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

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.

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.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R²_CV.

Quantification of clinical mAb solutions using Raman spectroscopy: Macroscopic vs microscopic analysis

Raman Spectroscopy is well emerged in the field of Analytical Quality Control (AQC) as a rapid and cost-effective technique useful in many applications. The advantage of Raman spectroscopy is the non-invasiveness of measurements that enablesto analyse samples directly in its container. In this study, the potential of Raman spectroscopy was investigated for analysis of clinical preparations of mAbs. Three commercial formulations of monoclonal antibodies (mAbs) Avastin®, Ontruzant® and Tecentriq® corresponding to Bevacizumab (BVC), Trastuzumab (TRS) and Atezolizumab (ATZ) respectively, were analysed in quartz cuvette in macroscopic analysis and through the wall of perfusion bags in microscopic analysis. The spectra have been compared to those of excipients (trehalose and sucrose) and of γ-Globulin, in order to investigate the origin of Raman bands. As expected, Raman spectra were a combination of bands from monoclonal antibodies and correspoding excipients found in formulas. For quantitative analysis of the solutions, models have been constructed using Partial Least Square Regression (PLSR) with Leave K-Out Cross Validation (LKOCV). The quantification performance was comparable for both macroscopic and microscopic analysis, in terms of error and linearity. The results are thus promising for future AQC in situ, in perfusion bags.

Estimating the Analytical Performance of Raman Spectroscopy for Quantification of Active Ingredients in Human Stratum Corneum

Confocal Raman microscopy (CRM) has become a versatile technique that can be applied routinely to monitor skin penetration of active molecules. In the present study, CRM coupled to multivariate analysis (namely PLSR—partial least squares regression) is used for the quantitative measurement of an active ingredient (AI) applied to isolated (ex vivo) human stratum corneum (SC), using systematically varied doses of resorcinol, as model compound, and the performance is quantified according to key figures of merit defined by regulatory bodies (ICH, FDA, and EMA). A methodology is thus demonstrated to establish the limit of detection (LOD), precision, accuracy, sensitivity (SEN), and selectivity (SEL) of the technique, and the performance according to these key figures of merit is compared to that of similar established methodologies, based on studies available in literature. First, principal components analysis (PCA) was used to examine the variability within the spectral data set collected. Second, ratios calculated from the area under the curve (AUC) of characteristic resorcinol and proteins/lipids bands (1400–1500 cm⁻¹) were used to perform linear regression analysis of the Raman spectra. Third, cross-validated PLSR analysis was applied to perform quantitative analysis in the fingerprint region. The AUC results show clearly that the intensities of Raman features in the spectra collected are linearly correlated to resorcinol concentrations in the SC (R² = 0.999) despite a heterogeneity in the distribution of the active molecule in the samples. The Root Mean Square Error of Cross-Validation (RMSECV) (0.017 mg resorcinol/mg SC), The Root Mean Square of Prediction (RMSEP) (0.015 mg resorcinol/mg SC), and R² (0.971) demonstrate the reliability of the linear regression constructed, enabling accurate quantification of resorcinol. Furthermore, the results have enabled the determination, for the first time, of numerical criteria to estimate analytical performances of CRM, including LOD, precision using bias corrected mean square error prediction (BCMSEP), sensitivity, and selectivity, for quantification of the performance of the analytical technique. This is one step further towards demonstrating that Raman spectroscopy complies with international guidelines and to establishing the technique as a reference and approved tool for permeation studies.

Analytical Methods for the Determination of Major Drugs Used for the Treatment of COVID-19. A Review

At the beginning of the COVID-19 outbreak (end 2019 - 2020), therapeutic treatments based on approved drugs have been the fastest approaches to combat the new coronavirus pandemic. Nowadays several vaccines are available. However, the worldwide vaccination program is going to take a long time and its success will depend on the vaccine public's acceptance. Therefore, outside of vaccination, the repurposing of existing antiviral, anti-inflammatory and other types of drugs, have been considered an alternative medical strategy for the COVI-19 infection. Due to the broad clinical potential of the drugs, but also to their possible side effects, analytical methods are needed to monitor the drug concentrations in biological fluids and pharmaceutical products. This review deals with analytical methods developed in the period 2015 - July 2021 to detect potential drugs that, according to a literature survey, have been taken into consideration for the treatment of COVID-19. The drugs considered here have been selected on the basis of the number of articles published in the period January 2020-July 2021, using the combination of the keywords: COVID-19 and drugs or SARS-CoV-2 and drugs. A section is also devoted to monoclonal antibodies. Over the period considered, the analytical methods have been employed in a variety of real samples, such as body fluids (plasma, blood and urine), pharmaceutical products, environmental matrices and food.

Filamentous Bacteriophage-A Powerful Carrier for Glioma Therapy

Glioma is a life-threatening malignant tumor. Resistance to traditional treatments and tumor recurrence present major challenges in treating and managing this disease, consequently, new therapeutic strategies must be developed. Crossing the blood-brain barrier (BBB) is another challenge for most drug vectors and therapy medications. Filamentous bacteriophage can enter the brain across the BBB. Compared to traditional drug vectors, phage-based drugs offer thermodynamic stability, biocompatibility, homogeneity, high carrying capacity, self-assembly, scalability, and low toxicity. Tumor-targeting peptides from phage library and phages displaying targeting peptides are ideal drug delivery agents. This review summarized recent studies on phage-based glioma therapy and shed light on the developing therapeutics phage in the personalized treatment of glioma.