Engaging with Raman Spectroscopy to Investigate Antibody Aggregation

Use of Raman spectroscopy and PCA for quality evaluation and out-of-specification identification in biopharmaceutical products

Over the past three decades, there was a remarkable growth in the approval of antibody-based biopharmaceutical products. These molecules are notably susceptible to the stresses occurring during drug manufacturing, often leading to structural alterations. A key concern is thus the ability to detect and comprehend these alterations caused by processes, such as aggregation, fragmentation, oxidation levels, as well as the change in protein concentration throughout the process steps, potentially resulting in out-of-spec products. In the present study, Raman spectroscopy, coupled with Principal Component Analysis (PCA), has proven to be an excellent tool for characterizing protein-based products. Notably, it offers the advantages of being minimally invasive, rapid and relatively insensitive to water. Therefore, it was successfully employed to discriminate between various stresses impacting a monoclonal antibody (mAb). The molecule used in this study is a fully human IgG1 fusion protein. Thermal stress was induced by incubating the samples at 50 °C for one month, while oxidative stress was induced by introducing hydrogen peroxide. Additionally, dilutions were performed to explore a broader range of protein concentrations. Specific key bands were identified in the Raman spectra, which facilitated the PCA classification and allowed for their association with distinct changes in the secondary and tertiary structures of the protein. Notably, it was observed that signals corresponding to amino acids exhibited a decrease in intensity with increasing levels of thermal stress, while other alterations were noted in the amide bands. It was shown that changes in the range 2800-3000 cm-1 pertains to the dilution process, while specific peaks of C-H stretching were essential for the discrimination between the oxidative-stressed samples and the thermal and diluted counterparts. Furthermore, the model calibrated on the mAb demonstrated remarkable performance when used to evaluate a different product, e.g. a hormone.

Development of Analytical Method for the Quantitation of Monoclonal Antibodies Solutions via Raman Spectroscopy: The Case of Bevacizumab

Personalized dosages of monoclonal antibodies are being used more regularly to treat various diseases, rendering their quantitation more essential than ever for the right dose administration to the patients. A promising alternative, which overcomes the obstacles of the well-established chromatographic techniques regarding the quantification of biopharmaceuticals, is Raman spectroscopy. This study aimed to develop and validate a novel analytical method for the quantitation of bevacizumab in solutions via Raman spectroscopy. For this purpose, a droplet of the solution was left to dry on a highly reflective carrier and a home-made apparatus was employed for rotation of the sample. Hence, each recorded Raman spectrum was the average of the signal acquired simultaneously from multiple points on a circular circumference. The method was validated, and the detection limit of the antibody was found to be 1.06 mg/mL. Bevacizumab was found to be highly distributed at the formed coffee ring of the dried droplet, though this was a function of solution concentration. Finally, Raman spectra at different distances on the coffee ring were obtained from the four quarters. The lowest bevacizumab detection limit was found at a distance of 75 μm from the external side of the coffee ring and it was determined to be equal to 0.53 mg/mL.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

BACKGROUND

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants.

OBJECTIVE

We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques.

METHOD

Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species.

RESULT

A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration.

CONCLUSION

We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation

Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Validation of the cell culture monitoring using a Raman spectroscopy calibration model developed with artificially mixed samples and investigation of model learning methods using initial batch data

The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.

Online Coupling of Size Exclusion Chromatography to Capillary Enhanced Raman Spectroscopy for the Analysis of Proteins and Biopharmaceutical Drug Products

The online coupling of size exclusion chromatography (SEC) to capillary enhanced Raman spectroscopy (CERS) based on a liquid core waveguide (LCW) flow cell was applied for the first time to assess the higher-order structure of different proteins. This setup allows recording of Raman spectra of the monomeric protein within complex mixtures, since SEC enables the separation of the monomeric protein from matrix components such as excipients of a biopharmaceutical product and higher molecular weight species (e.g., aggregates). The acquired Raman spectra were used for structural elucidation of well characterized proteins such as bovine serum albumin, hen egg white lysozyme, and β-lactoglobulin and of the monoclonal antibody rituximab in a medicinal product. Additionally, the CERS detection of the disaccharide sucrose, which is used as a stabilizing excipient, was quantified to achieve a limit of detection (LOD) of 120 μg and a limit of quantification (LOQ) of 363 μg injected on the column.

Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality

Subvisible particles (SVPs) are a critical quality attribute of injectable therapeutic proteins (TPs) that needs to be controlled due to potential risks associated with drug product quality. The current compendial methods routinely used to analyze SVPs for lot release provide information on particle size and count. However, chemical identification of individual particles is also important to address root-cause analysis. Herein, we introduce Morphologically-Directed Raman Spectroscopy (MDRS) for SVP characterization of TPs. The following particles were used for method development: (1) polystyrene microspheres, a traditional standard used in industry; (2) photolithographic (SU-8); and (3) ethylene tetrafluoroethylene (ETFE) particles, candidate reference materials developed by NIST. In our study, MDRS rendered high-resolution images for the ETFE particles (> 90%) ranging from 19 to 100 μm in size, covering most of SVP range, and generated comparable morphology data to flow imaging microscopy. Our method was applied to characterize particles formed in stressed TPs and was able to chemically identify individual particles using Raman spectroscopy. MDRS was able to compare morphology and transparency properties of proteinaceous particles with reference materials. The data suggests MDRS may complement the current TPs SVP analysis system and product quality characterization workflow throughout development and commercial lifecycle.

Expanding the application of graphene vertical devices to dual femtomolar detection of SARS-CoV-2 receptor binding domain in serum and saliva

The emergence of the graphene-based hybrid electrical-electrochemical vertical device (EEVD) has introduced a promising nanostructured biosensor tailored for point-of-care applications. In this study, we present an innovative EEVD capable of simultaneously detecting the receptor binding domain (RBD) of the SARS-CoV-2 spike protein in both serum and saliva. The foundation of the EEVD lies in a poly-neutral red-graphene heterojunction, which has been enhanced with a bioconjugate of gold nanoparticles and antibodies. The biodevice demonstrates a remarkable limit of detection, registering at the femtomolar scale (2.86 fmol L-1 or 0.1 pg mL-1). Its sensitivity is characterized by a 6.1 mV/decade response, and its operational range spans 10-12 to 10-7 g mL-1 in both serum and saliva samples. With a 20.0 μL of biological samples and a rapid processing time of under 10 min, the EEVD achieves the feat of dual antigen detection. The tests achieved 100.0% specificity, accuracy, and sensitivity in saliva, and 100.0% specificity, 88.9% accuracy, and 80.0% sensitivity in serum. This study highlights the EEVD as a low-cost solution of rapid viral detection during the crucial initial phases of COVID-19 infections.

Application of surface-enhanced Raman scattering to qualitative and quantitative analysis of arsenic species

Given the toxicity of arsenic, there is an urgent need for the development of efficient and reliable detection systems. Raman spectroscopy, a powerful tool for material characterization and analysis, can be used to explore the properties of a wide range of different materials. Surface-enhanced Raman spectroscopy (SERS) can detect low concentrations of chemicals. This review focuses on the progress of qualitative and quantitative studies of the adsorption processes of inorganic arsenic and organic arsenic in aqueous media using Raman spectroscopy in recent years and discusses the application of Raman spectroscopy theory simulations to arsenic adsorption processes. Sliver nanoparticles are generally used as the SERS substrate to detect arsenic. Inorganic arsenic is chemisorbed onto the silver surface by forming As-O-Ag bonds, and the Raman shift difference in the As-O stretching (∼60 cm-1) between As(V) and As(III) allows SERS to detect and distinguish between As(V) and As(III) in groundwater samples. For organic arsenicals, specific compounds can be identified based on spectral differences in the vibration modes of the chemical bonds. Under the same laser excitation, the intensity of the Raman spectra for different arsenic concentrations is linearly related to the concentration, thus allowing quantitative analysis of arsenic. Molecular modeling of adsorbed analytes via density functional theory calculation (DFT) can predict the Raman shifts of analytes in different laser wavelengths.

SARS-CoV-2 Virus-like Particles with Plasmonic Au Cores and S1-Spike Protein Coronas

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies aimed at the development of quick and safe ways of detecting viruses in the human body, studying the virus-antibody and virus-cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. Such species, called virus-like particles (VLPs), contain instead of capsids with viral DNA/RNA empty or synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by "coronas" of S1 domains of the virus's spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. During the studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.

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.

SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics

In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(-) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value-RMSEC and RMSECV below 0.3 and R²_cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

Identification of monoclonal antibody drug substances using non-destructive Raman spectroscopy

Monoclonal antibodies provide highly specific and effective therapies for the treatment of chronic diseases. These protein-based therapeutics, or drug substances, are transported in single used plastic packaging to fill finish sites. According to good manufacturing practice guidelines, each drug substance needs to be identified before manufacturing of the drug product. However, considering their complex structure, it is challenging to correctly identify therapeutic proteins in an efficient manner. Common analytical techniques for therapeutic protein identification are SDS-gel electrophoresis, enzyme linked immunosorbent assays, high performance liquid chromatography and mass spectrometry-based assays. Although effective in correctly identifying the protein therapeutic, most of these techniques need extensive sample preparation and removal of samples from their containers. This step not only risks contamination but the sample taken for the identification is destroyed and cannot be re-used. Moreover, these techniques are often time consuming, sometimes taking several days to process. Here, we address these challenges by developing a rapid and non-destructive identification technique for monoclonal antibody-based drug substances. Raman spectroscopy in combination with chemometrics were used to identify three monoclonal antibody drug substances. This study explored the impact of laser exposure, time out of refrigerator and multiple freeze thaw cycles on the stability of monoclonal antibodies. and demonstrated the potential of using Raman spectroscopy for the identification of protein-based drug substances in the biopharmaceutical industry.

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

Insights on Aggregation of Hen Egg-White Lysozyme from Raman Spectroscopy and MD Simulations

Protein misfolding and aggregation play a significant role in several neurodegenerative diseases. In the present work, the spontaneous aggregation of hen egg-white lysozyme (HEWL) in an alkaline pH 12.2 at an ambient temperature was studied to obtain molecular insights. The time-dependent changes in spectral peaks indicated the formation of β sheets and their effects on the backbone and amino acids during the aggregation process. Introducing iodoacetamide revealed the crucial role of intermolecular disulphide bonds amidst monomers in the aggregation process. These findings were corroborated by Molecular Dynamics (MD) simulations and protein-docking studies. MD simulations helped establish and visualize the unfolding of the proteins when exposed to an alkaline pH. Protein docking revealed a preferential dimer formation between the HEWL monomers at pH 12.2 compared with the neutral pH. The combination of Raman spectroscopy and MD simulations is a powerful tool to study protein aggregation mechanisms.

Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies

Therapeutic monoclonal antibodies (mAbs) are a new generation of protein-based medicines that are usually expensive and thus represent a target for counterfeiters. In the present study, a method based on Raman spectroscopy that combined extreme point sort transformation with a long short-term memory (LSTM) network algorithm was presented for the identification of therapeutic mAbs. A total of 15 therapeutic mAbs were used in this study. An in-house Raman spectrum dataset for model training was created with 1,350 spectra. The characteristic region of the Raman spectrum was reduced in dimension and then transformed through an extreme point sort transformation into a sequence array, which was fitted for the LSTM network. The characteristic array was extracted from the sequence array using a well-trained LSTM network and then compared with standard spectra for identification. To demonstrate whether the present algorithm was better, ThermoFisher OMNIC 8.3 software (Thermo Fisher Scientific Inc., U.S.) with two matching modes was selected for comparison. Finally, the present method was successfully applied to identify 30 samples, including 15 therapeutic mAbs and 15 other injections. The characteristic region was selected from 100 to 1800 cm⁻¹ of the full spectrum. The optimized dimensional values were set from 35 to 53, and the threshold value range was from 0.97 to 0.99 for 15 therapeutic mAbs. The results of the robustness test indicated that the present method had good robustness against spectral peak drift, random noise and fluorescence interference from the measurement. The areas under the curve (AUC) values of the present method that were analysed on the full spectrum and analysed on the characteristic region by the OMNIC 8.3 software’s built-in method were 1.000, 0.678, and 0.613, respectively. The similarity scores for 15 therapeutic mAbs using OMNIC 8.3 software in all groups compared with that of the relative present algorithm group had extremely remarkable differences (p < 0.001). The results suggested that the extreme point sort transformation combined with the LSTM network algorithm enabled the characteristic extraction of the therapeutic mAb Raman spectrum. The present method is a proposed solution to rapidly identify therapeutic mAbs.

Synthesis of gold nanoparticles@reduced porous graphene-modified ITO electrode for spectroelectrochemical detection of SARS-CoV-2 spike protein

Here, we reported the synthesis of reduced porous graphene oxide (rPGO) decorated with gold nanoparticles (Au NPs) to modify the ITO electrode. Then we used this highly uniform Au NPs@rPGO modified ITO electrode as a surface-enhanced Raman spectroscopy-active surface and a working electrode. The uses of the Au nanoparticles and porous graphene enhance the Raman signals and the electrochemical conductivity. COVID-19 protein-based biosensor was developed based on immobilization of anti-COVID-19 antibodies onto the modified electrode and its uses as a probe for capturing the COVID-19 protein. The developed biosensor showed the capability of monitoring the COVID-19 protein within a concentration range from 100 nmol/L to 1 pmol/L with a limit of detection (LOD) of 75 fmol/L. Furthermore, COVID-19 protein was detected based on electrochemical techniques within a concentration range from 100 nmol/L to 500 fmol/L that showed a LOD of 39.5 fmol/L. Finally, three concentrations of COVID-19 protein spiked in human serum were investigated. Thus, the present sensor showed high efficiency towards the detection of COVID-19.

Diagnosing COVID-19 in human sera with detected immunoglobulins IgM and IgG by means of Raman spectroscopy

The severe COVID-19 pandemic requires the development of novel, rapid, accurate, and label-free techniques that facilitate the detection and discrimination of SARS-CoV-2 infected subjects. Raman spectroscopy has been used to diagnose COVID-19 in serum samples of suspected patients without clinical symptoms of COVID-19 but presented positive immunoglobulins M and G (IgM and IgG) assays versus Control (negative IgM and IgG). A dispersive Raman spectrometer (830 nm, 350 mW) was employed, and triplicate spectra were obtained. A total of 278 spectra were used from 94 serum samples (54 Control and 40 COVID-19). The main spectral differences between the positive IgM and IgG versus Control, evaluated by principal component analysis (PCA), were features assigned to proteins including albumin (lower in the group COVID-19 and in the group IgM/IgG and IgG positive) and features assigned to lipids, phospholipids, and carotenoids (higher the group COVID-19 and in the group IgM/IgG positive). Features referred to nucleic acids, tryptophan, and immunoglobulins were also seen (higher the group COVID-19). A discriminant model based on partial least squares regression (PLS-DA) found sensitivity of 84.0%, specificity of 95.0%, and accuracy of 90.3% for discriminating positive Ig groups versus Control. When considering individual Ig group versus Control, it was found sensitivity of 77.3%, specificity of 97.5%, and accuracy of 88.8%. The higher classification error was found for the IgM group (no success classification). Raman spectroscopy may become a technique of choice for rapid serological evaluation aiming COVID-19 diagnosis, mainly detecting the presence of IgM/IgG and IgG after COVID-19 infection.

Enhancing Antibodies' Binding Capacity through Oriented Functionalization of Plasmonic Surfaces

Protein A has long been used in different research fields due to its ability to specifically recognize immunoglobulins (Ig). The protein derived from Staphylococcus aureus binds Ig through the Fc region of the antibody, showing its strongest binding in immunoglobulin G (IgG), making it the most used protein in its purification and detection. The research presented here integrates, for the first time, protein A to a silicon surface patterned with gold nanoparticles for the oriented binding of IgG. The signal detection is conveyed through a metal enhanced fluorescence (MEF) system. Orienting immunoglobulins allows the exposition of the fragment antigen-binding (Fab) region for the binding to its antigen, substantially increasing the binding capacity per antibody immobilized. Antibodies orientation is of crucial importance in many diagnostics devices, particularly when either component is in limited quantities.

Electrochemical Microbiosensor for Detecting COVID-19 in a Patient Sample Based on Gold Microcuboids Pattern

As continues increasing the COVID-19 infections, there is an urgent need for developing fast, simple, selective, and accurate COVID-19 biosensors. A highly uniform gold (Au) microcuboid pattern was used as a microelectrode that allowed monitoring a small analyte. The electrochemical biosensor was used to monitor the COVID-19 S protein within a concentration range from 100 to 5 pmol L⁻¹; it showed a lower detection limit of 276 fmol L⁻¹. Finally, the developed COVID-19 sensor was used to detect a positive sample from a human patient obtained through a nasal swab; the results were confirmed using the PCR technique. The results showed that the SWV technique showed high sensitivity towards detecting COVID-19 and good efficiency for detecting COVID-19 in a positive human sample.

Transcutaneous penetration of a single-chain variable fragment (scFv) compared to a full-size antibody: potential tool for atopic dermatitis (AD) treatment

Currently, several biologics are used for the treatment of cutaneous pathologies such as atopic dermatitis (AD), psoriasis or skin cancers. The main administration routes are subcutaneous and intravenous injections. However, little is known about antibody penetration through the skin. The aim was to study the transcutaneous penetration of a reduced-size antibody as a single-chain variable fragment (scFv) compared to a whole antibody (Ab) and to determine its capacity to neutralize an inflammatory cytokine involved in AD such as human interleukin-4 (hIL-4). Transcutaneous penetration was evaluated by ex vivo studies on tape-stripped pig ear skin. ScFv and Ab visualization through the skin was measured by Raman microspectroscopy. In addition, hIL-4 neutralization was studied in vitro using HEK-Blue™ IL-4/IL-13 cells and normal human keratinocytes (NHKs). After 24 h of application, analysis by Raman microspectroscopy showed that scFv penetrated into the upper dermis while Ab remained on the stratum corneum. In addition, the anti-hIL4 scFv showed very efficient and dose-dependent hIL-4 neutralization. Thus, scFv penetrates through to the upper papillary dermis while Ab mostly remains on the surface, the anti-hIL4 scFv also neutralizes its target effectively suggesting its potential use as topical therapy for AD.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Understanding the discrimination and quantification of monoclonal antibodies preparations using Raman spectroscopy

The use of Raman spectroscopy for analytical quality control of anticancer drug preparations in clinical pharmaceutical dispensing units is increasing in popularity, notably supported by commercially available, purpose designed instruments. Although not legislatively compulsory, analytical methods are frequently used post-preparation to verify the accuracy of a preparation in terms of identity and quantity of the drug in solution. However, while the rapid, cost effective and label free analysis achieved with Raman spectroscopy is appealing, it is important to understand the molecular origin of the spectral contributions collected from the solution of actives and excipients, to evaluate the strength and limitation for the technique, which can be used to identify and quantify either the prescribed commercial formulation, and/or the active drug itself, in personalised solutions. In the current study, four commercial formulations, Erbitux®, Truxima®, Ontruzant® and Avastin® of monoclonal antibodies (mAbs), corresponding respectively to cetuximab, rituximab, trastuzumab and bevacizumab have been used to highlight the key role of excipients in discrimination and quantification of the formulations. It is demonstrated that protein based anticancer drugs such as mAbs have a relatively weak Raman response, while excipients such as glycine, trehalose or histidine contribute significantly to the spectra. Multivariate analysis (partial least square regression and partial least square discriminant analysis) further demonstrates that the signatures of the mAbs themselves are not prominent in mathematical models and that those of the excipients are solely responsible for the differentiation of formulation and accurate determination of concentrations. While Raman spectroscopy can successfully validate the conformity of mAbs intravenous infusion solutions, the basis for the analysis should be considered, and special caution should be given to excipient compositions in commercial formulations to ensure reliability and reproducibility of the analysis.

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.

Raman Spectroscopy to Monitor Post-Translational Modifications and Degradation in Monoclonal Antibody Therapeutics

Monoclonal antibodies (mAbs) represent a rapidly expanding market for biotherapeutics. Structural changes in the mAb can lead to unwanted immunogenicity, reduced efficacy, and loss of material during production. The pharmaceutical sector requires new protein characterization tools that are fast, applicable in situ and to the manufacturing process. Raman has been highlighted as a technique to suit this application as it is information-rich, minimally invasive, insensitive to water background and requires little to no sample preparation. This study investigates the applicability of Raman to detect Post-Translational Modifications (PTMs) and degradation seen in mAbs. IgG4 molecules have been incubated under a range of conditions known to result in degradation of the therapeutic including varied pH, temperature, agitation, photo, and chemical stresses. Aggregation was measured using size-exclusion chromatography, and PTM levels were calculated using peptide mapping. By combining principal component analysis (PCA) with Raman spectroscopy and circular dichroism (CD) spectroscopy structural analysis we were able to separate proteins based on PTMs and degradation. Furthermore, by identifying key bands that lead to the PCA separation we could correlate spectral peaks to specific PTMs. In particular, we have identified a peak which exhibits a shift in samples with higher levels of Trp oxidation. Through separation of IgG4 aggregates, by size, we have shown a linear correlation between peak wavenumbers of specific functional groups and the amount of aggregate present. We therefore demonstrate the capability for Raman spectroscopy to be used as an analytical tool to measure degradation and PTMs in-line with therapeutic production.