242] Residual Prediction Deviation (RPD): A model performance indicator. RPD should be maximized.

[0243] ·R 2 : Coefficient of determination, describing the relationship between spectral data and concentration data. 2Maximized to nearly 100.

[0244] When evaluating the predictive power of a chemometric or multivariate model on an independent data set, the following criteria may also be considered:

[0245] Bias: The average difference between the reference value and the predicted value. Should be close to 0.

[0246] Root Mean Square Error of Prediction (RMSEP): A measure of the accuracy of predictions for independent test samples.

[0247] RMSEP should be minimized.

[0248] Residual Prediction Deviation (RPD): A model performance indicator. RPD should be maximized.

[0249] ·R 2 : Coefficient of determination, describing the relationship between spectral data and concentration data. 2 Maximized to nearly 100.

[0250] The generation of the model may involve training samples, which may include a set of representative samples covering variables such as different paste types, sample temperatures, instrument variability, operator manipulation, raw materials, and plasma sources. Using such varying reference samples in the generation of the training model to capture such variables will further ensure model robustness when evaluating various samples containing unknown concentrations of analytes. Example

[0251] Example 1 - Description of the NIR measurement setup for bystander protein determination in an Ig precipitate suspension.

[0252] NIR Spectrometer

[0253] The NIR measurements were performed with an FT-NIR Matrix-F process spectrometer from Bruker Optics GmbH. NIR process spectrometers can be used for spectroscopic analysis of liquids, suspensions and solids by transmission, diffuse reflection and transflection (the latter two were used here).

[0254] NIR probes

[0255] The NIR spectra of the training samples were mainly collected with a transflection probe (see Table 1 for details). A few spectra were also recorded with a reflection probe in order to compare the two measuring principles and to select the one that is most suitable for the intended purpose.

[0256] Table 1. NIR probes used in the feasibility study.

[0257]

[0258] software

[0259] Spectral acquisition and data processing were performed using the following software programs:

[0260] - Bruker OPUS: Basic software for operating the spectrometer, acquiring spectra and defining instrument parameters.

[0261] - Bruker OPUS QUANT: Software package for generation, optimization and validation of NIR models and for quantitative analysis.

[0262] Sideline measurement setup

[0263] The measurement setup for NIR at-line data acquisition consisted of the following components: magnetic stirrer, laboratory lift, magnetic stirring bar (2.5 cm long), stand and clamp.

[0264] In 4000 -1 -11000cm -1 The spectral range is 16cm -1 The NIR spectra were recorded with 64 scans at a resolution of 1.38 Å.

[0265] A background spectrum of air was recorded and used to correct the sample NIR spectra. The spectra of all samples were recorded in triplicate.

[0266] In case of obvious spectral outliers (as identified by the shape of the spectrum, e.g. due to air entrapment in the slits), all duplicate spectra for respective samples were manually excluded from both the training and testing datasets.

[0267] The NIR probe was fixed with a stand and clamp. A magnetic stirring bar was added to the sample container and the sample was stirred at 450 rpm for 2 minutes. The sample was then raised on a laboratory lift until the probe was immersed in the sample. The sample was stirred for another minute to ensure that the suspension was evenly distributed in the slit of the transflectance probe.

[0268] Analytical reference method - Dumas assay

[0269] The total nitrogen content was determined by the Dumas method (Ph. Eur., Chapter 2.5.33, “Total Protein”, Method 7, Procedure B, current edition; U.S.P. <1057>

[0270] "Biotechnology-Derived Articles-Total Protein Assay", Method 7, Procedure 2, current edition; Japanese Pharmacopoeia, G3 "Biotechnological / Biological Products-Total Protein Assay", Method 7, Procedure B, current edition) quantify the concentration of total protein in the training samples. Results are reported in g / kg. All reference samples were stored at 2°C-8°C prior to analysis.

[0271] Sample preparation

[0272] Laboratory samples

[0273] In the laboratory, resuspended IgG precipitate samples from different precipitation types and different precipitation batches were prepared in 100 ml vacuum flasks with sodium acetate buffer. The total sample volume was approximately 70 mL.

[0274] All laboratory samples were measured in triplicate at room temperature of approximately 21°C. In addition, some samples were also measured at approximately 15°C and approximately 30°C to 35°C; for temperature conditioning, the samples were tempered in a water bath prior to the NIR measurement. If the samples were not measured on the same day as their preparation, they were stored at 4°C until measurement.

[0275] Conventional samples

[0276] For each routine batch, two independent samples were transferred to 100 ml vacuum flasks in the 1g production facility. The NIR spectra of the samples were recorded in the laboratory and / or on a NIR spectrometer available in the routine production facility.

[0277] Conventional samples were measured in triplicate at room temperature of approximately 21°C. In addition, some samples were also measured at approximately 15°C and approximately 30°C to 35°C; for temperature conditioning, the samples were tempered in a water bath prior to the NIR measurement. If the samples were not measured on the same day as their preparation, they were stored at 4°C until measurement.

[0278] Stoichiometric or multivariate models

[0279] Model generation using QUANT

[0280] In a typical NIR spectrum of a resuspended IgG precipitate sample, the peak at approximately 6,900 cm -1 and 5,000cm -1There are two main peaks at wavenumbers of , which reflect the total absorption of the incident light by the water contained in the matrix. Since hardly any light reaches the detector in these spectral regions, quantitative information cannot be reliably extracted. Therefore, the spectral region around these peaks is not included in the model.

[0281] In order to emphasize the spectral changes, the raw spectra are usually preprocessed. The preprocessed spectra are the basis for generating chemometric quantitative models. The applied spectral preprocessing is first-order derivative, vector normalization and a combination of first-order derivative and vector normalization.

[0282] All models were generated based on the acquired spectra and the corresponding protein reference values ​​using the Bruker QUANT software package. The best combination of spectral ranges and pre-processing of the spectra was determined in the optimization tool of the software. Cross-validation was used for internal validation of the models.

[0283] Model quality standards

[0284] When evaluating the model quality of different chemometric models or multivariate models, consider the following criteria:

[0285] - Rank: corresponds to the number of factors of the stoichiometric model. A lower rank generally leads to increased model stability.

[0286] - Root mean square error of cross validation (RMSECV): RMSECV should be minimized.

[0287] - Residual Prediction Deviation (RPD): Model performance indicator. RPD should be maximized.

[0288] -R 2 : Coefficient of determination, describing the relationship between spectral data and concentration data. 2 Maximized to nearly 100.

[0289] When evaluating the predictive power of a chemometric or multivariate model for independent data sets, consider the following criteria:

[0290] -Bias: The average difference between the reference value and the predicted value. Should be close to 0.

[0291] - Root Mean Square Error of Prediction (RMSEP): A measure of the accuracy of predictions for independent test samples.

[0292] RMSEP should be minimized.

[0293] - Residual Prediction Deviation (RPD): Model performance indicator. RPD should be maximized.

[0294] -R 2 : Coefficient of determination, describing the relationship between spectral data and concentration data.2 Maximized to nearly 100.

[0295] Example 2 - Probe selection for paralogous protein determination in Ig precipitate suspensions: transflectance mode vs. reflection mode

[0296] A series of training samples of one type of Ig precipitate suspension were measured with transflectance and reflection probes. The spectra of the samples obtained with the two probe types are depicted in Figure 1. Based on the spectra of the training samples and the corresponding protein reference values, a separate model for each measurement mode was generated. The spectral range during optimization was set to five default ranges (9400-7500cm -1 、7500-6100cm -1 、6100-5450cm -1 、5450-4600cm -1 4600-4250cm -1 ). The properties of the NIR model are listed in Table 2.

[0297] Table 2. Model properties for transflection and reflection data

[0298] Model characteristics Transflective mode Reflection Mode RMSECV 0.374 0.546 rank 5 6 RPD 21.1 14.4 <![CDATA[R 2 ]]> 99.77 99.52

[0299] High-quality models can be generated from both sets of spectral data. Compared with the model generated from the reflectance data, the model generated from the transflectance data produced lower error (RMSECV), lower rank, RPD and R 2 =Higher. Therefore, transflectance probes are preferred over reflection probes. All further NIR studies on the Ig precipitate suspensions were continued with the NIR transflectance probe.

[0300] Example 3 - Determination of paralogous proteins in Ig precipitate suspension

[0301] General NIR Model

[0302] Developing NIR Model Mod gen ( Figure 2a ) and Mod lab ( Figure 2b ) to facilitate the prediction of protein concentration in Ig precipitate suspensions of different precipitate types (PPT A, FrI+II+III, Fr II+III).

[0303] Multivariate NIR models were generated by partial least squares (PLS) regression using the OPUS QUANT software package processing raw spectra of the training samples in combination with respective protein reference values ​​(Dumas).

[0304] Based on the signal variation in the NIR loading graph, select approximately 9'000-7'500cm -1 、6'900-5'600cm -1 and 4'935-4'500cm -1 The wavelength region was used for NIR model optimization; the wavelength region between 7'500 and 6'900 cm was excluded. -1 Between 5'600 and 4'935cm -1 The main water-derived signal between

[0305] The NIR model was optimized with a predefined selection of spectral preprocessing, namely first order derivative, vector normalization and a combination of first order derivative and vector normalization.

[0306] A preliminary set of NIR models was selected and validated by internal cross-validation using a standard number of 1 holdout sample for every 30 samples included in the model. The selected models were chosen from the preliminary set of NIR models by comprehensively evaluating the statistical quality attributes of the internal validation results and the prediction quality of the independent test samples.

[0307] Model robustness testing

[0308] To ensure that the model contains a representative set of spectra, model generalization (i.e., the ability of the model to correctly adapt to new, previously unseen data drawn from the same distribution as that used to create the model) is continuously assessed during data collection. Testing the ability of a model to generalize is called "model robustness testing."

[0309] In short, the RMSECV and R obtained from the cross-validation of the model 2 The values ​​are compared with:

[0310] 1. Validate the RMSEP and R obtained from a test set of the same dataset randomly split into two equivalent subsets (a training set and a test set) using the same spectral region and preprocessing as the cross validation performed initially. 2 value.

[0311] 2. Validate the RMSEP and R obtained from a test set of the same dataset randomly split into two equivalent subsets (one training set and one test set, interchanged with 1) using the same spectral region and preprocessing as the cross validation performed initially 2 value.

[0312] If no or little error margin is observed between cross validation and validation on the two test sets (RMSECV / RMSEP and R 2), the model is considered sufficiently stable. Otherwise, the model should be further improved (e.g., by augmenting with additional spectra) to enhance the predictive power of the model.

[0313] Model Mod gen Features

[0314] Table 3 provides the optimized NIR model Mod gen Overview of Mod gen The visualization is presented in Figure 2a middle.

[0315] Table 3. Mod gen Characteristics and quality attributes

[0316]

[0317]

[0318] *Each sample was measured at three different temperatures in the laboratory.

[0319] Model Mod lab Nature

[0320] Table 4 provides the optimized NIR model Mod lab Overview of Figure 2b Mod in the protein range of 16 to 42 g / kg was presented lab Visualization.

[0321] Table 4. Mod lab Characteristics and quality attributes

[0322]

[0323] Mod gen The predictive power of

[0324] The NIR model Mod was evaluated with a set of independent test samples from the IG routine preparation (i.e., samples not included in the model). gen The predictive power of the samples covered suspensions of different IG precipitate types (PPT A, Fr I+II+III, Fr II+III). NIR spectra of routine test samples (2 independent samples, each with three replicate measurements) were obtained on different spectrometers.

[0325] If the absolute difference between the averages of 2 × 3 regular spectral replicates exceeded a maximum value of 1.5 g / kg, all replicate spectra of the corresponding sample were removed from the test data set.

[0326] In Table 5 and Figure 3 An overview of the predictions in the test datasets covering different sediment types is provided in .

[0327] The RMSEP of the entire test set containing different sediment types is 0.880 g / kg and is therefore very similar to Mod gen The RMSECV of 0.860 g / kg (Table 5) indicates that the gen Robustness of predictions for independent samples. Furthermore, the predictions for samples in the test set were characterized by a negligible bias of -0.0624, an offset value of 2.446, a slope of 0.920, and a correlation coefficient (R) of 0.9277.

[0328] Table 5. For determining Mod gen Overview of the statistical quality attributes and test set composition for predictive ability across an independent sample set covering different sediment types

[0329]

[0330] Example 4 - On-line protein determination in Ig precipitate suspension

[0331] Under strict temperature control, 400 to 900 g of 1 g precipitate (PPT A, Fr I+II+III, Fr II+III) were resuspended in a scaled-down double-jacketed steel reactor. After the suspension reached the target temperature of 20° C., NIR process monitoring of the 1 g precipitate resuspension was started and continued for approximately 24 hours.

[0332] The NIR setup includes a Matrix FT-NIR process spectrometer with an IN 271 transflection probe and a 5 m optical fiber. The NIR probe is introduced into the liquid level through a subsurface port and is configured to be able to measure the NIR spectrum during mixing of the sample. During mixing, the optical slit of the NIR probe is oriented parallel to the direction of the fluid flow.

[0333] The conditions for NIR data acquisition were equivalent to the at-line NIR system described in Example 1. NIR spectra were recorded at 3 min intervals.

[0334] The NIR model developed with an inline sample of the Ig pellet suspension was used for online protein quantification during the Ig pellet resuspension reaction.

[0335] Additionally, reference samples were taken after 2 hours and approximately 24 hours and analyzed using the Dumas protein assay.

[0336] The online monitoring results of the resuspension reactions of three different Ig-containing precipitate types (PPT A, Fr I+II+III, Fr II+III in this paper, respectively) are shown in Figures 4, 5, and 6. In addition to the NIR raw spectra, the spectra were preprocessed with first-order derivatives and vector normalization to emphasize spectral variations.

[0337] The results demonstrate that the NIR model developed for by-line protein prediction is suitable for online protein prediction during resuspension of pastes of alcohol (e.g., ethanol) precipitate type obtained from blood-derived plasma. The benefit of this method is that it enables quantification of proteins in resuspensions of pastes obtained from ethanol precipitation of blood-derived plasma without prior sample preparation (as is the case with current by-line and off-line procedures). In addition, the method enables real-time monitoring of the progress of paste resuspension, potentially reducing cycle times between processing steps.

[0338] Example 5 - Description of the NIR measurement setup for online ethanol (EtOH) in plasma fractionation at manufacturing scale

[0339] NIR Spectrometer

[0340] The NIR measurements were performed with an FT-NIR Matrix-F process spectrometer from Bruker Optics GmbH. NIR process spectrometers can be used for spectroscopic analysis of liquids, suspensions and solids by transmission, diffuse reflection and transflection.

[0341] NIR probes

[0342] During manufacturing scale fractionation, transreflectance was used to follow the ethanol concentration and other matrix changes. Therefore, NIR spectra in a scaled-down model were recorded using a transreflectance probe from Bruker (IN271-02, 1 mm slit width).

[0343] NIR spectra were recorded at 3 min intervals using the following standardized instrument settings: Preamplifier B (for product) or Preamplifier A (for air background), 64 scans, spectral range 4'000-11'948 cm -1 , resolution 16cm -1 .

[0344] software

[0345] Data acquisition was performed using CMET from Bruker Optics GmbH.

[0346] The OPUS software package QUANT was used for model optimization and calibration as well as predictions for data sets not included in the calibration data set.

[0347] The OPUS 3D software package was used to qualitatively analyze the spectral evolution caused by changes in the sample matrix, especially changes in ethanol concentration.

[0348] Stoichiometric or multivariate models

[0349] Model generation using QUANT

[0350] All models were generated based on the acquired spectra and the corresponding ethanol reference values ​​using the Broker QUANT software package. The best combination of spectral ranges and pre-processing of the spectra was determined in the optimization tool of the software. Cross-validation was used for internal validation of the models.

[0351] Model quality standards

[0352] Similar criteria as described in Example 1 above were considered when evaluating the model quality and predictive power of different chemometric models or multivariate models.

[0353] Example 6 - Bypass EtOH Determination in Plasma Fractionation

[0354] General NIR Model

[0355] Develop NIR model I+II+III (Developed using samples from plasma fractionation of Cohn fractions (I+)II+III), Model IV (Developed using samples from plasma fractionation of Cohn Fraction IV) and Mod comp (Developed using samples from plasma fractionation of both Cohn Fraction (I+)II+III and Cohn Fraction IV) to aid in the prediction of ethanol (EtOH) concentrations in the plasma fractionation step.

[0356] The multivariate NIR model was generated by partial least squares (PLS) regression using the OPUS QUANT software package processing the raw spectra of the training samples in combination with the theoretical ethanol concentrations.

[0357] Based on the signal variation in the NIR loading graph, approximately 9,400-5,448 cm -1 The wavelength region used for Model comp 、Model I+II+III and Model IV NIR model optimization.

[0358] The NIR model was optimized with a predefined selection of spectral preprocessing such as first derivative, vector normalization, min-max normalization, straight line subtraction and a combination of first derivative and vector normalization.

[0359] Model comp The preprocessing was based on vector normalization and first-order derivatives and showed a cross-validated root mean square error (RMSECV) of 0.149 and a PLS rank of 10. The overlay of the preprocessed spectra is shown in Figure 7 middle.

[0360] Model I+II+III The preprocessing was based on vector normalization and first-order derivatives and showed a cross-validated root mean square error (RMSECV) of 0.0576 and a PLS rank of 7. The overlay of the preprocessed spectra is shown in Fig. 9 middle.

[0361] Model IV Vector normalization and first derivatives were used as preprocessing and showed a cross-validated root mean square error (RMSECV) of 0.0564 and a PLS rank of 9. An overlay of the preprocessed spectra is shown in Fig.11 middle.

[0362] Mod comp The predictive power of

[0363] Using Model comp Prediction of ethanol concentrations in manufacturing-scale runs of plasma fractionation of Cohn Fractions (I+)II+III and Cohn Fraction IV.

[0364] Based on the amount added, i.e. the weight of ethanol, the theoretical ethanol concentration is calculated for each recorded spectrum. comp The ethanol predictions were very close to the theoretical concentrations, indicating that the model can accurately determine the ethanol concentrations in manufacturing-scale plasma fractionation of Cohn Fractions (I+)II+III and Cohn Fraction IV ( Figure 8 ).

[0365] Mod I+II+III The predictive power of

[0366] Using Model I+II+III Prediction of ethanol concentrations in manufacturing-scale runs of plasma fractionation of Cohn fractions (I+)II+III.

[0367] Based on the amount added, i.e. the weight of ethanol, the theoretical ethanol concentration is calculated for each recorded spectrum. I+II+IIIThe ethanol predictions were very close to the theoretical concentrations, indicating that the model can accurately determine the ethanol concentration in manufacturing-scale plasma fractionation of Cohn fractions (I+)II+III ( Fig.10 ).

[0368] Mod IV The predictive power of

[0369] Using Model IV Prediction of ethanol concentration in manufacturing-scale runs of plasma fractionation of Cohn's Fraction IV.

[0370] Based on the amount added, i.e. the weight of ethanol, the theoretical ethanol concentration is calculated for each recorded spectrum. IV The ethanol predictions were very close to the theoretical concentrations, indicating that the model can accurately determine the ethanol concentration in manufacturing-scale plasma fractionation of Cohn Fraction IV ( Fig.12 ).

[0371] Example 7 - Determination of paralogous proteins in albumin precipitate resuspension

[0372] Precipitate C (PPT C) or Precipitate V (PPT V) was resuspended in a double-jacketed vessel on a small scale (<1 L). The suspension samples were taken and NIRS samples with different concentrations of PPT C or PPT V were prepared in vacuum flasks; these dilutions were prepared by mixing different amounts of suspension, distilled water and filter aid. The NIR transmittance and reflectance spectra of these samples were collected three times and used as the basis for the model (Model Albresusp ) calibration set.

[0373] For the model used during resuspension (Model Albresusp ), as spectral preprocessing methods, the combination of first-order derivative and vector normalization provides the best results. Fig.13 shows the NIR spectra of the training dataset, and Fig.14 It is shown that the resulting model can accurately determine the protein concentration during resuspension of PPT C or PPT V.

[0374] Model Albresusp Vector normalization and first order derivatives were used as preprocessing and a root mean square error of cross validation (RMSECV) of 0.284 and a PLS rank of 5 were shown.

[0375] Table 3. Model Albresusp Characteristics and quality attributes

[0376]

[0377] To ensure that the model makes reliable predictions for protein concentration, independent spectra for which corresponding reference protein values ​​are known were tested. In this case, 21 test spectra (7 samples, replicated three times) were used to test the model, as Fig.15 The predictions for the independent test set yielded an RMSEP of 0.733 g / kg. Albresusp The predicted values ​​of correspond well to the actual protein concentrations obtained by the Dumas assay. Albresusp Protein concentration can be predicted with good accuracy.

[0378] Example 8 - At-line and On-line EtOH Determination in Plasma Fractionation

[0379] For this feasibility study, NIR transmittance and reflectance spectra were acquired for three different sample sets:

[0380] 1. Training set: PPT C (7 experimental series) or PPT V (3 experimental series) were resuspended on a small scale (<1 L). After the resuspension was complete, ethanol was added stepwise. The NIR probe was directly installed in the resuspension container (2L glass reactor) and the spectrum was recorded after each step of ethanol addition. A homogeneous mixture in the reactor was ensured by stirring between the ethanol mixture for 10 minutes (350rpm, overhead blade stirrer). The spectra of these samples were collected three times and used as a training set to build a model for ethanol concentration prediction. Spectral data acquisition was carried out at 0±1°C.

[0381] 2. Test set (byline): Processed samples were obtained from a pilot plant facility or PAT laboratory. The samples were cooled to 0°C (±1°C) and prepared for spectral acquisition as described in Table 4 (byline test set). The byline spectra of these samples were collected in triplicate. Those independent data sets were later used as byline test sets to evaluate model performance.

[0382] 3. Test set (online): Three independent albumin resuspension runs (2x PPT C, 1x PPT V) were performed with a near infrared (NIR) probe mounted in the suspension vessel and spectra acquired every minute in an online manner.

[0383] For all samples analyzed, a reference measurement by gas chromatography (GC) was performed to determine the true ethanol concentration.

[0384] Table 4. Data collection conditions adopted for this feasibility study. For each parameter, it is listed to which dataset that particular condition was applied.

[0385]

[0386] To construct a model capable of predicting ethanol concentration, partial least squares (PLS) regression was applied to a set of spectra selected from the model training set samples. A total of N=34 samples were included in the model training set.

[0387] As spectral preprocessing methods, a combination of first-order derivatives and vector normalized standard normal variables (SNV) provided the best results. In addition, spectral regions as outlined in Table 5 have been adopted. The resulting preliminary ethanol quantification model Model EtOH performance (see Fig.17 (B) and Table 6).

[0388] Table 5. Model EtOH Detailed calibration characteristics

[0389]

[0390] Table 6. Model EtOH Detailed characteristics of the predictive power of

[0391] RMSEP deviation SEP RPD Compensation value Slope 0.584 -0.175 0.416 2.28 2.564 0.695

[0392] Test set (byline)

[0393] After the preliminary model has been built, use Model EtOH The ethanol content of 14 individual test set samples (11x PPT C, 3x PPT V) was quantified. Comparisons between the results from the QC reference assays and the ethanol concentration predictions are summarized in Fig.17 The preliminary model provided predictions of ethanol content with an RMSEP (root mean square error of prediction) of 0.584% w / w for ethanol in suspension.

[0394] Test set (online)

[0395] To track the timely changes in ethanol concentration during the resuspension of PPT C or PPT V, an online NIRS protocol was established on a laboratory scale (2x PPT C, 1x PPT V). During the resuspension of PPT, the online NIR probe recorded spectra every minute and then immediately measured by Model EtOH The analysis was performed. The data obtained were then plotted (ethanol concentration [% w / w] vs. time [min]) and displayed in real time on a connected computer. This enabled the dissolution of ethanol from the PPT to be followed in real time and thus also allowed the resuspension state and kinetics of the PPT to be inferred.

[0396] A total of three resuspension runs have been monitored in this way and are provided in Fig.18 middle.

[0397] in conclusion

[0398] A NIR model has been developed that is able to predict the ethanol concentration at a given time during dissolution and resuspension of PPT C and PPT V. Even with a relatively small number of samples used for model calibration (N=34), predictions can be achieved within the calibrated ethanol concentration range (RMSEP=0.584% w / w ethanol), as demonstrated with a single test sample in the by-line (N=14).

[0399] Furthermore, real-time monitoring of ethanol concentration during PPT dissolution and resuspension was demonstrated by observing three runs with an online NIR probe followed immediately by spectral analysis. This enabled real-time information on the ethanol concentration within the reactor at a given moment, allowing inferences to be made about the dissolution state and resuspension progress of the PPT.

[0400] It should be understood that the invention disclosed and defined in this specification extends to all alternative combinations of two or more of the individual features mentioned or evident from the text or drawings. All these different combinations constitute various alternative aspects of the invention.

Claims

1. A method for determining the concentration of an analyte in a sample obtained from processing blood-derived plasma, the method comprising: - applying a light source in the near infrared spectrum to a test sample obtained from processed blood-derived plasma; - measuring the reflectivity, transmittance or transflectivity of the test sample in the near infrared wavelength range, thereby generating a test wavelength spectrum, - comparing said test wavelength spectrum to a reference wavelength spectrum obtained from a reference sample having a known concentration of said analyte to determine the concentration of said analyte in said sample.

2. The method of claim 1, wherein multivariate data analysis is performed on the test wavelength spectra.

3. A method for determining the concentration of an analyte in a sample obtained from processing blood-derived plasma, the method comprising: - applying a light source in the near infrared spectrum to a test sample obtained from processed blood-derived plasma, - measuring the reflectivity, transmittance or transflectivity of the test sample in the near infrared wavelength range, thereby generating a test wavelength spectrum, - comparing the test wavelength spectrum to a reference data set in the form of a model generated using multivariate analysis of processed reference wavelength spectra of reference samples having known concentrations of the analyte to determine the concentration of the analyte in the sample.

4. A method for generating a model to determine the concentration of an analyte in a sample obtained from plasma processing, the method comprising: - providing a training sample obtained from processing blood-derived plasma, wherein said sample has a known concentration of said analyte, - applying a light source in the near infrared spectrum to said training samples, - measuring the reflectivity, transmittance or transflectivity of the training sample in the near infrared wavelength range, thereby generating a training wavelength spectrum, - selecting a spectral region of interest within said training wavelength spectrum; - generating a model by applying multivariate analysis to said spectra to provide correlations with known concentrations of said analyte, A model for determining the concentration of analyte in samples obtained from plasma processing is thereby obtained.

5. The method of any one of claims 2 or 4, wherein the multivariate analysis is selected from partial least squares regression (PLS); PLS discriminant analysis (PLS-DA); ordinary least squares (OLS) regression; MLR (multivariate linear regression); OPLS (orthogonal-PLS); SVM (support vector machine); GLD (general discriminant analysis); GLMC (generalized linear models); GLZ (generalized linear and nonlinear models); LDA (linear discriminant analysis); classification tree; Cluster analysis; Neural network; Related to Pearson.

6. The method according to claim 3 or 4, wherein The model is a model generated using partial least squares (PLS) regression of processed wavelength spectra of samples with known concentrations of the analyte.

7. The method of any one of claims 4 to 6, wherein the generated model is judged using the following statistical parameters: the number of latent variables (PLS factors) in the model, ·deviation, RMSECV, RMSEP of independent test samples, ·R 2 , and / or RPD value.

8. The method of any one of claims 1 to 7, wherein the method comprises applying at least one spectral pre-processing to the wavelength spectrum.

9. The method of claim 8, wherein the spectral preprocessing is first-order derivative, vector normalization, or a combination of first-order derivative and vector normalization.

10. The method of any one of the preceding claims, wherein: The measurement mode is transflection.

11. The method of any of the preceding claims, wherein a light source in the near infrared range is applied to the training samples and / or the test samples using a probe adapted to emit light having a wavelength in the near infrared range.

12. The method of claim 11, wherein the probe is configured for inclusion in an industrial protein mixing, filtration or purification device for online measurement of the reflectance, transmittance or transflectance of the training sample in the near infrared wavelength range.

13. The method of any of the preceding claims, wherein the light source in the near infrared range is applied to the training sample and / or the test sample during mixing of the samples.

14. The method of any of the preceding claims, wherein the light source is applied to the one or more samples at an angle parallel to the direction of fluid flow during mixing of the one or more samples or at an angle of approximately 45° to the direction of fluid flow during mixing of the one or more samples.

15. The method of any of the preceding claims, wherein the analyte is total protein or alcohol.

16. The method of claim 15, wherein the analyte is total protein.

17. The method of claim 15, wherein the alcohol is ethanol.

18. The method of any one of claims 4 to 17, wherein the training sample is obtained from a conventional preparation of a blood-derived plasma product.

19. The method of any one of the preceding claims, wherein the analyte is a protein and the concentration of the protein in a reference or training sample is determined using the Dumas assay.

20. The method of any one of claims 4 to 19, wherein the training samples include concentrations of the analyte within a concentration range determined for the test samples.

21. The method of any of the preceding claims, wherein the sample comprising the analyte is a sample obtained from processing blood-derived plasma obtained from human blood.

22. The method of any of the preceding claims, wherein the sample is obtained or derived from processed blood-derived plasma, including fresh plasma, cold-depleted plasma, or cold-enriched plasma.

23. The method of claim 22, wherein the plasma is obtained from a plurality of donors and / or subjects and pooled.

24. The method of any of the preceding claims, wherein the sample is obtained or derived from hyperimmune plasma.

25. The method of any one of the preceding claims, wherein the sample containing the analyte is a resuspension of a precipitate or paste obtained from blood-derived plasma.

26. The method of any of the preceding claims, wherein the sample containing the analyte is a resuspension of a precipitate or paste, or is a fraction selected from the following: Cohn fraction I (Fr I), Cohn fraction II+III (Fr II+III), Cohn fraction I+II+III (Fr I+II+III), Cohn fraction II (Fr II), Cohn fraction III (Fr III), Cohn fraction IV (Fr IV), Cohn fraction V (Fr V), Kistler / Nitschmann precipitate A, Kistler / Nitschmann precipitate B, Kistler / Nitschmann precipitate C.

27. The method of claim 27, wherein the sample containing the analyte is a resuspension of a precipitate or paste, or is a fraction selected from the following: Cohn fraction II+III (Fr II+III), Cohn fraction I+II+III (Fr I+II+III), or Kistler / Nitschmann precipitate A.

28. The method of any of the preceding claims, wherein the sample comprising the analyte is a fraction prepared under conditions that produce Cohn Fraction I+II+III (Fr I+II+III) or Cohn Fraction IV (Fr IV).

29. The method of any of the preceding claims, wherein the sample is a turbid solution or suspension having a nephelometric turbidity unit (NTU) of 10 NTU or greater, 15 NTU or greater, 20 NTU or greater, 25 NTU or greater, 30 NTU or greater, 35 NTU or greater, 40 NTU or greater, 45 NTU or greater, 50 NTU or greater, 55 NTU or greater, 60 NTU or greater, 65 NTU or greater , equal to or greater than 70NTU, equal to or greater than 75NTU, equal to or greater than 80NTU, equal to or greater than 85NTU, equal to or greater than 90NTU, equal to or greater than 95NTU, equal to or greater than 100NTU, equal to or greater than 150NTU, equal to or greater than 200NTU, equal to or greater than 250NTU, equal to or greater than 300NTU, equal to or greater than 350NTU, equal to or greater than 400NTU, equal to or greater than 450NTU, equal to or greater than 500NTU, equal to or greater than 550NTU NTU, equal to or greater than 600 NTU, equal to or greater than 650 NTU, equal to or greater than 700 NTU, equal to or greater than 750 NTU, equal to or greater than 800 NTU, equal to or greater than 850 NTU, equal to or greater than 900 NTU, equal to or greater than 950 NTU, equal to or greater than 1,000 NTU, equal to or greater than 1,500 NTU, equal to or greater than 2,000 NTU, equal to or greater than 2,500 NTU, equal to or greater than 3,000 NTU, equal to or greater than 3,500 NTU , equal to or greater than 4,000 NTU, equal to or greater than 4,500 NTU, equal to or greater than 5,000 NTU, equal to or greater than 5,500 NTU, equal to or greater than 6,000 NTU, equal to or greater than 6,500 NTU, equal to or greater than 7,000 NTU, equal to or greater than 7,500 NTU, equal to or greater than 8,000 NTU, equal to or greater than 8,500 NTU, equal to or greater than 9,000 NTU, equal to or greater than 9,500 NTU or equal to or greater than 10,000 NTU.

30. The method of any of the preceding claims, wherein the sample is a turbid solution or suspension having a nephelometric turbidity unit (NTU) of 10 to 100 NTU, 10 to 90 NTU, 10 to 80 NTU, 10 to 70 NTU, 10 to 60 NTU, 10 to 50 NTU, 10 to 40 NTU, 10 to 30 NTU, 10 to 20 NTU, 20 to 100 NTU, 30 to 100 NTU, 40 to 100 NTU, 50 to 100 NTU, 60 to 100 NTU, 70 to 100 NTU, 80 to 100 NTU, or 90 to 100 NTU.

31. The method of any of the preceding claims, wherein the sample is a turbid solution or suspension having a maximum NTU of 10,000 NTU, 9,500 NTU, 9,000 NTU, 8,500 NTU, 8,000 NTU, 7,500 NTU, 7,000 NTU, 6,500 NTU, 6,000 NTU, 5,500 NTU, 5,000 NTU, 4,500 NTU, 4,000 NTU, 3,500 NTU , 3,000NTU, 2,500NTU, 2,000NTU, 1,500NTU, 1,000NTU, 950NTU, 900NTU, 850NTU, 800NTU, 750NTU, 700NTU, 650NTU, 600NTU, 550NTU, 500NTU, 450NTU, 400NTU, 350NTU, 300NTU, 250NTU, 200NTU, 150NTU or 100NTU.

32. The method of any of the preceding claims, wherein any or all of the steps of the method are performed in-line, at-line, off-line or in-line.

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