A novel method for continuous chromatographic separation of monoclonal antibody charge variants by combining displacement mode chromatography and step elution.

Charge heterogeneity of monoclonal antibodies is considered a critical quality attribute and hence needs to be monitored and controlled by the manufacturer. Typically, this is accomplished via separation of charge variants on cation exchange chromatography (CEX) using a pH or conductivity based linear gradient elution. Although an effective approach, this is challenging particularly during continuous processing as creation of linear gradient during continuous processing adds to process complexity and can lead to deviations in product quality upon slightest changes in gradient formation. Moreover, the long length of elution gradient along with the required peak fractionation makes process integration difficult. In this study, we propose a novel approach for separation of charge variants during continuous CEX chromatography by utilizing a combination of displacement mode chromatography and salt-based step elution. It has been demonstrated that while the displacement mode of chromatography enables control of acidic variants ≤26% in the CEX eluate, salt-based step gradient elution manages basic charge variant ≤25% in the CEX eluate. The proposed approach has been successfully demonstrated using feed materials with varying compositions. On comparing the designed strategy with 2-column concurrent (CC) chromatography, the resin specific productivity increased by 95% and resin utilization increased by 183% with recovery of main species >99%. Further, in order to showcase the amenability of the designed CEX method in continuous operation, the method was examined in our in-house continuous mAb platform.

Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography.

Protein A capture chromatography, which forms the core of the mAb purification platform, demands cautious use and maximum resin utilization due to high cost associated with resin. In this paper, we propose an application of advanced machine learning (ML) algorithms to address two most crucial objectives of column integrity breach and yield prediction for resin cycling study of protein A chromatography. Two approaches have been considered to detect anomalies in case of column integrity breach. The first approach utilized the traditional Principal Component Analysis (PCA) method for dimensionality reduction followed by anomaly detection using Isolation Forest (IF) algorithm. The second approach involved the application of deep learning neural network based Long Short Term Memory autoencoder (LSTM AE). Both the algorithms could successfully predict the column integrity failure 4 cycles ahead of the actual cycle. In the case of prediction of percentage yield decay, a partial least squares-artificial neural network (PLS-ANN) augmented model was utilized and compared with the traditional PLS regression model. The developed PLS-ANN model with higher R2 and lower RMSE values of 0.96 and 0.014 respectively could outperform the classical PLS model with lower R2 and RMSE values of 0.88 and 0.028, resulting in more accurate yield prediction. The developed ML algorithms for both case studies could not only successfully forecast anomalies by detecting subtle changes in column packing quality and thereby facilitate real time control decisions for preventive measures, a prerequisite for continuous manufacturing, but also demonstrated the ability to predict complex yield decay behaviour for protein A chromatography. As biopharmaceutical manufacturing adopts continuous processing, copious amount of data will be generated from the process and analytical equipment on the manufacturing floor, and the proposed advanced ML algorithms have significant potential in dealing with nonlinearities of the different unit operations simultaneously and facilitate real-time control decision making.

Continuous manufacturing of monoclonal antibodies: Automated downstream control strategy for dynamic handling of titer variations.

Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.

Dimerization of SARS-CoV-2 nucleocapsid protein affects sensitivity of ELISA based diagnostics of COVID-19.

Nucleocapsid protein (N protein) is the primary antigen of the virus for development of sensitive diagnostic assays of COVID-19. In this paper, we demonstrate the significant impact of dimerization of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) N-protein on sensitivity of enzyme-linked immunosorbent assay (ELISA) based diagnostics. The expressed purified protein from E. coli is composed of dimeric and monomeric forms, which have been further characterized using biophysical and immunological techniques. Indirect ELISA indicated elevated susceptibility of the dimeric form of the nucleocapsid protein for identification of protein-specific monoclonal antibody as compared to the monomeric form. This finding also confirmed with the modelled structure of monomeric and dimeric nucleocapsid protein via HHPred software and its solvent accessible surface area, which indicates higher stability and antigenicity of the dimeric type as compared to the monomeric form. The sensitivity and specificity of the ELISA at 95% CI are 99.0% (94.5-99.9) and 95.0% (83.0-99.4), respectively, for the highest purified dimeric form of the N protein. As a result, using the highest purified dimeric form will improve the sensitivity of the current nucleocapsid-dependent ELISA for COVID-19 diagnosis, and manufacturers should monitor and maintain the monomer-dimer composition for accurate and robust diagnostics.