System and method for determining deamidation and immunogenicity of polypeptides

Abstract

To provide a system and method for determining and evaluating deamidation of a protein sample.SOLUTION: Characteristics of proteins, peptides, and / or peptoids can be determined via two-dimensional correlation spectroscopy and / or two-dimensional co-distribution spectroscopies. Spectral data of the proteins, peptides, and / or peptoids can be obtained with respect to an applied stress (for example, thermal stress). Two-dimensional correlation spectroscopy can be used to generate two-dimensional synchronous and asynchronous plots. The asynchronous plot provides enhanced resolution and sequential order of molecular events that occur as a function of the applied stress. Peaks may be identified in the asynchronous plot, and correlation of peaks that exhibit out-of-phase intensity changes can be used to determine existence and extent of deamidation events.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a system and method for determining deamidation and immunogenicity of polypeptides.

Background Art

[0002] The high attrition rate of drug candidates such as protein therapeutics is a major cost driver in drug development and continues to be an important issue in the biopharmaceutical industry. Immunogenicity, protein aggregation, deamidation, and oxidation are concerns for regulatory authorities because they can affect the efficacy and safety in patients. Proteins are complex molecules that are exposed to the possibility of non-enzymatic deamidation, the conversion of asparagine to aspartate or glutamine to glutamate, under various conditions. The generation of isomeric products is observed only under high pH conditions. Specifically, the process of deamidation in proteins is associated with both low and high pH conditions, as well as heat stress. Therefore, the risks of this occurrence include: (1) upstream processing during the cell culture production of therapeutic proteins, and / or (2) downstream processing during purification, (3) virus removal, and during storage and delivery, and (4) heat stress and / or low / high pH conditions.

[0003] Currently, there are limitations in high-throughput evaluation of protein deamidation in solution. Current techniques such as HPLC, NMR, and MS have limitations regarding the number of samples that can be analyzed, the evaluation of protein stability as a result of deamidation, and the resulting impact on efficacy and safety.

[0004] The mechanism of deamidation is kinetically driven and requires the adjacent residue (N+1) to be small in order to prevent stearic hindrance, allowing the formation of a succinimide intermediate, followed by hydrolysis of the -NH2 group and the residue becoming negatively charged. Current techniques used to detect deamidation are based on the separation of charged variants by high performance liquid chromatography ("HPLC") such as ion exchange (IEX) or reverse phase. Nuclear magnetic resonance spectroscopy ("NMR") is then used to identify changes in the structure and primary sequence within the protein. Mass spectrometry ("MS") has also been developed for the detection of asparagine deamidation to isoaspartate only at high pH. This MS technique requires fragmentation of the full-length charged variant protein and peptide mapping for exclusive detection of the mass difference of isoaspartate. This is a complex and time-consuming process.

Summary of the Invention

Means for Solving the Problems

[0005] For example, the technology of the present subject matter will be described according to various aspects described below. Below, various examples of aspects of the technology of the present subject matter will be described. These are illustrative and do not limit the technology of the present subject matter.

[0006] Aspects of the technology of the present subject matter provide a system and method for determining and evaluating deamidation of a protein sample under thermal duress. Specifically, this system and method provide for determining and evaluating deamidation within glutamine and within asparagine, as well as determining the size, identity, degree and mechanism of aggregation, and stability, target binding, and validity of bioassays. This system and method enable the evaluation of the developability and comparability of therapeutic proteins with only 1 μL volume per sample, independent of molecular weight, post-translational modifications, and / or formulation status. Furthermore, these empirical results can directly influence the design and re-engineering of proteins.

[0007] According to one aspect of the technology of the present subject matter, the systems and methods described herein involve obtaining and analyzing spectral data of proteins, such as infrared (IR) spectra, for example, IR spectra obtained using a quantum cascade laser (“QCL”) microscope. The systems and methods provide real-time high-throughput hyperspectral imaging (“HSI”) that enables monitoring of arrays of proteins in solution during heat stress. Unlike certain existing methods of monitoring proteins, this method does not require separation techniques and does not include flow channels. The system uses a QCL transmission microscope with linear response detection based on first principles, precise thermal control, and a unique heating cell holder with a multi-array type slide cell that allows for a fixed volume requirement. This provides a high-speed acquisition system that is up to 200 times faster than a Fourier transform infrared (“FT-IR”) microscope, with an improved signal-to-noise ratio (“SNR”) such that the size, identity, extent, and mechanism of aggregation can be determined. The QCL microscope spectral data is processed using analysis algorithms as described herein to determine the presence and extent of deamidation. The systems and methods can also process the spectral data to monitor and evaluate the stability of the colloids or evaluate other stress factor conditions such as pH.

[0008] The systems and methods provide analysis of hundreds of samples per day. Using the methods employed for spectral analysis, regions of deamidation are mapped, regions prone to aggregation are identified, and domain stability is established. The correlation dynamics software included in the system and used to implement aspects of the method enables correlation of side-chain modes used to examine proteins in solution under stress factor conditions. As a result, the data is highly informative and statistically robust.

[0009] According to one aspect of the present technology, the system and method use HSI for real-time monitoring and analysis of protein deamidation events under thermal stress and / or chemical stress (including using an array of therapeutic proteins in solution). The results of such monitoring and analysis have a predictive meaning and at the same time enable mapping of deamidation-prone sites. For example, deamidation can predict immunogenicity and / or the tendency to aggregate. This result is statistically robust. Further, by analyzing variants of protein candidates, comprehensive evidence for the selection of preclinical candidates at the early discovery stage can be provided. Further, the technology of the present subject matter can also explain the aggregation mechanism and unfolding of proteins, thereby providing molecular details of events that can cause immunogenicity. The selection of therapeutic protein candidates is based on the predictive power of the data processing methods and analysis methods described herein, based on HIS obtained using QCL microscopy. Other analytical tools currently used to evaluate the occurrence of deamidation (e.g., HPLC, NMR, and MS) can also be used in combination with the systems and methods disclosed herein, thus enabling the selection of stable candidates with a relatively low risk of candidate withdrawal while ensuring efficacy and safety.

[0010] Using the methods, systems, and instructions for processing the data described herein, the potential for deamidation, aggregation, and immunogenicity can be evaluated as part of the development of protein therapeutics. Studies conducted on protein samples using the technology of the present subject matter demonstrate the evaluation and determination of deamidation of asparagine residues and / or glutamine residues in an array of proteins in solution, and provide a direct detection method for aggregation of the drug substance (''DS'') and / or formulation (''DP'') in vitro or in situ, thereby providing validation of immunogenicity and anti-drug antibody (ADA) bioassays.

[0011] Additional features and advantages of the technology of this subject matter will be described in the following description, some of which will be apparent from this description or may be learned by the practice of the technology of this subject matter. The advantages of the technology of this subject matter are realized and achieved by the structures particularly pointed out in this specification, the claims, and the accompanying drawings.

[0012] It should be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the technology of the claimed subject matter.

[0013] The accompanying drawings, which are included to provide a further understanding of the technology of this subject matter and are incorporated in and constitute a part of this description, illustrate embodiments of the technology of this subject matter and together with the description serve to explain the principles of the technology of this subject matter.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2A

Figure 2B

Figure 3

Figure 4A

Figure 4B

Figure 4C

Figure 5

Figure 6A

Figure 6B

Figure 6C

Figure 7A

Figure 7B

Figure 7C

Figure 8A

Figure 8B

Figure 8C

Figure 9A

Figure 9B

Figure 9C

Figure 10A

Figure 10B

Figure 10C

Figure 11A

Figure 11B

Figure 11C

Figure 12A

Figure 12B

Figure 12C

Figure 13A

Figure 13B

Figure 13C

Figure 14A

Figure 14B

Figure 14C

Figure 15A

Figure 15B

Figure 15C

Figure 16A

Figure 16B

Figure 17A

Figure 17B

Figure 17C

Figure 18

Figure 19

Figure 20

Figure 21

Figure 22

Figure 23

Figure 24

Figure 25

Figure 26

DETAILED DESCRIPTION OF THE INVENTION

[0015] In the following detailed description, specific details are set forth in order to understand the technology of the present subject matter. However, it will be apparent to those skilled in the art that the technology of the present subject matter can be practiced without these specific details. In other instances, well-known structures and technologies are not shown in detail so as not to obscure the technology of the present subject matter.

[0016] A protein is a large organic compound made up of amino acids, which are arranged in a linear sequence and are joined to each other by peptide bonds between the carboxyl group and the amino group of adjacent amino acid residues. Most proteins fold into a unique three-dimensional structure. The shape into which a protein naturally folds is known as the native state of this protein. Many proteins can fold on their own simply due to the chemical properties of the amino acids of this protein, while others require the help of molecular chaperones to fold into the native state. There are the following four different aspects to the structure of a protein: · Primary structure: Amino acid sequence.

[0017] · Secondary structure: Regularly repeating local structures stabilized by hydrogen bonds. Since the secondary structure is local, there can be many regions with different secondary structures in the same protein molecule.

[0018] · Tertiary structure: The overall shape of a single protein molecule; the spatial relationship of the mutually secondary structures. · Quaternary structure: In this context, a shape or structure resulting from the interaction of multiple protein molecules, usually referred to as protein subunits, that functions as part of a larger assembly or protein complex.

[0019] Proteins are not exactly rigid molecules. In addition to this level of structure, proteins can shift between several related structures while performing their biological functions. In the context of this functional reconfiguration, these tertiary and quaternary structures are usually referred to as "conformations", and the transitions between them are called conformational changes.

[0020] Protein aggregation is characterized by the grouping of misfolded rigid proteins and is regarded as a widespread phenomenon throughout industrial bioprocesses. Aggregation is considered a major mode of proteolysis and often leads to the loss of protein immunogenicity and biological activity. Protein aggregation is extremely important in a wide variety of biomedical situations, from abnormal pathologies such as Alzheimer's and Parkinson's diseases to the manufacture, stability, and delivery of protein drugs.

[0021] Deamidation is regarded as a post-translational modification of proteins after protein biosynthesis that can potentially affect the stability, structure, and efficacy of therapeutic proteins, and may cause aggregation that can lead to undesirable immune responses such as immunogenicity and anti-drug antibody reactions (ADA). Residues that exhibit deamidation are asparagine, and to a lesser extent, glutamine. Deamidation converts asparagine to aspartate and / or glutamine to glutamate. The introduction of a negative charge at this site can potentially reduce the stability of the protein, causing the protein to aggregate, degrade, and reduce the binding selectivity and binding affinity to the target, resulting in loss of efficacy and safety. Post-translational modification of asparagine occurs readily when the adjacent residue (position N+1) is glycine, with reduced steric hindrance when a succinimide intermediate is formed, resulting in aspartate or isoaspartate. Deamidation events occur even in the absence of enzymes and are accelerated at high pH and / or temperature. Deamidation may indicate proteolysis within the cell, thus reducing the half-life of therapeutic proteins within the cell and potentially affecting PK / PD.

[0022] Aspects of the technology of the present subject matter provide a fast, accurate, and reproducible technique for real-time monitoring of deamidation events in an array of therapeutic proteins in solution under heat stress and / or chemical stress. For example, the technology of the present subject matter provides techniques for evaluating and monitoring the deamidation of asparagine and glutamine in therapeutic proteins in solution under heat stress at high and low pH. The systems and methods described herein enable comparative evaluation of full-length monoclonal antibodies under various concentrations and heat stress factor conditions. By comparison of real-time high-throughput HSI, it becomes possible to monitor an array of proteins in solution during heat stress. Spectral data from HSI can be analyzed to create a covariance (difference) spectrum. Then, 2D IR correlation techniques can be applied to this covariance spectrum to create synchronous plots and asynchronous plots. Along with changes in intensity, the intensity peaks within the synchronous plots and within the asynchronous plots can be analyzed. Changes in intensity and peak shifts within the spectral region of interest are analyzed, and this change represents the behavior of the protein under heat stress. Correlation relationships between peaks can be used to establish the occurrence of deamidation for a sample. By determining the order of molecular events during heat stress for each sample within the array, the behavior of the protein in solution can be described. The region where deamidation is occurring can be mapped, and based on the degree of deamidation, the stability of the protein being examined can be determined, and based on the order of molecular events, the thermal stability can be determined.

[0023] The computational methods and computational systems described herein provide a significant improvement compared to existing analyses of proteins. The computational methods and computational systems described herein create and store data in a form that facilitates efficient and meaningful analysis without the need to use several pieces of equipment. Thus, the computational methods and computational systems described herein can improve the efficiency of spectral data analysis for the evaluation of candidate drugs.

[0024] Aspects of the technology of the present subject matter include the use of two-dimensional correlation analysis spectroscopy ("2DCOS") and / or two-dimensional co-distribution spectroscopy ("2DCDS") to provide essential information regarding the degree and mechanism of deamidation of protein therapeutics. The methods described herein may include the analysis of side-chain modes as internal probes, providing information that supports the stability of structural motifs or structural domains within the protein. The methods described herein have been shown to be useful in high-throughput - developability and comparability assessment ("HT-DCA") by a design of experiments ("DOE") approach in accordance with Quality by Design ("QBD").

[0025] According to some embodiments, for example, as shown in FIG. 3, spectral analysis may be performed stepwise. According to some embodiments, the protein in the solution sample is perturbed (thermally, chemically, by pressure, or acoustically) to induce dynamic fluctuations in the vibrational spectrum. In step 310, raw spectral data may be collected and / or analyzed. Spectral data may be acquired at regular temperature intervals and continuously. According to some embodiments, this data may be baseline corrected.

[0026] According to some embodiments, the spectral data may be used to determine the presence and degree of deamidation events. For this purpose, the first low-temperature average spectrum is subtracted from subsequent spectra to create a dynamic spectrum. In step 320, a covariance (difference) spectrum may be created by subtracting the first low-temperature average spectrum (24° C.) from all subsequent spectra. As a result, this covariance (difference) spectrum includes positive and negative peaks and is also referred to as in-phase and out-of-phase with each other.

[0027] Note that the processes described herein do not require manual subtraction of water or other references (e.g., solutes) from spectral data. Such manual subtraction is a very subjective process that often occurs in spectral analysis of proteins. Instead, the processes described herein create a differential spectral data set based on perturbation of the sample of interest. This output can then be used for further analysis. The spectral contribution of water is automatically subtracted by subtracting from all subsequent spectra the first low-temperature average spectrum in which the water band overlaps with the amide I band. That is, the contribution of water and the vibrational modes of all proteins that were not perturbed, for example, by heat stress are subtracted, enabling evaluation of only the changes that occurred in the spectral region of interest (1780 - 1450 cm -1 ) during heat stress.

[0028] Next, as shown in step 330, detailed molecular evaluation of the protein in solution is obtained by applying 2D IR correlation techniques. In step 330, 2D IR correlation techniques can be applied to create a synchronous plot (step 340) and an asynchronous plot (step 350). For example, the spectral data can be fast Fourier transformed (“FFT”) to create a complex matrix, and from this complex matrix, an intensity matrix is obtained by cross-correlation product, and synchronous and asynchronous plots are created.

[0029] The synchronous plot represents the overall intensity changes that occur during perturbation within the spectral region of interest. Along the diagonal of this plot are peaks or bands that change throughout the spectrum (known as auto peaks). Off the diagonal are cross peaks that show the correlation relationships between auto peaks (i.e., the relationships between the observed changes in secondary structure). This synchronous plot can be used to associate intensity changes or shifts of in-phase peaks.

[0030] In the synchronous correlation spectrum, the auto-peaks located on the diagonal represent the degree of dynamic fluctuation of the spectral signal induced by perturbation. The cross-peaks represent the simultaneous changes of the spectral signals at two different frequencies, which suggests the cause of the associated or related intensity changes. When the sign of the cross-peak is positive, the intensities at the corresponding frequencies are both increasing or decreasing. When this sign is negative, one is increasing while the other is decreasing.

[0031] The asynchronous plot includes only the cross-peaks used to determine the order of molecular events that occurred in response to thermal stress or other applied perturbations. The asynchronous plot can be used to associate the intensity changes or shifts of out-of-phase peaks that occurred in response to thermal stress. For example, simultaneous with the observation of the increase in aspartate intensity in the ν(COO -1 ) vibration mode at 1572.0 cm - , the observation of the decrease in asparagine intensity in the related δ(NH -1 ) vibration mode at 1612.7 cm 2 can be used to indicate deamidation.

[0032] In the asynchronous correlation spectrum, cross-peaks occur only when the intensities fluctuate out of phase with each other for some Fourier frequency components of the signal fluctuation. The sign of the cross-peak is positive when the intensity change at frequency v2 occurs before the intensity change at frequency v1. The sign of the cross-peak is negative when the intensity change at frequency v2 occurs after the intensity change at frequency v1. When the translated same asynchronous cross-peak position in the synchronous plot falls in the negative region (Φ(v1, v2) < 0), the above sign rule is reversed.

[0033] Using 2D IR correlation spectroscopy, complex bands such as amide I and II bands can be decomposed. Specifically, 2D IR correlation enhances the spectral resolution of peaks underlying broad bands such as amide I and II bands by spreading them two-dimensionally. As described, synchronous plots and asynchronous plots are created by 2D IR correlation techniques. These plots are essentially symmetric and, for the sake of discussion, are analyzed with reference to the upper triangle. The synchronous plot (shown at 340) contains the following two types of peaks: (a) an auto-peak, which is a positive peak on the diagonal, and (b) a cross-peak, which is a peak off the diagonal that can be either positive or negative. The asynchronous plot (shown at 350) consists only of cross-peaks that associate out-of-phase peaks. As a result, this plot reveals that the spectral resolution is more improved. The following rules can be applied to establish the order of molecular events: I. When the asynchronous cross-peak ν2 is positive, ν2 is perturbed before ν1 (ν2→ν1).

[0034] II. When the asynchronous cross-peak ν2 is negative, ν2 is perturbed after ν1 (ν2←ν1). III. When the synchronous cross-peak (off-diagonal peak, not shown in Figure 3) is positive, the order of events is established exclusively using the asynchronous plot (Rules I and II).

[0035] IV. When the synchronous plot contains a negative cross-peak and the corresponding asynchronous cross-peak is positive, the order is reversed. V. When the synchronous plot contains a negative cross-peak and the corresponding asynchronous cross-peak is negative, the order is maintained.

[0036] ν 2 axisFor each peak observed, the order of events can be established. The order of each event can be summarized to create a table. At stage 360, a table summarizing the order of each event is used to create the order of the event plot. Above each step (event), spectroscopic information of the cross-peak ν2 is shown, and at the bottom of each step, the assignment of the corresponding peak or the biochemical information of each event is shown in the order perturbed as a function of temperature. An example is shown herein.

[0037] Those skilled in the art will note "Two-dimensional co-distribution spectroscopy to determine the sequential order of distributed presence of species" by Dr. Isao Noda, which describes algorithms suitable for use in 2D IR correlation analysis, Journal of Molecular Structure, Vol. 1069, pp. 51-54. The outline of the 2D IR correlation spectroscopy developed by Dr. Isao Noda using a series of infrared continuous spectra of a sample protein is as follows. The sample protein can include a monoclonal antibody (mAb). For example, using the QCL IR spectrum as a function of perturbation (in this case, heat stress (28-56 °C)), a covariance (difference) spectral data set can be obtained by subtracting the first spectrum from all subsequent spectra. For a system measured under the influence of an external perturbation that induces a change in the observed spectral intensity, a discretely sampled set of spectrum A(ν j ,t k ) can be obtained. The spectral variable ν j (j = 1,2,...,n) can be, for example, wavenumber, frequency, scattering angle, etc., and other variable t k (k = 1,2,...,m) represents the influence of the applied perturbation (e.g., time, temperature, and potential). t1 and t mOnly a continuously sampled spectral dataset obtained at a clearly defined observation interval with respect to [something] is used for 2D IR correlation analysis. For the sake of brevity, although this specification uses wavenumber and time to specify two variables, it must be understood that the use of other physical variables is also valid.

[0038] The covariance (difference) spectrum used in 2D IR correlation spectroscopy is

[0039]

Number

[0040] defined as, where

[0041]

Number

[0042] is the first spectrum of the dataset for creating the covariance spectrum. If the reference state is not known in advance, the reference spectrum can also be set as the time-averaged spectrum of the observation interval between t1 and t m and [something].

[0043] The synchronous 2D correlation intensity of the covariance spectral data is

[0044]

Number

[0045] defined by. The asynchronous 2D correlation intensity of the covariance spectral data is

[0046]

Number

[0047] defined by. The term N ijis an element of the so-called Hilbert-Noda transformation matrix,

[0048]

Number

[0049] as shown by. Applying the cross-correlation function to this difference spectrum dataset results in two separate, yet symmetric, 2D plots. The correlation intensity Φ(ν1, ν2) obtained as a function of two independent frequency axes ν1 and ν2 is a synchronous plot. The correlation intensity Ψ(ν1, ν2) obtained as a function of two independent frequencies ν1 and ν2 is an asynchronous plot. The synchronous plot contains positive peaks on the diagonal known as auto-peaks and summarizes the changes observed in the spectrum dataset. The relationship established in this synchronous plot associates spectral intensity changes that are (occurring simultaneously) in phase with each other. The asynchronous plot is a contour plot that associates out-of-phase intensity changes, improves the resolution of the spectral region of interest, and can be easily distinguished from the synchronous plot by lacking peaks on the diagonal. Both plots also contain off-diagonal peaks called cross-peaks, which correlate with the observed spectral changes. The observed spectral intensity changes are due to the incremental heat stress applied to the protein sample. Thus, information from both the synchronous and asynchronous plots allows the determination of the order of molecular events occurring under stress factors and conditions according to Noda's rules. The synchronous and asynchronous plots are essentially symmetric, and in this case too, for the sake of discussion, the upper triangle is always referred to for analysis. To determine the order of molecular events, we start with the plot with the most enhanced resolution (i.e., the asynchronous plot): I. When the asynchronous cross-peak ν2 is positive, ν2 is perturbed before ν1 (ν2 → ν1).

[0050] II. When the asynchronous cross-peak ν2 is negative, ν2 is perturbed after ν1 (ν2 ← ν1). III. If the corresponding synchronous cross-peak is positive, the order of events is established using the asynchronous plot (Rules I and II).

[0051] IV. However, if the corresponding synchronous cross-peak is negative and the asynchronous cross-peak is positive, the order is reversed. For each peak of interest in the defined spectral region observed on the ν2 axis, the order of the molecular events can be established. Then, as described herein, the peak of interest is used in the evaluation of protein deamidation under heat stress.

[0052] Referring again to FIG. XX, at stage 370, the perturbation region of the protein population distribution (80% threshold) in the solution is obtained by the co-distribution correlation plot. By co-distribution correlation analysis, the common behavior of the distributed population of proteins in the solution is obtained. Those skilled in the art are referred to Isao Noda, "Two-dimensional co-distribution spectroscopy to determine the sequential order of distributed presence of species," Journal of Molecular Structure, Vol. 1069, pp. 51-54, which describes an algorithm suitable for use in 2DCDS analysis.

[0053] t1 ≤ t k ≤ t m A set of m time-dependent spectra A(ν j , t k ) obtained continuously at the observation interval of, (time-averaged spectrum

[0054]

Number

[0055] In the case of being represented by Equation (2), the characteristic (time) index is

[0056] [Number]

[0057] defined as The dynamic spectrum used in this specification

[0058] [Number]

[0059] is the same as that defined by Equation (1). The corresponding characteristic time of the distribution of the spectral intensity observed at the wave number ν1 is

[0060] [Number]

[0061] shown by Although repetitive, the time used in this specification means a general description of the representative variables of the applied perturbation, and therefore, it is understood that it may be replaced by any other appropriate physical variables (for example, temperature, concentration, and pressure) specifically selected for the experimental conditions. The characteristic time

[0062] [Number]

[0063] is the first moment of the distribution density of the spectral intensity A(ν m ,t j ,t k ) along the time axis bounded by the observation interval between t1 and t with respect to the origin of the time axis (that is, t = 0). This corresponds to the position of the center of gravity of the observed spectral intensity distributed over this time.

[0064] Characteristic time of the time distribution of spectral intensities measured at two different frequencies ν1 and ν2

[0065]

Number

[0066] and

[0067]

Number

[0068] On the premise of and, the synchronous co-distribution spectrum and the asynchronous co-distribution spectrum are

[0069]

Number

[0070] defined as, where T(ν1, ν2) is

[0071]

Number

[0072] the total combined difference given by. The synchronous co-distribution intensity Γ(ν1, ν2) is a measure of the coexistence or overlap of the distributions of two separate spectral intensities along the time axis. In contrast, the asynchronous co-distribution intensity Δ(ν1, ν2) is a measure of the difference between the distributions of two spectral signals. The term "co-distribution" indicates the comparison of two separate distributions, and this measurement criterion is distinguished from the concept of "correlation" based on the comparison of two fluctuations.

[0073] By combining equations 5, 6, and 8, the equation for the asynchronous co-distribution spectrum is

[0074]

Number

[0075] is shown by The value of Δ(ν1,ν2) is

[0076]

Number

[0077] or

[0078]

Number

[0079] is set to zero when it meets the conditions of any of the above, which indicates the absence of the spectral intensity signal at any wave number. The synchronous co-distribution spectrum is

[0080]

Number

[0081] obtained from the relationship of In the asynchronous co-distribution spectrum, and in the case of a cross-peak where the sign is positive (i.e., Δ(ν1,ν2)=0), the presence of the spectral intensity at ν1 is mainly distributed in the initial stage along the time axis compared to the case of ν2. On the other hand, when Δ(ν1,ν2)<0, the order is reversed.

[0082]

Number

[0083] in the case of, the average distribution of the spectral intensities observed at two wave numbers over time is similar. The sign of the synchronous co-distribution peak is always positive, which limits the information content of the synchronous spectrum to some extent beyond a clear qualitative measurement of the degree of overlap of the distribution patterns.

[0084] The co-distribution (2DCDS) analysis can provide elements of protein stability or protein aggregation states, or can provide any process under investigation in a weighted manner. Using 2DCDS, a column of the distributed presence of species can be directly provided along the variable axis during stress (e.g., temperature, concentration, pH, etc.). This technique can be used as a complementary tool to enhance 2DCOS analysis for the direct identification of the presence of intermediate species. According to some embodiments, perturbation-dependent spectra are continuously obtained during the observation interval. The 2D correlation spectra (synchronous spectrum and asynchronous spectrum) are derived from the spectral variations. The synchronous co-distribution intensity is measured as the coexistence or overlap of the distributions of two separate spectral intensities along the perturbation axis. The asynchronous co-distribution intensity is measured as the difference in the distributions of two spectral signals. In the case of a cross-peak where the sign is positive (i.e., Δ(ν1,ν2)>0), the presence of the spectral intensity at ν1 is mainly distributed at the initial stage along the time axis compared to the case of ν2. On the other hand, when Δ(ν1,ν2)<0, the order is reversed.

[0085]

Number

[0086] In this case, the average distribution of the spectral intensities observed at two wavenumbers over time is similar. The difference between 2DCOS analyses provides an average description of the path due to the perturbation process and the influence of this perturbation process on the sample, and 2DCDS analysis provides a weighted element of the population of molecules (proteins) during the perturbation process. The results of 2DCOS and 2DCDS are direct and simplified descriptions of the elements that change in the spectral data due to the perturbation.

[0087] According to some embodiments, for example, as shown in FIG. 1, a system for performing data analysis may include at least the components shown for carrying out the functions of the methods described herein. Data may be obtained from multiple sources and may include information regarding HSI images obtained by a QCL transmission microscope, information from an automated liquid handling system, and information from a bioassay. The data obtained may be provided to one or more computing devices (e.g., a preprocessor and a processor) for analysis. A module may be provided for performing or managing the analysis of this data. Information from this module may also be run or exported to a web browser, a mobile application, or a desktop application. Such modules may include a correlation dynamics module, a visual model generation module, and / or a human interaction module. The human interaction module may be provided, for example, as a web browser, a mobile application, or a desktop application. The modules may communicate with each other. In some embodiments, the modules may be implemented in software (e.g., subroutines and code). For example, the modules may be stored in memory and / or data storage, such as laboratory memory and / or backup memory, and executed by a processor. The processor may include a business engine having pre-programmed rules and instructions for acting on the data obtained. The business engine may communicate with a business memory that stores sample profiles for use in data analysis according to the methods described herein. In some aspects, some or all of the modules may be implemented in hardware (e.g., an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a state machine, gated logic, discrete hardware components, or other suitable devices), firmware, software, and / or combinations thereof.Further features and functions of these modules according to various aspects of the technology of the present subject matter are further described in the present disclosure.

[0088] According to some embodiments, as shown, for example, in FIG. 2A, a method of validating and preparing the acquired data may be implemented. As shown in FIG. 2A, input data is loaded 200 and provided to a processor (e.g., the processor shown in FIG. 1). The type of data is identified 201, and a determination 202 is made as to whether this data is of a valid type. If the data is of a valid type, the data is processed 203, stored 204, and it is determined 205 that the validation and preparation of the acquired data is successful. However, if it is determined that the data is not of a valid type, an error is displayed 206 and it is determined 207 that the validation and preparation has failed. The data may be converted and / or stored if the validation is successful, and may be rejected by displaying an error to the user if the validation fails.

[0089] According to some embodiments, as shown, for example, in FIG. 2B, a method of analyzing the acquired data may be implemented. The type of data is verified with respect to an appropriate signal-to-noise ratio relative to a threshold. Based on the verification, the data may be subjected to analysis or may be subjected to a smoothing filter process prior to analysis.

[0090] According to some embodiments, as shown, for example, in FIG. 2B, the data may be analyzed by operations including application of baseline correlation, performance of normal distribution analysis, determination of field intensity, calculation of aggregate size, selection of a region of interest, calculation of an average, calculation of a covariance, calculation of a correlation, and calculation of a co-distribution.

[0091] Data manipulation may include the automatic recognition of regions of interest (ROIs) for the identification of particles and solutions. The size and number of particles can be determined to confirm the population distribution of the particles. Data manipulation can be performed to ensure compliance such as S / N ratio determination, baseline correction, determine the amount of water vapor, and determine the signal intensity of the elements of interest within the spectral region being studied. The data output for statistical analysis can be simplified, particularly using experimental design methods. The intensity and spectral position of the elements of interest can be output as a comma-separated file ( * .csv). A covariance or dynamic spectral dataset can be created based on the perturbation of the sample of interest, and this output can be used for further analysis. For example, the data output can be provided in a format that facilitates integration with other biological analysis results for comparative likelihood assessment derived from the type of perturbation, additives, protein therapeutics, protein concentration, temperature, acquisition date, and / or biological analysis techniques. This approach will enable the performance of statistical analysis for all experiments conducted under similar conditions. More importantly, the results of the DOE analysis will be an independent document in a state where a final report can be made and decisions can be made.

[0092] According to some embodiments, the methods and systems described herein can apply a correlation function to covariance or dynamic spectral data, as described above, to create synchronous and asynchronous plots. Changes in spectral data that are in-phase with each other (e.g., peak intensity) can be correlated as obtained in a synchronous plot. Elements that change in the spectral data can be determined. The overall maximum intensity change of the spectral data can be determined. The overall minimum intensity change of the spectral data can be determined. For curve fitting analysis, the minimum number of underlying spectral contributions in broad bands such as the amide bands of proteins and peptides can be determined, thereby enabling the determination of the composition of the secondary structure. In particular, with respect to broad bands in the spectrum, the resolution of the spectral region being studied can be enhanced. Further, by analyzing the synchronous and asynchronous plots, the order of events can be determined.

[0093] Changes in spectral data (e.g., peak intensity) that are out of phase with each other can be correlated as obtained in an asynchronous plot. From the asynchronous plot, the order of events that describes the behavior of the protein in molecular detail can be obtained. By performing a detailed evaluation of this plot, it is possible to confirm the order of events. Alternatively, or in combination, this process can be automated. A combined dispersion function can be applied to the covariance or dynamic spectral data to create an integrated asynchronous plot that can be directly interpreted to determine the order of events. Alternatively, this method can be used to verify the above interpretation regarding the description of the molecular behavior of proteins, which is a complex description.

[0094] By analyzing the out-of-phase correlation of peaks in an asynchronous plot, evidence of deamidation as a result of heat stress can be obtained. If there is a correlation of peaks showing out-of-phase intensity changes in the asynchronous plot, it can be determined that deamidation has occurred. As a long-term solution to the complexity of the attributes that need to be correlated and resolved, a machine learning approach can be implemented. Research examples The evaluation of three NIST mAbs (standard and candidate RMs 8671 and 8670, respectively) using various concentration ranges: (low) 1.0 - 1.5 μg / μL, (intermediate) 2.0 - 2.4 μg / μL, (intermediate - high) 2.8 - 10 μg / μL was used to evaluate the feasibility and comparability of systems and methods for use in monitoring and determining protein deamidation. NIST mAb (lot number 14HB - D - 002) is an IgG1κ isotype with a molecular weight of 150 kD, a homodimer composed of two heavy - chain subunits and two light - chain subunits containing inter - and intra - chain disulfide bonds. In addition, this protein has post - translational modifications (PTMs) including N - linked glycosylation sites at N 300 with in the Fc region.

[0095] The specific NIST mAb protein samples used in this evaluation are as follows: PDS NIST mAb (RM 8671), a sample of NIST mAb RM8671 that was stored at the Protein Dynamic Solutions facilities in Puerto Rico and thus subjected to extreme heat stress when electricity and other infrastructure were destroyed during Hurricane Maria in 2017; NIST mAB (RM 8671), a sample of NIST-provided mAb RM 8671 that was not exposed to heat stress; and NIST mAb candidate (8670), a therapeutic antibody candidate provided by NIST.

[0096] The protein sample to be studied is λ max with a theoretical molar extinction coefficient (ε) of 212,270 M -1 cm -1 at λ = 280 nm. A dilution series of the NIST mAb samples was performed using 12.5 mM L-histidine buffer at pH 6.00. Concentration determination was also performed on this sample. The diluted NIST mAb samples were used for concentration determination by UV spectrophotometry together with 12.5 mM L-histidine buffer at pH 6.00 as an appropriate reference. The UV spectra of the diluted NIST mAb (RM 8671 and 8670) samples were acquired at room temperature (24 °C) using a Jasco (Tokyo, Japan) model V-630 spectrophotometer and a Starna (Essex, UK) removable quartz cell model DMV-Bio (0.2 mm path length). Two scans were added together within the spectral region of 235 - 320 nm at a scan speed of 400 nm / min and a data pitch of 1.0. For all spectra collected, single-point baseline correction was performed at 320 nm. Desired plots and analyses were performed using MicroCal's Origin 7 professional software.

[0097] For the experiment design, a predetermined amount (e.g., 1 μL) of each sample was applied, along with each reference, to a predefined well on a custom-designed CaF₂ slide cell. Coordinates were provided for automated image acquisition while controlling and maintaining the heat of the slide cell. Care was taken to collect the background at each temperature to eliminate potential coherence effects due to the quantum cascade laser.

[0098] Automated image acquisition of an array of protein samples in solution was performed under precise thermal control of a custom heating slide holder and slide cell using a real-time Hyperspectral Imaging Quantum Cascade Laser Transmission Microscope (QCLTM). The path length of each sample in this array was known, and quantitative analysis such as that described in PCT / US Patent Application Publication No. 2017 / 014338, which is incorporated herein by reference, was possible. The HSI raw spectral data of each sample protein was captured by the QCLTM and this HSI data was evaluated for the presence of particles / aggregates. Further, after determining and baseline-correcting the average spectral data of each sampled protein solution, 2D IR correlation plots and co-distribution plots were created to further evaluate deamidation events.

[0099] During the inspection of the HSI obtained for the sample proteins, no particles or fewer than five particles were observed. The observed differences were due to the extent of the effect of deamidation on the stability of the mAb. For example, from this evaluation, in the NIST mAb candidate and the PDS NIST mAb exposed to high temperature for a long time in Hurricane Maria in Puerto Rico, due to heat stress, asparagine N localized in the FC domain at a low concentration (1.0 - 1.5 μg / μl) 318The event of deamidation was confirmed. Similarly, at higher mAb concentrations, the colloidal stability of the NIST mAb standard (RM 8671) and the candidate (RM8670) changed, which could also be a sign of deamidation, but further evaluation is needed. Finally, it was observed that the NIST mAB standard has higher stability compared to the NIST mAb candidate.

[0100] Aggregates were visualized using the HSI obtained for each protein solution sampled in this array. In the dataset used in this study, less than 5 aggregates were observed or no aggregates were observed. Furthermore, for the buffer 12.5 mM L-histidine at pH 6.0, there were also no aggregates. Based on the optical settings, any detected aggregates were in the size range of 4.3 μm to 2.0 mm.

[0101] The use of a linear response microbolometer focal plane array (480 × 480 pixels) detector was enabled by three quantum cascade lasers that improve the signal-to-noise ratio (SRN). For spatial resolution, a low magnification objective lens (4×) with an aperture number (NA) of 0.3 NA within a field of view (FOV) of 2 × 2 mm providing a pixel size with a spatial resolution of 4.25 × 4.25 μm was used. For each protein sample in this array, the QCL IR spectrum was collected with a resolution of 4 cm−1 within a spatial region of 1780 - 1450 cm−1. To prevent the coherence effect caused by the fluctuations of the QCL, the background was collected at each set temperature when thermal equilibrium (4 minutes) was achieved. For each sample within this array, the typical HSI acquisition time was 0.4 minutes. 2 of the field of view (FOV) was used. For each protein sample in this array, in the spatial region of 1780 - 1450 cm−1 -1 within 4 cm−1 -1 resolution. To prevent the coherence effect caused by the fluctuations of the QCL, the background was collected at each set temperature when thermal equilibrium (4 minutes) was achieved. For each sample within this array, the typical HSI acquisition time was 0.4 minutes.

[0102] Comma-separated file ( *The raw spectral data was saved as a.csv file. Analyses and plots were created from this raw data as needed. QCL IR overlays and 2D IR correlation plots were created from this raw data. The deamidation evaluation module used to perform the analysis in this evaluation study included a cursor function to enable unbiased cross-peak intensity changes and positions, which are useful for determining the order of molecular events.

[0103] Figures A - 4C show the mid-IR spectral region from 1780 - 1450 cm -1 and hyperspectral images acquired in the temperature range of 28 - 56 °C at 4 °C temperature intervals for each protein sample in the array. Figure 4A shows the hyperspectral image acquired for PDS NIST mAb RM 8671 at 1 μg / μL at 28 °C and 56 °C. Figure 4B shows the hyperspectral image acquired for NIST mAb RM 8671 at 2 μg / μL at 28 °C and 56 °C. Figure 4C shows the hyperspectral image acquired for NIST mAb candidate RM 8670 at 2.4 μg / μL at 28 °C and 56 °C. HSI and background acquisitions were performed when the set temperature was achieved after a 4-minute equilibration period. Each HSI consisted of 223,000 QCL IR spectra. Each average spectrum at the defined temperature represents the average of 223,000 spectra within a 2 × 2 mm 2 FOV. This FOV corresponds to the diameter of the well for each sample in the array.

[0104] Next, QCL IR overlays were created for each NIST mAb in the array and only the baseline was corrected. Figure 5 shows the 1780 - 1450 cm -1Shows the QCL IR spectrum overlay of amide I and II bands where the absorption of L - histidine and H2O overlaps in the spectral region. Samples of PDS NIST mAb standard (RM 8671), NIST mAb standard (RM 8671), and NIST mAb candidate (RM 8670) were studied at two different concentrations. The upper columns in Figure 5 represent the QCL IR spectrum overlay for concentrations of 1 - 1.5 μg / μL, and the lower columns show the overlay for concentrations of 2 - 2.5 μg / μL. Generally, the low concentrations within the range were the PDS NIST and NIST mAb standard (RM 8671), and the high concentrations within the range were the NIST mAb candidate (RM 8670). Each protein sample had a low - temperature average spectrum at 28°C.

[0105] Next, using the low - temperature average spectra at 28°C for each protein sample, difference spectra were created. As a result, intensity changes and peak shifts within the target spectral region could be analyzed, and thus, the behavior of proteins in solution due to heat stress could be represented.

[0106] Table 1 shows an overview of the backbone vibration modes and positions used in this evaluation.

[0107]

Table 1

[0108] Table 2 shows an overview of the side - chain modes and positions used in this evaluation.

[0109]

Table 2

[0110] The band positions used for the comparability evaluation represent the average of all 2D IR correlation peaks determined for the entire dataset studied for NIST mAb standard RM 8671 and NIST mAb candidate RM 8670. 1700 - 1600 cm -1In the case of the amide I band, mainly due to C=O stretch, C-N stretch contributes slightly, and to a relatively low degree, N-H deformation mode contributes and is sensitive to conformational changes. Overall for all three mAbs, the QCL IR spectrum consists of β-turn (1693.1 cm -1 ), hinge loop (1665.2 cm -1 ), α-helix (1665.2 cm -1 ), and β-sheet (1635.6 cm -1 ). These secondary structures are commonly observed in IgG. In the case of side-chain modes, there are several overlapping vibration modes within the amide I band and others located within the amide II band (1600 - 1500 cm -1 ). Immediately before the amide I band, the following side-chain modes are located: three p-substituted aromatic peaks (1748.7, 1726.7, and 1705.0 cm -1 ) representing both phenylalanine and tyrosine side-chains, glutamine ν(C=O) (1670.0 cm -1 ), asparagine ν(C=O) (1678.3 cm -1 ) and δ(NH2) (1612.7 cm -1 ), and lysine δ as (NH3 + ) (1621.0 cm -1 ). Side-chain modes located within the amide II band are as follows: glutamine δ(NH2) (1591.0 cm -1 ), histidine ν(C=C) (1600.1 cm -1 ), aspartate ν(COO - ) (1572.0 cm -1 ), and two different aspartates (one of these is presumed to be involved in salt-bridge interactions) ν(COO - ) (1540.7 and 1559.0 cm -1 ), lysine δ s (NH3 + ) (1525.0 cm -1 ), and finally tyrosine at (1519.0 cm -1 ). The vibration modes of arginine and tryptophan can also be considered.

[0111] When subtracting the low-temperature average spectrum from all subsequent average spectra, the contribution of H2O and the vibrational modes of all proteins that were not perturbed by heat stress are subtracted, thereby enabling the evaluation of only the changes that occurred in the target spectral region (1780 - 1450 cm -1 ) during heat stress. By performing 2D IR correlation analysis, a detailed molecular evaluation of the protein in solution was obtained.

[0112] The correlation function was applied to create the following two different plots: (1) a synchronous plot (showing the overall intensity change within the target spectral region), and (2) an asynchronous plot (showing the enhanced resolution and the order of molecular events occurring in response to heat stress). Furthermore, the asynchronous plot showed a detailed correlation of peaks indicating out-of-phase intensity changes indicative of deamidation. For example, as detailed below, in this evaluation, a decrease in the intensity of asparagine at 1612.7 cm -1 related to the δ(NH2) vibrational mode, attributed to deamidation, was observed, and furthermore, a simultaneous increase in intensity regarding the aspartate intensity in the ν(COO -1 ) vibrational mode at 1572.0 cm - ) was observed.

[0113] The overall thermal stability of the studied protein was also evaluated. The overall thermal stability was determined using a heat-dependent plot by evaluating the onset of the heat transition temperature. Regarding the following, as a function of temperature in the range of 28 - 56 °C, 1780 - 1450 cm -1Within the spectral region, the maximum value of the QCL IR peak position was observed: i) NISST mAb RM 8671 and RM 8670 alone at concentrations of 1 - 1.5 μg / μL, 2 - 2.4 μg / μL, and 2.8 or 10 μg / μL; ii) NIST mAb 86781 and RM 8670 at concentrations of 1 - 1.5 μg / μL, 2 - 2.4 μg / μL, and 2.8 or 10 μg / μL, and the reference; and iii) the reference of deionized H2O and 12.5 mM L-histidine buffer at pH 6.0. The PDS NIST mAb standard (RM8671), NIST mAb standard (RM8671), and NIST mAb candidate (RM 8670) at low concentrations in the range of 1 - 2.8 μg / μL showed the start of the same peak shift at 50°C. However, the NIST candidate (RM8670) at 10 μg / μL showed low stability, and a peak shift occurred at 32.5°C. In the case of deionized H2O, no shift in the maximum value of the peak was observed within this temperature range, and in the case of 12.5 mM L-histidine buffer, the start of the thermal transition was observed at 52.5°C, thus indicating that it is more stable compared to the NIST mAb. This suggests that the changes observed with the NIST mAb were due to the endogenous behavior caused by thermal stress.

[0114] In this evaluation, aggregation events were also monitored, but under the conditions examined, no aggregation during thermal stress was observed for any of the three NIST mAb (RM8671 and 8670) samples.

[0115] As described in more detail below, in this evaluation, spectral data was further monitored to detect and analyze deamidation events. In this evaluation, the amide (νC=O) at 1678.3 cm -1 and the amide bending mode (δNH2) at 1612.7 cm -1 within the asparagine side-chain carbonyl stretching mode and the aspartate carboxylate stretching mode (νCOO -1 at 1572.0 cm -)Focused on the analysis of cross-peaks associated with this. The correlation between these peaks was confirmed to demonstrate the occurrence of deamidation for the samples (NIST mAb, RM 8671, and NIST mAb candidate 8670).

[0116] By determining the order of molecular events during heat stress (28 - 56 °C) for each sample in the array, a description of the behavior of the protein in solution was obtained. This experimental approach did not determine the overall heat transition temperature for the three NIST mAb standard (RM 8671 and 8670) protein samples. Instead, it determined the differences in stability among the three proteins examined and determined whether deamidation was observed. The heat transition temperature can be determined using well-established procedures as needed. In the case of this study, as discussed above, to establish and understand the differences in sensitivity due to concentration with respect to such individual events that can cause changes in protein stability and potentially reduce effectiveness, the data were separated based on low (1.0 - 1.5 μg / μL), intermediate (2.0 - 2.4 μg / μL), and intermediate to high concentrations (2.8 - 10.0 μg / μL) of the protein. Next, in this study, the regions where deamidation occurred for the protein in solution under heat stress were mapped. Finally, in this study, the stability was determined based on the degree of deamidation, and the thermal stability was determined based on the order of molecular events.

Example

[0117] Example 1 Using the methods and systems described herein, for PDS NIST mAb at 1 μg / μL, NIST mAb at 1 μg / μL, and NIST mAb candidate at 1.5 μg / μL in 12.5 mM L - histidine at pH 6.0 that were under heat stress in the temperature range of 28 - 56 °C, regarding 1780 - 1450 cm -1Comparative 2D IR correlation spectroscopy was performed within the spectral region of. For each of the three studied sample proteins, QCL IR spectral overlays of the amide I and amide II bands within the spectral region corresponding to the temperature range of 28 - 56 °C were created. From these spectral overlays, synchronous plots and asynchronous plots corresponding to the temperature range of 28 - 56 °C were created using 2D IR correlation. Figure 6A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region corresponding to the temperature range of 28 - 56 °C for the PDS NIST mAb sample. -1 Figure 6B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 6A, and Figure 6C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 6A. Figure 7A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region corresponding to the temperature range of 28 - 56 °C for the NIST mAb sample. -1 Figure 7B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 7A, and Figure 7C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 7A. Figure 8A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region corresponding to the temperature range of 28 - 56 °C for the NIST mAb candidate sample. -1 Figure 8B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 8A, and Figure 8C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 8A. -1

[0118] From the analytical interpretation of this 2D IR correlation plot, the behavior of three mAb samples in the low concentration range (1.0 - 1.5 μg / μL) during heat stress was obtained. As shown in Fig. 9A, using the rules of Noda described herein, from the analysis of the synchronous and asynchronous plots shown in Figs. 6B and 6C, the order of events for PDS NIST mAb was obtained. As shown in Fig. 9A, the order of molecular events for PDS NIST mAb (RM 8671) was as follows: tyrosine residue (1519.0 cm -1 ), followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then two glutamate ν(COO -1 ) at 1540.7 and 1559.0 cm - , followed by two aspartate ν(COO-) at 1580.0 cm -1 and 1572.0 cm -1 (presumably involved in hydrogen bonding or salt - bridge interactions with nearby tyrosine and lysine), β - sheet (1635.6 cm -1 ), followed by the helical region (1653.8 cm -1 )(observed for all mAbs at low concentration), then lysine δ as (NH3 + )(1621.0 cm -1 ), followed by asparagine side - chain mode δ(NH2)(1612.7 cm -1 ), and glutamine δ(NH2)(1591.0 cm -1 ), followed by histidine ν(C = C)(1600.1 cm -1 )(all of these residues are presumably in close proximity to each other), then the hinge loop (1665 cm -1 ), followed by glutamine ν(C = O)(1670.0 cm -1 ), and asparagine side - chain mode δ(C = O)(1678.3 cm -1 ), followed by phenylalanine and tyrosine p - substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1)(Suggesting a change in the accessibility of the mAb to the aqueous solvent due to partial unfolding near 56 °C), and ultimately, the β-turn (1693.1 cm -1 ) is perturbed. This final molecular event is common to all mAbs.

[0119] Figure 9B shows the order of events for the NIST mAb obtained from the synchronous and asynchronous plots shown in Figures 7B and 7C. As shown in Figure 9B, the order of events for the NIST mAb (RM 8671) was as follows: tyrosine residue (1519.0 cm -1 ), followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then glutamate ν(COO -1 ) at 1540.7 cm - ), followed by aspartate ν(COO-) at 1580.0 cm -1 ), followed by β-sheet (1635.6 cm -1 ) and helical region (1653.8 cm -1 ), then lysine δ as (NH3 + )(1621.0 cm -1 ), followed by aspartyl side-chain mode δ(NH2)(1612.7 cm -1 ) and glutamine δ(NH2)(1591.0 cm -1 ), followed by histidine ν(C=C)(1600.1 cm -1 )(all of these residues are presumed to be in close proximity to each other), then hinge loop (1665 cm -1 ), followed by glutamine ν(C=O)(1670.0 cm -1 ), then glutamate ν(COO -1 ) at 1559.0 cm - ), and aspartate ν(COO -1 ) at 1572.0 cm - ) and aspartyl side-chain mode ν(C=O)(1678.3 cm -1 ), followed by phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1)(suggesting a change in the accessibility of the mAb to the aqueous solvent due to partial unfolding near 56 °C), and ultimately, the β-turn (1693.1 cm -1 ) is perturbed. The NIST mAb that was not subjected to the stress associated with Hurricane Maria had two stable vibrational modes (1559.0 cm -1 ) for glutamate ν(COO - ) and 1572.0 cm -1 ) for aspartate ν(COO-). These are modes associated with deamidation.

[0120] Figure 9C shows the order of events for the NIST mAb candidate (RM 8670) obtained from the synchronous and asynchronous plots shown in Figures 8B and 8C. As shown in Figure 9C, the order of events for the NIST mAb candidate (RM 8670) was as follows: The tyrosine residue (1519.0 cm -1 ) had the lowest stability, followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then glutamate ν(COO -1 ) at 1540.7 cm - ), followed by aspartate ν(COO-) at 1580.0 cm -1 ), then glutamine δ(NH2) (1591.0 cm -1 ), followed by lysine δ as (NH3 + )(1621.0 cm -1 ), then asparagine side-chain mode δ(NH2) (1612.7 cm -1 ), followed by histidine ν(C=C) (1600.1 cm -1 ), then the secondary structure was perturbed within the β-sheet (1635.6 cm -1 ), helical region (1653.8 cm -1 ), and hinge loop (1665 cm -1 ), followed by the deamidation event glutamine ν(C=O) (1670.0 cm -1 ), then 1559.0 cm -1for glutamate ν(COO - ), and for aspartate ν(COO -1 ) and aspartate side-chain mode ν(C=O) (1678.3 cm - ) at 1572.0 cm, followed by phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ) (suggesting a change in the accessibility of the mAb to the aqueous solvent due to partial unfolding near 56 °C), and finally, the β-turn (1693.1 cm -1 -1 ) is perturbed.

[0121] The differences in the order of these molecular events were due to the stability of these NIST mAbs prior to heat stress. For all three mAbs at low concentrations, the reliability was high due to the repetition of the events observed at both the initial and final stages of heat stress. Further analysis was performed on the cross-peaks in the asynchronous plots that appear to be destabilized at various times, because these cross-peaks may be related to the deamidation process that changes the domain stability of the mAb. Example 2 Using the methods and systems described herein, 1780 - 1450 cm -1 comparative 2D IR correlation spectroscopy was performed in the spectral region for PDS NIST mAb (RM 8671) at 2 μg / μL in 12.5 mM L-histidine at pH 6.0, NIST mAb (RM 8671) at 2 μg / μL, and NIST mAb candidate (RM 8670) at 2.4 μg / μL that were heat stressed in the temperature range of 28 - 56 °C. For each of the three studied sample proteins, 1780 - 1450 cm -1A QCL IR spectral overlay of the amide I and amide II bands within the spectral region was created. From these spectral overlays, synchronous plots and asynchronous plots corresponding to the temperature range of 28 - 56 °C were created using 2D IR correlation. Figure 10A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region of 1780 - 1450 cm -1 corresponding to the temperature range of 28 - 56 °C for the PDS NIST mAb sample. Figure 10B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 10A, and Figure 10C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 10A. Figure 11A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region of 1780 - 1450 cm -1 corresponding to the temperature range of 28 - 56 °C for the NIST mAb sample. Figure 11B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 11A, and Figure 11C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 11A. Figure 12A shows the QCL spectral overlay of the amide I and amide II bands within the spectral region of 1780 - 1450 cm -1 corresponding to the temperature range of 28 - 56 °C for the NIST mAb candidate sample. Figure 12B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 12A, and Figure 12C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 12A.

[0122] From the analytical interpretation of this 2D IR correlation plot, the behavior of three mAb samples in the intermediate concentration range (2.0 - 2.4 μg / μL) during heat stress was obtained. As shown in Figure 13A, using the rules of nodes described herein, the order of events for the PDS NIST mAb was obtained from the analysis of the synchronous and asynchronous plots shown in Figures 10B and 10C. As shown in Figure 13A, the order of molecular events for the PDS NIST mAb (RM 8671) was as follows: tyrosine residue (1519.0 cm -1 ), followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then glutamate ν(COO at 1540.7 cm -1 - ), followed by aspartate ν(COO-) at 1580.0 cm -1 ), followed by β-sheet (1635.6 cm -1 ), then lysine δ as (NH3 + )(1621.0 cm -1 ), followed by helical region (1653.8 cm -1 ), glutamate ν(COO-) at 1559.0 cm -1 and aspartate ν(COO at 1572.0 cm -1 - )(along with aspartate side chain mode δ(NH2)(1612.7 cm -1 ) and glutamine δ(NH2)(1591.0 cm -1 )) (suggesting the deamidation process), followed by histidine ν(C=C)(1600.1 cm -1 ), then the hinge loop (1665 cm -1 ) is perturbed, followed by glutamine ν(C=O)(1670.0 cm -1 ), then aspartate side chain mode ν(C=O)(1678.3 cm -1 ), followed by phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ), and finally, the β-turn (1693.1 cm -1 ) is perturbed.

[0123] Figure 13B shows the order of events for the NIST mAb obtained from the synchronous and asynchronous plots shown in Figures 11B and 11C. As shown in Figure 13B, the order of events for the NIST mAb (RM 8671) was as follows: tyrosine residue (1519.0 cm -1 ), followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then glutamate ν(COO -1 ) at 1540.7 cm - ), followed by aspartate ν(COO-) at 1580.0 cm -1 ), followed by β-sheet (1635.6 cm -1 ), followed by helical region (1653.8 cm -1 ), then lysine δ as (NH3 + )(1621.0 cm -1 ), followed by hinge loop (1665 cm -1 ), then aspartyl side chain mode δ(NH2)(1612.7 cm -1 ), followed by glutamine ν(C=O)(1670.0 cm -1 ), glutamine δ(NH2)(1591.0 cm -1 ), then histidine ν(C=C)(1600.1 cm -1 ), then glutamate ν(COO -1 ) at 1559.0 cm - , and aspartate ν(COO -1 ) at 1572.0 cm - ), followed by aspartyl side chain mode ν(C=O)(1678.3 cm -1 ), followed by phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ), and finally, the β-turn (1693.1 cm -1 ) is perturbed.

[0124] Figure 13C shows the order of events for the NIST mAb candidate (RM 8670), obtained from the synchronous and asynchronous plots shown in Figures 12B and 12C. As shown in Figure 13C, the order of events for the NIST mAb candidate (RM 8670) was as follows: The tyrosine residue (1519.0 cm -1 ) had the lowest stability, followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then glutamate ν(COO - ) at 1540.7 cm -1 , followed by a β-sheet (1635.6 cm -1 ), then a helical region (1653.8 cm -1 ), followed by aspartate ν(COO-) at 1580.0 cm -1 , lysine δ as (NH3 + )(1621.0 cm -1 ), then asparagine side chain mode δ(NH2)(1612.7 cm -1 ), followed by a hinge loop (1665 cm -1 ), then histidine ν(C=C)(1600.1 cm -1 ), then glutamine δ(NH2)(1591.0 cm -1 ), glutamine ν(C=O)(1670.0 cm -1 ), followed by glutamate ν(COO -1 ) at 1559.0 cm - (suggesting deamidation); followed by aspartate ν(COO -1 ) at 1572.0 cm - , then asparagine side chain mode ν(C=O)(1678.3 cm -1 ), followed by phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ), and finally, the β-turn (1693.1 cm -1 ) is perturbed. Example 3 Using the methods and systems described herein, NIST mAb (RM 8671) at 2.8 μg / μL and NIST mAb candidate (RM 8670) at 10.0 μg / μL in 12.5 mM L-histidine at pH 6.0, which were subjected to heat stress within the temperature range of 28 - 56°C, were analyzed by comparative 2D IR correlation spectroscopy in the spectral region of 1780 - 1450 cm -1 A QCL IR spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C was created for NIST mAb at 2.8 μg / μL and NIST mAb candidate at 10.0 μg / μL. From these spectral overlays, synchronous plots and asynchronous plots corresponding to the temperature range of 28 - 56°C were created using 2D IR correlation. Figure 14A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb sample. Figure 14B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 14A, and Figure 14C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 14A. Figure 15A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb candidate sample. -1 A QCL IR spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C was created for NIST mAb at 2.8 μg / μL and NIST mAb candidate at 10.0 μg / μL. From these spectral overlays, synchronous plots and asynchronous plots corresponding to the temperature range of 28 - 56°C were created using 2D IR correlation. Figure 14A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb sample. Figure 14B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 14A, and Figure 14C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 14A. Figure 15A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb candidate sample. -1 A QCL IR spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C was created for NIST mAb at 2.8 μg / μL and NIST mAb candidate at 10.0 μg / μL. From these spectral overlays, synchronous plots and asynchronous plots corresponding to the temperature range of 28 - 56°C were created using 2D IR correlation. Figure 14A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb sample. Figure 14B shows the synchronous plot created based on the QCL spectral overlay data shown in Figure 14A, and Figure 14C shows the asynchronous plot created based on the QCL spectral overlay data shown in Figure 14A. Figure 15A shows the QCL spectral overlay of the amide I and amide II bands in the spectral region of 1780 - 1450 cm corresponding to the temperature range of 28 - 56°C for the NIST mAb candidate sample. -1Shows the QCL spectrum overlay of the amide I and amide II bands within the spectral region. Figure 15B shows a synchronous plot created based on the QCL spectrum overlay data shown in Figure 15A, and Figure 15C shows an asynchronous plot created based on the QCL spectrum overlay data shown in Figure 15A. The relatively high concentrations shown in the synchronous and asynchronous plots may reflect one or more of the following: (1) changes in the colloidal stability of the protein due to an increase in concentration during heat stress; (2) the appearance of intermolecular interactions that are less frequent at low protein concentrations; and / or (3) deamidation events of glutamine, when combined with deamidation events of asparagine residues that occur more readily (kinetically favorable compared to glutamine), may reduce the stability of the NIST mAb.

[0125] From the analytical interpretation of this 2D IR correlation plot, the order of molecular events at intermediate and high concentrations of 2.8 and 10.0 μg / μL for the NIST mAb standard (RM 8671) and the NIST mAb candidate (RM 8670) during heat stress was obtained. As shown in Figure 16A, using the rules of Noda described herein, the order of events for the PDS NIST mAb was obtained from the analysis of the synchronous and asynchronous plots shown in Figures 14B and 14C. As shown in Figure 16A, the order of events for the NIST mAb (RM 8671) at intermediate (2.8 μg / μL) was as follows: the tyrosine residue (1519 cm -1 ) was perturbed first, followed by lysine (1525 cm -1 ), then aspartate ν(COO-) at 1580.0 cm -1 , two glutamate ν(COO -1 ) at 1540.7 and 1559.0 cm - and aspartate ν(COO-) at 1572.0 cm -1 (presumed to be involved in hydrogen bonding and salt bridge interactions); then the β-sheet (1635.6 cm -1 ), then the helical region (1653.8 cm -1 ) was perturbed, followed by glutamine δ(NH2) (1591.0 cm -1) and glutamine ν(C=O) (1670.0 cm -1 ), then the hinge loop (1665 cm -1 ) is perturbed, followed by lysine δ as (NH3 + )(1621.0 cm -1 ), then the asparagine side-chain mode δ(NH2) (1612.7 cm -1 ), followed by histidine ν(C=C) (1600.1 cm -1 ), then the asparagine side-chain mode ν(C=O) (1678.3 cm -1 ), followed by the phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ), and finally, the β-turn (1693.1 cm -1 ) is perturbed.

[0126] Figure 16B shows the order of events for the NIST mAb candidate (RM 8670) obtained from the synchronous and asynchronous plots shown in Figures 15B and 15C. As shown in Figure 13C, the order of events for the NIST mAb candidate (RM 8670) at high (10 μg / μL) concentration was as follows: tyrosine residue (1519.0 cm -1 ), followed by lysine δ s (NH3 + )(1525.0 cm -1 ), then two glutamate ν(COO-) at 1540.7 and 1559.0 cm -1 , followed by two aspartate ν(COO-) at 1580.0 cm -1 and 1572.0 cm -1 (presumably involved in hydrogen bonding or salt-bridge interactions with nearby tyrosine and lysine), then the secondary structure is perturbed at the β-sheet (1635.6 cm -1 ), followed by the helical region (1653.8 cm -1 ) and the hinge loop (1665 cm -1 ), followed by glutamine ν(C=O) (1670.0 cm -1 ), then lysine δ as (NH3 + )(1621.0 cm-1 ), followed by the asparagine side chain mode δ(NH2) (1612.7 cm -1 ), and the glutamine δ(NH2) (1591.0 cm -1 ); (These suggest that they are stable residues that have not undergone deamidation), followed by the histidine ν(C=C) (1600.1 cm -1 ), then the asparagine side chain mode ν(C=O) (1678.3 cm -1 ), followed by the phenylalanine and tyrosine p-substituted aromatic ring modes (1726.7, 1748.7, 1705.0 cm -1 ), and finally, the β-turn (1693.1 cm -1 ) is perturbed. Determination of heat stress-induced deamidation From the evaluation of the asynchronous plots for the PDS NIST mAb Standard (RM 8671), NIST mAb Standard (RM 8671), and NIST mAb Candidate (RM 8670) shown in FIGS. 6C, 7C, and 8C, a multivariate relationship consistent with protein deamidation was demonstrated. Therefore, from the data evaluated in this evaluation, it is demonstrated that HSI using QCL microscopy and the methods disclosed herein is selective and sensitive for the determination of asparagine and glutamine deamidation induced by heat stress.

[0127] FIGS. 17A, 17B, and 17C are asynchronous plots for the PDS NIST mAb Standard (RM 8671), NIST mAb Standard (RM 8671), and NIST mAb Candidate (RM 8670) corresponding to the asynchronous plots shown in FIGS. 6A, 6B, and 6C, but additional markings have been made to show evidence of the observed deamidation. As shown in FIGS. 17A, 17B, and 17C, deamidation in the low concentration range (1 - 1.5 μg / μL) as a function of heat stress is evident from the out-of-phase correlations highlighted by white circles in the asynchronous plots. In each of FIGS. 17A, 17B, and 17C, the white circles designated (a) and also indicated by left arrows are at 1572.0 cm -1Represents the increase in the intensity of aspartate ν(COO-) at. (b) The white circles designated as represent the aspartate δ(NH2) at 1612.7 cm -1 Represents the aspartate δ(NH2) at. Finally, the white circles designated as (c) and also indicated by the upward arrow represent the aspartate ν(C=O) carbonyl stretch of the amide side-chain mode at 1678 cm -1 Represents the aspartate ν(C=O) carbonyl stretch of the amide side-chain mode at. For both the PDS NIST mAb standard (RM 8671) and the NIST mAb candidate (RM 8670), in the asynchronous contour plots where deamidation is a significant event due to stress factor conditions, the aspartate cross-peaks are not clearly observed when compared to the NIST mAb standard (RM8671).

[0128] Regarding the important cross-peaks in FIGS. 17A, 17B, and 17C, the intensity changes were determined and used in the deamidation analysis. FIGS. 18A, 18B, and 18C show bar graphs of the intensity changes at the positions designated as (a), (b), and (c) in FIGS. 17A, 17B, and 17C, respectively. These bar graphs further show the relative stability of the beta-sheet / helical secondary structure (α / β). In particular, by monitoring the cross-peaks located in the beta-sheet associated with the deamidation event, (a) is a peak reflecting the formation of aspartate via ν(COO-) during the conversion to aspartate in the deamidation process, (b) is a peak reflecting the loss of aspartate via δ(NH2) during the change to aspartate in the deamidation process, and (c) is a peak representing the perturbation of the aspartate side-chain ν(C=O) during the change to aspartate in the deamidation process.

[0129] In this evaluation, evidence of glutamine deamidation for the NIST mAb candidate at low concentrations during heat stress has also been found, indicating that the evaluation of glutamine residues can also be used for the identification and mapping of deamidation events. FIG. 19 shows 1780 - 1485 cm for the NIST mAb candidate at low concentrations during heat stress -1Shows an asynchronous plot within the spectral region. Monitored the important cross peaks within this plot and identified the following three important peaks located in the β-sheet associated with deamidation: (a) a peak representing the formation of glutamate via ν(COO-) during the conversion to glutamate, (b) a peak representing the loss of glutamine via δ(NH2) during the conversion to glutamate, and (c) a peak representing the perturbation of the glutamine side chain ν(C=O) during the conversion to glutamate.

[0130] Figure 20 is a bar graph summarizing the ratio of the intensity changes of the important cross peaks identified in Figure 19. By analyzing the intensity changes of the major cross peaks, deamidation events are confirmed. From this result, the deamidation of glutamine within the NIST mAb candidate (RM 8670) is confirmed. This process significantly contributes to the destabilization of the mAb.

[0131] Figure 21 is a diagram showing the mechanism of asparagine deamidation along with the major vibrational modes used to monitor events during heat stress. QCL IR is very selective for molecular events during deamidation and provides sensitive detection. This vibrational mode serves as an internal probe of this process in intact proteins in solution during stress. Also monitored are the intensity changes associated with deamidation events (asparagine / glutamine) and the relative stability of the secondary structure. Examples of backbone ν(C=O) affected by deamidation events include: (a) the formation of aspartate via ν(COO-) during the formation of the succinimide intermediate and the conversion to aspartate, (b) the loss of asparagine via δ(NH2), and (c) the perturbation of the asparagine side chain ν(C=O).

[0132] Deamidation is considered a post-translational modification that can affect the stability, structure, and efficacy of therapeutic proteins and may cause aggregation that can lead to unwanted immune responses. Residues that show deamidation are asparagine and, to a lesser extent, glutamine. Post-translational modification of asparagine occurs readily when the adjacent residue (at position N+1) is glycine, with reduced steric hindrance when a succinimide intermediate is formed, resulting in aspartate or isoaspartate. Deamidation events occur even in the absence of enzymes and are accelerated at high pH and / or temperature. Deamidation may indicate proteolysis intracellularly, thus reducing the half-life of therapeutic proteins intracellularly and potentially affecting PK / PD.

[0133] By using the systems and methods described herein, it is possible to evaluate whether deamidation as a post-translational modification is widespread in protein solutions and whether it affects protein stability in solution. To do so, it is beneficial to focus on the analysis of the sites most likely to undergo deamidation. From the investigation of the primary sequence of the NIST mAb standard IgG1κ described herein, it was revealed that there are only two asparagine residues within the entire protein sequence that meet the above-described N+1 criterion: 1) N located within a β-turn that is also exposed to the aqueous environment 369 G 370 and 2) N 300 3 10 downstream from the glycosylation site and located within a helix 318 N 319 G

[0134] Furthermore, N 369The number of glutamates adjacent to 369 N may lead to the destabilization of the β-sheet or hinge loop (i.e., the FC domain) where N 10 is localized. Another candidate is the N 318 located in the 3

[0135] helix within the Fc domain that also has adjacent glutamate, aspartate, histidine, and tyrosine residues, which may be the main cause of the perturbation levels observed at low concentrations. Deamidation of glutamine residues with the lowest level of steric hindrance is distributed across both the heavy and light chains. More importantly, this glutamine residue is located in the variable FAB region and further in the CDRs within the light chain. In contrast, asparagine residues that are prone to deamidation are limited to the FC domain. Investigation of the glutamine residues of the NIST mAb standard (RM 8671) with an adjacent glycine residue (position Q+1) to identify surrounding adjacent residues enabled the mapping and subsequent identification of QG as the cause of the deamidation event. Immunogenicity risk Bioassays have long been used to assess the potential of therapeutic proteins to be immunogenic. Therapeutic proteins represent the second largest biopharmaceutical category after vaccines. To date, in the biopharmaceutical industry, the use of binding antibody-based screening, collectively referred to as bioassays, has been used to evaluate the potential of therapeutic proteins to induce an immunogenic or anti-drug antibody (ADA) response. Unfortunately, these bioassays sometimes produce false positive or false negative results. This has motivated the FDA to draft immunogenicity guidelines in 2016. Generally, regulatory authorities around the world require the implementation of orthogonal analytical tools to validate bioassays for assessing the risk of immunogenicity and / or ADA.

[0136] Protein aggregation is a common factor in both immunogenicity and the ADA in situ response. Aggregation is measured directly, without using probes, based on first principles data obtained from the platform technology used to implement the systems and methods described herein. This platform technology consists of a dedicated liquid handling system, a real-time quantum cascade laser microscope with a modified stage to improve the signal-to-noise ratio (SNR), a slide cell, a heated slide cell holder, a PLC controller, and a computer system that implements software modules for analyzing data and storing and communicating results. As shown in Figure 22, this platform technology may include a liquid handling system 2201 for sample preparation and a spectral image acquisition system 2202 such as an HSI imaging system that uses a QCL microscope for monitoring arrays of proteins in solution under stress. A data management system (e.g., a cloud storage system) may be provided that communicates with the liquid handling system 2201 and the special image acquisition system 2202. This data management system 2203 may also communicate with a remote computer processing system 2204, enabling remote or offline analysis of the data.

[0137] This platform technology may be implemented in an array-based method to enable reproducible determination of aggregation induced by therapeutic proteins in human serum under physiological temperature ranges (37 - 41 °C). The array-based method requires minimal samples and can have results determined prior to human clinical trials based on predictive profiles of adverse events based on gender, medical history, or current prescriptions. This provides a predictive tool for the design of subsequent clinical trials. Furthermore, the quality and statistical robustness of the results obtained are suitable for big data analysis and machine learning.

[0138] Orthogonal execution of platform technologies implementing array-based methods can provide validation of current bioassays performed with biopharmaceuticals containing therapeutic proteins. Appropriately designed ADA assays should be based on the rationale of the immunogenicity test paradigm at the investigational new drug (IND) application stage. When positive immunogenicity results are obtained, an ADA assay is required.

[0139] This platform technology further provides an evaluation of the analytical approach to existing immunogenicity assays. The validation process includes an assessment of sensitivity, specificity, selectivity, and accuracy requirements. The assessment of aggregation is at the core of this process and can be confirmed by a very selective and sensitive technique that is statistically robust. The use of animal models for immunogenicity screening is questioned for its transferability / applicability to humans based on the results of animal models and clinical trials.

[0140] The proposed method for immunogenicity and ADA risk assessment is that there is no risk of the patient or donor having an immune response to the therapeutic protein product, because this analysis is performed on sample serum and not in vivo. The serum required for the triple assay is only 100 μL. This analysis is designed to include appropriate negative and positive controls.

[0141] Table 3 shows an overview of a typical triple assay setup for comparison of immunogenicity at various dosing levels.

[0142]

Table 3

[0143] Real-time evaluation involves the analysis of samples as soon as possible after sampling and before banking the samples. An aliquot of human serum sample serves as a negative control, additional control samples contain the formulation, and a series of serum samples are exposed to various amounts of therapeutic protein product according to the FDA draft guidance on immunogenicity. To assess the presence of aggregation in the sample serum, the analysis can be performed at determined times. This platform technology provides a highly selective and sensitive approach for the direct determination of aggregates that enables the assessment of comparability between the biosimilar and the reference substance (originator). If aggregation is observed, the degree of aggregation can be determined and followed up with a titration ADA assay.

[0144] The platform technology for the titration ADA assay is designed to measure the magnitude of the ADA response by assessing the degree of aggregation in each serum sample. Aggregation events would likely be considered a risk to patient safety. Aggregation events in serum or PBMC may also correlate with a decrease in efficacy if they persist during ADA titration. The accuracy of the ADA assay is evaluated with three independent preparations of the same sample per slide cell with a coefficient of variation of less than 10%. This evaluation includes low, medium, and high ranges for the validation of this assay.

[0145] FIG. 23 is a flowchart showing the operation of an exemplary design of an experimental method according to some aspects of the technology of the present subject matter. As shown in FIG. 23, an experimental technique design is applied to the obtained biological information and sequence comparison information. Then, for example, the obtained data is subjected to spectral analysis at 2302 by using the correlation analysis technique described with respect to FIG. 3. Then, the result of the spectral analysis can be subjected to the comparative analysis 2303 described herein.

[0146] FIG. 24 is a flowchart showing the operation of an exemplary method for ADA screening and immunogenicity risk assessment. FIG. 25 is a flowchart showing the operation of an exemplary comparative analysis that can be implemented using the platform technology and methods described herein.

[0147] FIG. 26 is a block diagram showing an exemplary computer system in which a computer processing device (e.g., of FIG. 4) can be implemented. In certain embodiments, computer system 1900 can be implemented using hardware in a dedicated server, integrated in another entity, or distributed across multiple entities, or using a combination of software and hardware.

[0148] Computer system 1900 includes a bus 1908 or other communication mechanism for communicating information, and a processor 1902 coupled to bus 1908 for processing information. By way of example, computer system 1900 can implement one or more processors 1902. Processor 1902 can be a general-purpose microprocessor, a microcontroller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a state machine, gate-type logic, discrete hardware components, and / or any other suitable entity capable of performing calculations or other operations on information.

[0149] In addition to hardware, computer system 1900 may include code (e.g., processor firmware, protocol stack, database management system, operating system, or code constituting one or more combinations of those stored in included memory 1904) that creates an execution environment for the computer programs in question, and as this memory, random access memory (RAM) connected to bus 1908 to store information and instructions executed by processor 1902, flash memory, read only memory (ROM), programmable read only memory (PROM), erasable PROM (EPROM), registers, hard disk, removable disk, CD-ROM, DVD, and / or any other suitable storage device. Processor 1902 and memory 1904 may be supplemented by, or incorporated in, dedicated logic circuitry.

[0150] The commands can be stored in the memory 1904 and can also be implemented in one or more computer program products (i.e., one or more modules of computer program instructions encoded in a computer-readable medium for execution by or to control the operation of a computer system 1900) and in any method known to those skilled in the art (e.g., but not limited to, computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architecture languages (e.g., Java,.NET), and / or application languages (e.g., PHP, Ruby, Perl, Python)). The commands can also be implemented in computer languages such as, for example, array languages, aspect-oriented languages, assembly languages, authoring languages, command-line interface languages, compiler languages, concurrent languages, curly-bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extended languages, fourth-generation languages, functional languages, interactive-mode languages, interpreter-type languages, iterative languages, list-based languages, little languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multi-paradigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, offside rule languages, procedural languages, reflective languages, rule-based languages, script languages, stack-based languages, synchronous languages, syntax-processing languages, visual languages, wirth languages, and / or xml-based languages. The memory 1904 can also be used to store temporary variables or other intermediate information during the execution of the instructions executed by the processor 1902.

[0151] The computer programs discussed herein are not necessarily in correspondence with files of a file system. The program can be stored in a part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that hold one or more modules, subprograms, or portions of code). The computer program can be deployed to be executed on one computer or can be located at one site or distributed across multiple sites and executed on multiple computers interconnected by a communication network. The processes and logic flows described herein are performed by one or more programmable processors executing one or more computer programs, which can perform functions by manipulation of input data and generation of output.

[0152] The computer system 1900 further includes a data storage device 1906 (e.g., a magnetic disk or an optical disk) connected to a bus 1908 for storing information and instructions. The computer system 1900 can be connected to various devices (e.g., devices 1914 and 1916) via an input / output module 1910. The input / output module 1910 can be any input / output module. Exemplary input / output modules 1910 include data ports (e.g., USB ports), audio ports, and / or video ports. In some embodiments, the input / output module 1910 includes a communication module. Exemplary communication modules include network interface cards such as Ethernet cards, modems, and routers. In certain aspects, the input / output module 1910 is configured to connect to multiple devices such as an input device 1914 and / or an output device 1916. Exemplary input devices 1914 include a keyboard and / or a pointing device (e.g., a mouse or a trackball), whereby a user can provide input to the computer system 1900. Other types of input devices 1914 (e.g., tactile input devices, visual input devices, voice input devices, and / or brain-computer interface devices) can also be used to provide interaction with the user. For example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, and / or tactile feedback), and the input from the user can be received in any form such as acoustic, voice, tactile, and / or electroencephalogram input. Exemplary output devices 1916 include a display device (e.g., a cathode ray tube (CRT) monitor or a liquid crystal display (LCD) monitor) for displaying information to the user.

[0153] According to certain embodiments, the client device and / or server may be implemented using computer system 1900 in response to execution by processor 1902 of one or more sequences of one or more instructions included in memory 1904. Such instructions may be read into memory 1904 from another machine-readable medium, such as data storage device 1906. Execution of the sequences of instructions included in memory 1904 causes processor 1902 to perform the process steps described herein. One or more processors in a multiprocessing arrangement may also be used to execute the sequences of instructions included in memory 1904. In some embodiments, various aspects of the present disclosure may be implemented using circuitry incorporated in hardware, instead of or in combination with software instructions. Therefore, aspects of the present disclosure are not limited to any particular combination of hardware circuitry and software.

[0154] Various aspects of the subject matter described herein may be implemented in a computer processing system (e.g., a data server) that includes back-end components, or in a computer processing system (e.g., an application server) that includes middleware components, or in a computer processing system (e.g., a client computer and / or a web browser having a graphical user interface through which a user may interact with an implementation of the subject matter described herein) that includes front-end components, or in any combination of one or more such back-end, middleware, or front-end components. The components of system 1900 may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks and wide area networks.

[0155] As used herein, the term "machine-readable storage medium" or "computer-readable medium" refers to any medium that participates in providing instructions to a processor 1902 for execution. Such a medium may take many forms (e.g., but not limited to, non-volatile media, volatile media, and transmission media). Examples of non-volatile media include optical disks or magnetic disks such as data storage device 1906. Examples of volatile media include dynamic memory such as memory 1904. Examples of transmission media include coaxial cables, copper wire, and fiber optics, such as the wires that make up bus 1908. Common forms of machine-readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic medium, CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH EPROM, any other memory chip or cartridge, or any other medium that can be read by a computer. A machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter that generates a machine-readable propagated signal, or a combination of one or more of these.

[0156] As used herein, "processor" may include one or more processors, and "module" may include one or more modules. In one aspect of the technology of this subject matter, a machine-readable medium is a computer-readable medium in which instructions are encoded or stored, and is an arithmetic element that defines a structural and functional relationship between the instructions and the rest of the system that enables the realization of the functions of these instructions. The instructions may be realizable, for example, by a system or a processor of this system. The instructions may be, for example, a computer program including code. A machine-readable medium may include one or more media.

[0157] As used herein, the word "module" refers to logic embodied in hardware or firmware, or a collection of software instructions, possibly with input and output points, written in a programming language such as C++. A software module may be linked to a compiled, executable program, installed in a dynamic link library, or written in an interpreted language such as BASIC. It will be understood that a software module can be callable from other modules or itself, and / or can be called in response to detected events or interrupts. Software instructions can be embedded in firmware such as EPROM or EEPROM. It will further be understood that a hardware module can be composed of connected logic units such as gates and flip-flops, and / or can be composed of programmable units such as programmable gate arrays or processors. The modules described herein are preferably implemented as software modules, but may be represented in hardware or firmware.

[0158] It is contemplated that a module can be integrated into a smaller number of modules. One module can also be separated into multiple modules. The modules described can be implemented as hardware, software, firmware, or any combination thereof. Additionally, the modules described can exist in various locations connected via a wired or wireless network, or the Internet.

[0159] In general, it will be understood that a processor may include, for example, a computer, program logic, or other substrate configurations that represent data and instructions and operate as described herein. In other embodiments, a processor may include a controller circuit, a processor circuit, a processor, a general-purpose single-chip or multi-chip microprocessor, a digital signal processor, an embedded microprocessor, a microcontroller, and the like.

[0160] Furthermore, it will be understood that in one embodiment, program logic may be advantageously implemented as one or more components. This component may advantageously be configured to be executed on one or more processors. Examples of such components include, but are not limited to: software or hardware components, modules, such as software modules, object-oriented software components, class components, and task components, process methods, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.

[0161] The foregoing description is provided to enable a person skilled in the art to practice the various configurations described herein. Although the subject technology has been specifically described with reference to various drawings and configurations, these are for illustrative purposes only and should not be construed as limiting the scope of the subject technology.

[0162] There may be many other ways to implement the subject technology. The various functions and elements described herein may be divided in a different manner than shown without departing from the scope of the subject technology. Various modifications to these configurations will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other configurations. Therefore, many changes and modifications may be made to the subject technology by those skilled in the art without departing from the scope of the subject technology.

[0163] It is understood that the specific order or hierarchy of steps in the processes of the present disclosure is an illustration of an exemplary approach. Based on design preferences, it is understood that the specific order or hierarchy of the process steps can be rearranged. Some of the steps can be implemented simultaneously. The accompanying methods show the elements of the present invention of various steps in the order of samples and are not meant to be limited to the specific order or hierarchy shown.

[0164] As used herein, the phrase "at least one" preceding a series of items modifies the list as a whole, rather than each member (i.e., each item) of the list, with the terms "and" or "or" used to separate any of these items. The phrase "at least one" does not require a selection of at least one of each of the listed items; rather, this phrase allows for the meaning of including at least one of any one of these items, and / or at least one of any combination of these items, and / or at least one of each of these items. By way of example, the phrases "at least one of A, B, and C" or "at least one of A, B, or C" each refer to only A, only B, or only C; any combination of A, B, and C; and / or at least one of each of A, B, and C.

[0165] Terms such as "upper", "bottom", "front", "rear", etc., and the like, when used in the present disclosure, should be understood to refer to any reference system rather than the normal gravity reference system. Therefore, the upper surface, bottom surface, front surface, and rear surface may extend upward, downward, obliquely, or horizontally in the gravity reference system.

[0166] Further, when the terms "include", "have", or the like are used in this specification or in the claims, such terms are intended to be inclusive in the same manner as the term "comprise" as interpreted when "comprise" is used as a transitional term in the claims.

[0167] The word "exemplary" as used herein means "serving as an example, instance, or illustration". An exemplary embodiment described herein is not necessarily to be construed as preferred or advantageous over other embodiments.

[0168] References to elements in the singular are not intended to mean "one and only one" unless explicitly so stated, but rather "one or more". The masculine pronoun (e.g., "his") includes the feminine and neuter pronouns (e.g., "her" and "its"), and vice versa. The term "some" refers to one or more. Headings and subheadings that are underlined and / or in italics are used for convenience only and do not limit the technology of the subject matter, nor are they to be referred to in connection with the interpretation of the description of the subject matter. All structural and functional equivalents to the various elements of the various configurations described throughout this disclosure that are known or later become known to those of ordinary skill in the art are hereby expressly incorporated by reference and are intended to be embraced by the technology of the subject matter. Further, what is disclosed herein is not intended to be made available to the public regardless of whether such disclosure is expressly recited in the above description.

[0169] Certain aspects and embodiments of the technology of this subject matter are described, but these are shown by way of example only and are not intended to limit the scope of the technology of this subject matter. In fact, the methods and systems described herein may be embodied in various other forms without departing from their spirit. The appended claims and their equivalents are intended to cover such forms or modifications as fall within the scope and spirit of the technology of this subject matter.

Claims

1. In a method for processing data representing at least one characteristic of a protein, a peptide, and a peptoid, using a quantum cascade laser microscope to obtain spectral data of at least one of the protein, the peptide, and the peptoid without using a probe or an additive related to the applied perturbation; applying two-dimensional correlation analysis to create an asynchronous correlation plot of at least one of the protein, the peptide, and the peptoid; and identifying at least one peak related to deamidation of at least one of the protein, the peptide, and the peptoid in the asynchronous correlation plot.

2. The method according to claim 1, further comprising determining an order of the distributed presence of spectral intensity changes related to the applied perturbation using the at least one peak.

3. The step of using the at least one peak Two wave numbers ν １ and ν ２ The method according to claim 2, comprising the step of determining whether the at least one peak corresponding to the two wave numbers has a positive value.

4. The step of using the at least one peak Two wave numbers ν １ and ν ２ A step of determining whether or not the at least one peak corresponding to the two wave numbers has a negative value comprises the method according to claim 2.

5. The method according to claim 1, further comprising identifying a plurality of peaks in the asynchronous correlation plot and determining that a deamidation event has occurred when there is a correlation of peaks showing intensity changes out of phase.

6. The method according to claim 1, wherein the step of obtaining the spectral data includes analyzing at least one side chain mode of at least one of the protein, the peptide, and the peptoid as an internal probe.

7. The method according to claim 1, further comprising performing two-dimensional co-distribution analysis on the spectral data.

8. The method according to claim 1, further comprising applying two-dimensional correlation analysis to create a synchronous correlation plot of at least one of the protein, the peptide, and the peptoid.

9. The method according to claim 8, further comprising determining an order of molecular events from the asynchronous correlation plot and the synchronous correlation plot.

10. The method according to claim 9, further comprising determining a degree of deamidation based on the order of the molecular events.

11. The method according to claim 8, further comprising determining a stability of a domain in at least one of the protein, the peptide, and the peptoid.

12. In a system for processing data representing at least one characteristic of a protein, a peptide, and a peptoid, a data acquisition module for obtaining spectral data of at least one of the protein, the peptide, and the peptoid without using a probe or an additive for an applied perturbation, using a quantum cascade laser microscope; a correlation analysis module, applying two-dimensional correlation analysis to create an asynchronous correlation plot of at least one of the protein, the peptide, and the peptoid; identifying at least one peak associated with deamidation of at least one of the protein, the peptide, and the peptoid in the asynchronous correlation plot; A system having

13. The correlation analysis module uses the at least one peak to determine the order of the distributed presence of spectral intensity changes for the applied perturbation, the system according to claim 12.

14. Using the at least one peak Two wave numbers ν １ and ν ２ Regarding, determining whether the at least one peak corresponding to the two wave numbers has a positive value including, the system according to claim 13.

15. Using the at least one peak Two wave numbers ν １ and ν ２ Regarding, determining whether the at least one peak corresponding to the two wave numbers has a negative value including, the system according to claim 13.

16. Obtaining the spectral data includes analyzing at least one side chain mode of at least one of the protein, the peptide, and the peptoid as an internal probe, the system according to claim 12.

17. The correlation analysis module applies two-dimensional correlation analysis to create a synchronous correlation plot for at least one of the protein, the peptide, and the peptoid, the system according to claim 12.

18. The correlation analysis module determines the order of molecular events from the asynchronous correlation plot and the synchronous correlation plot; and determines the degree of deamidation based on the order of the molecular events is further configured to, the system according to claim 17.

19. When executed by one or more computers, causing the one or more computers to obtain spectral data of at least one of a protein, a peptide, and a peptoid without using a probe or an additive for an applied perturbation, using a quantum cascade laser microscope; Applying two-dimensional correlation analysis to create at least one asynchronous correlation plot of at least one of the protein, peptide, and peptoid; A non-transitory computer-readable medium that executes an instruction to perform a step of identifying at least one peak associated with deamidation of at least one of the protein, peptide, and peptoid in the asynchronous correlation plot.

Images