Monitoring antibody aggregation in early drug development using Raman spectroscopy and perturbation-correlation moving windows

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Preparation and biological evaluation of antibody targeted metal-organic framework drug delivery system (TDDS) in Her2 receptor-positive cells

In this study, we designed and prepared a trastuzumab-coupled drug delivery system with pH response characteristics using mesoporous zeolitic imidazolate framework-8 (ZIF-8) as the carrier, Trastuzumab@ZIF-8@DOX. As results, the targeted drug delivery system (TDDS) ultimately showed high drug loading and good biocompatibility. The cumulative curve of drug release indicated that the early leakage levels were low under neutral pH conditions. However, under acidic pH conditions, there was an effective enhancement in drug release, indicating the presence of an explicit pH-triggered drug release mechanism. The results indicate that the prepared nanoparticles have the potential to serve as drug delivery systems, as they can release the loaded drug in a controlled manner. The results of cellular uptake tests showed that the uptake of the nanoparticles was greatly enhanced by the internalization mediated by the HER2 antibody. This finding indicates that the prepared nanoparticles can selectively target cancer cells that overexpress HER2. When the doxorubicin dose was 5 μg/ml, the survival rate of SK-BR-3 cells (cancer cells) was 47.75 %, and the survival rate of HaCaT cells (healthy cells) was 75.25 % when co-cultured with both cells. The therapeutic efficacy of Trastuzumab@ZIF-8@DOX was assessed on BALB/c nude mice to validate its potential as an effective drug delivery system for tumor inhibition in vivo. In conclusion, these findings demonstrate the specificity-targeted and pH-responsive nature of this smart drug delivery system, highlighting its promising prospects for efficient and controllable cancer treatment applications.

Deciphering DOM-metal binding using EEM-PARAFAC: Mechanisms, challenges, and perspectives

Dissolved organic matter (DOM) is a pivotal component of the biogeochemical cycles and can combine with metal ions through chelation or complexation. Understanding this process is crucial for tracing metal solubility, mobility, and bioavailability. Fluorescence excitation emission matrix (EEM) and parallel factor analysis (PARAFAC) has emerged as a popular tool in deciphering DOM-metal interactions. In this review, we primarily discuss the advantages of EEM-PARAFAC compared with other algorithms and its main limitations in studying DOM-metal binding, including restrictions in spectral considerations, mathematical assumptions, and experimental procedures, as well as how to overcome these constraints and shortcomings. We summarize the principles of EEM to uncover DOM-metal association, including why fluorescence gets quenched and some potential mechanisms that affect the accuracy of fluorescence quenching. Lastly, we review some significant and innovative research, including the application of 2D-COS in DOM-metal binding analysis, hoping to provide a fresh perspective for possible future hotspots of study. We argue the expansion of EEM applications to a broader range of areas related to natural organic matter. This extension would facilitate our exploration of the mobility and fate of metals in the environment.

Advanced Techniques for Detecting Protein Misfolding and Aggregation in Cellular Environments

Protein misfolding and aggregation, a key contributor to the progression of numerous neurodegenerative diseases, results in functional deficiencies and the creation of harmful intermediates. Detailed visualization of this misfolding process is of paramount importance for improving our understanding of disease mechanisms and for the development of potential therapeutic strategies. While in vitro studies using purified proteins have been instrumental in delivering significant insights into protein misfolding, the behavior of these proteins in the complex milieu of living cells often diverges significantly from such simplified environments. Biomedical imaging performed in cell provides cellular-level information with high physiological and pathological relevance, often surpassing the depth of information attainable through in vitro methods. This review highlights a variety of methodologies used to scrutinize protein misfolding within biological systems. This includes optical-based methods, strategies leaning on mass spectrometry, in-cell nuclear magnetic resonance, and cryo-electron microscopy. Recent advancements in these techniques have notably deepened our understanding of protein misfolding processes and the features of the resulting misfolded species within living cells. The progression in these fields promises to catalyze further breakthroughs in our comprehension of neurodegenerative disease mechanisms and potential therapeutic interventions.

Discovery and Characterization of an Acid-Labile Serine-Lysine Cross-Link in Antibody High-Molecular-Weight Species Using a Multipronged Mass Spectrometry Approach

Characterizing the cross-links responsible for the covalent high-molecular-weight (HMW) species in therapeutic monoclonal antibodies (mAbs) is of great importance as it not only provides a framework for risk assessment but also offers insights for process improvement. However, owing to the complexity and low abundance, identification of novel and unknown cross-links in mAb products can be very challenging. Here, applying a multipronged MS-based approach, we report the discovery of a novel covalent cross-link formed via an imine bond between lysine and serine residues. In particular, this Ser-Lys cross-link was found to be acid-labile and can be easily overlooked by conventional LC-MS techniques operated at low pH. It is worth noting that although imine-based cross-link has been previously reported in collagen protein cross-linking, this is the first time that a Ser-Lys cross-link has been found in a mAb product that contributes to covalent HMW species formation.

Raman Spectroscopic Analysis of Highly-Concentrated Antibodies under the Acid-Treated Conditions

PURPOSE

Antibody drugs are usually formulated as highly-concentrated solutions, which would easily generate aggregates, resulting in loss of efficacy. Although low pH increases the colloidal dispersion of antibodies, acid denaturation can be an issue. Therefore, knowing the physical properties at low pH under high concentration conditions is important.

METHODS

Raman spectroscopy was used to investigate pH-induced conformational changes of antibodies at 50 mg/ml. Experiments in pH 3 to 7 were performed for human serum IgG and recombinant rituximab.

RESULTS

We detected the evident changes at pH 3 in Tyr and Trp bands, which are the sensitive markers of intermolecular interactions. Thermal transition analysis over the pH range demonstrated that the thermal transition temperature (Tm) was highest at pH 3. Acid-treated and neutralized one showed higher Tm than that of pH 7, indicating that their extent of intermolecular interactions correlated with the Tm values. Onset temperature was clearly different between concentrated and diluted samples. Colloidal analyses confirmed the findings of the Raman analysis.

CONCLUSION

Our studies demonstrated the positive correlation between Raman analysis and colloidal information, validating as a method for evaluating antibody conformation associated with aggregation propensities.

Visible Particle Identification Using Raman Spectroscopy and Machine Learning

Visible particle identification is a crucial prerequisite step for process improvement and control during the manufacturing of injectable biotherapeutic drug products. Raman spectroscopy is a technology with several advantages for particle identification including high chemical sensitivity, minimal sample manipulation, and applicability to aqueous solutions. However, considerable effort and experience are required to extract and interpret Raman spectral data. In this study, we applied machine learning algorithms to analyze Raman spectral data for visible particle identification in order to minimize expert support and improve data analysis accuracy. We manually prepared ten types of particle standard solutions to simulate the particle types typically observed during manufacturing and established a Raman spectral library with accurate peak assignments for the visible particles. Five classification algorithms were trained using visible particle Raman spectral data. All models had high prediction accuracy of >98% for all types of visible particles. Our results demonstrate that the combination of Raman spectroscopy and machine learning can provide a simple and accurate data analysis approach for visible particle identification.

Multi-attribute Raman spectroscopy (MARS) for monitoring product quality attributes in formulated monoclonal antibody therapeutics

Rapid release of biopharmaceutical products enables a more efficient drug manufacturing process. Multi-attribute methods that target several product quality attributes (PQAs) at one time are an essential pillar of the rapid-release strategy. The novel, high-throughput, and nondestructive multi-attribute Raman spectroscopy (MARS) method combines Raman spectroscopy, design of experiments, and multivariate data analysis (MVDA). MARS allows the measurement of multiple PQAs for formulated protein therapeutics without sample preparation from a single spectroscopic scan. Variable importance in projection analysis is used to associate the chemical and spectral basis of targeted PQAs, which assists in model interpretation and selection. This study shows the feasibility of MARS for the measurement of both protein purity-related and formulation-related PQAs; measurements of protein concentration, osmolality, and some formulation additives were achieved by a generic multiproduct model for various protein products containing the same formulation components. MARS demonstrates the potential to be a powerful methodology to improve the efficiency of biopharmaceutical development and manufacturing, as it features fast turnaround time, good robustness, less human intervention, and potential for automation.

High volatility of superbase-derived eutectic solvents used for CO2 capture

High volatility would lead to a highly flammable hazard, explosion danger, low regeneration efficiency and air pollution. Eutectic solvents (ESs) are assumed to be nonvolatile; however, the assumption is not correct. Here, we, for the first time, find that superbase-derived ESs are highly volatile. Even at room temperature (i.e., 25 °C) and atmospheric pressure, the mass loss of ESs could reach as high as 43.5% after 20 h of exposure. Superbase-derived ESs are promising solvents for CO2 capture, and they are also highly volatile after CO2 capture. We found that typical ethylene glycol : 1,8-diazabicyclo[5.4.0]undec-7-ene (EG : DBU (4 : 1)) has a three-stage volatilizing mechanism. EG and DBU volatilize first by breaking weak hydrogen-bonding interactions (1st stage), followed by the destruction of strong hydrogen-bonding interactions (2nd stage), and finally by destroying much stronger hydrogen-bonding interactions (3rd stage). This work presents a new horizon that ESs and their mixture with CO2 are highly volatile, which is helpful for mitigating laboratory explosion, combustion hazards, air pollution and designing new types of ESs with negligible volatility.

Raman Spectroscopy with 2D Perturbation Correlation Moving Windows for the Characterization of Heparin-Amyloid Interactions

It has been shown extensively that glycosaminoglycan (GAG)-protein interactions can induce, accelerate, and impede the clearance of amyloid fibrils associated with systemic and localized amyloidosis. Obtaining molecular details of these interactions is fundamental to our understanding of amyloid disease. Consequently, there is a need for analytical approaches that can identify protein conformational transitions and simultaneously characterize heparin interactions. By combining Raman spectroscopy with two-dimensional (2D) perturbation correlation moving window (2DPCMW) analysis, we have successfully identified changes in protein secondary structure during pH- and heparin-induced fibril formation of apolipoprotein A-I (apoA-I) associated with atherosclerosis. Furthermore, from the 2DPCMW, we have identified peak shifts and intensity variations in Raman peaks arising from different heparan sulfate moieties, indicating that protein-heparin interactions vary at different heparin concentrations. Raman spectroscopy thus reveals new mechanistic insights into the role of GAGs during amyloid fibril formation.

High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development

Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations.

Rapid, quantitative determination of aggregation and particle formation for antibody drug conjugate therapeutics with label-free Raman spectroscopy

Lot release and stability testing of biologics are essential parts of the quality control strategy for ensuring therapeutic material dosed to patients is safe and efficacious, and consistent with previous clinical and toxicological experience. Characterization of protein aggregation is of particular significance, as aggregates may lose the intrinsic pharmaceutical properties as well as engage with the immune system instigating undesirable downstream immunogenicity. While important, real-time identification and quantification of subvisible particles in the monoclonal antibody (mAb) drug products remains inaccessible with existing techniques due to limitations in measurement time, sensitivity or experimental conditions. Here, owing to its exquisite molecular specificity, non-perturbative nature and lack of sample preparation requirements, we propose label-free Raman spectroscopy in conjunction with multivariate analysis as a solution to this unmet need. By leveraging subtle, but consistent, differences in vibrational modes of the biologics, we have developed a support vector machine-based regression model that provides fast, accurate prediction for a wide range of protein aggregations. Moreover, in blinded experiments, the model shows the ability to precisely differentiate between aggregation levels in mAb like product samples pre- and post-isothermal incubation, where an increase in aggregate levels was experimentally determined. In addition to offering fresh insights into mAb like product-specific aggregation mechanisms that can improve engineering of new protein therapeutics, our results highlight the potential of Raman spectroscopy as an in-line analytical tool for monitoring protein particle formation.

Characterizing Properties and Environmental Behaviors of Dissolved Organic Matter Using Two-Dimensional Correlation Spectroscopic Analysis

Dissolved organic matter (DOM) exists ubiquitously in environments and plays critical roles in pollutant mitigation, transformation, and organic geochemical cycling. Understanding its properties and environmental behaviors is critically important to develop water treatment processes and environmental remediation strategies. Generalized two-dimensional correlation spectroscopy (2DCOS), which has numerous advantages, including enhancing spectral resolution and discerning specific order of structural change under an external perturbation, could be used as a powerful tool to interpret a wide range of spectroscopic signatures relating to DOM. A suite of spectroscopic signatures, such as UV-vis, fluorescence, infrared, and Raman spectra that can be analyzed by 2DCOS, is able to provide additional structural information hiding behind the conventional one-dimensional spectra. In this article, the most recent advances in 2DCOS applications for analyzing DOM-related environmental processes are reviewed, and the state-of-the-art novel spectroscopic techniques in 2DCOS are highlighted. Furthermore, the main limitations and requirements of current approaches for exploring DOM-related environmental processes and how these limitations and drawbacks can be addressed are explored. Finally, suggestions and new approaches are proposed to significantly advance the development of 2DCOS in analyzing the properties and behaviors of DOM in natural and engineered environments.

Engaging with Raman Spectroscopy to Investigate Antibody Aggregation

In the last decade, a number of studies have successfully demonstrated Raman spectroscopy as an emerging analytical technique for monitoring antibody aggregation, especially in the context of drug development and formulation. Raman spectroscopy is a robust method for investigating protein conformational changes, even in highly concentrated antibody solutions. It is non-destructive, reproducible and can probe samples in an aqueous environment. In this review, we focus on the application and challenges associated with using Raman spectroscopy as a tool to study antibody aggregates.

Least Squares Moving-Window Spectral Analysis

Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.

Analytical and Numerical Characterization of Autocorrelation and Perturbation-Correlation Moving-Window Methods

Moving-window (MW) approaches to two-dimensional correlation spectroscopy (2D-COS) make it possible to characterize spectral changes occurring in a narrow range of perturbation variable (e.g., time, temperature, and concentration). Despite the wide range of application, the physical meanings of MW correlation intensities have been only qualitatively associated with the direction and curvature of spectral intensity change with regard to a perturbation variable. Here are full and simplified analytical expressions of autocorrelation moving-window (ACMW) and synchronous and asynchronous perturbation-correlation moving-window ( s-PCMW and as-PCMW) intensities. When the window is set sufficiently narrower than the bandwidth of spectral change, the square root of ACMW intensity and s-PCMW intensity becomes proportional to the first order derivative, and as-PCMW intensity becomes proportional to the negative of the second order derivative. This paper demonstrates that both ACMW and PCMW profiles can be significantly altered by non-uniform perturbation spacing. It is also found that intensity noise can cause ACMW to display a false offset drift. This analytical and numerical characterization of the two MW correlation intensities elucidates their physical meanings and ascertains the analysis conditions for reliable interpretation.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>€35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.