Multi-wavelength UV-based PAT tool for measuring protein concentration

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.

Analytical figures of merit of a dual-wavelength absorbance approach for real-time broad protein content monitoring for biomanufacturing

Real-time in-line broad protein content monitoring in biomanufacturing downstream unit operations enables the ability to optimize and afford consistent protein recovery. Protein determination from 2 to 400 mg/mL is demonstrated herein via real-time dual-wavelength LED photometric sensor configured at 280 and 310 nm. The figures of merit of this approach include measurement accuracy within the common acceptance criteria of 100 % ± 5 with negligible bias across the linear dynamic ranges. This work expands the utility of an LED based photometric sensor for biopharmaceutical process analytical technology (PAT) applications. It is also congruent with process digitalization and automation industry 4.0 concepts underpinned by Quality by Design (QbD) principles.

Image processing-based online analysis and feedback control system for droplet dripping process

Droplets find wide application across diverse industries, where maintaining their quality is paramount. Precise control over the substance content within droplets demands non-destructive and online analysis techniques, such as Process Analytical Technology (PAT), often integrated with control strategies. In this context, the present study focuses on the example of controlling droplet quality during the dripping process of pills. Leveraging the dripping and image acquisition systems established in previous research, a novel feedback control system centered on image processing was devised for the quality control of dripping pills. The system was developed and its efficacy was assessed, yielding satisfactory outcomes. The proposed system facilitates real-time monitoring of pill weight through the analysis of droplet images during the dripping process, thereby offering real-time feedback control of pill weight. Importantly, this system holds potential for broader applications beyond the scope of this study.

Development of a potential primary method for protein quantification via electrospray differential mobility analysis

Accurate protein quantification is the basis for establishing the metrological traceability of in vitro diagnostics or drug products. In this study, we established and validated a potential primary method for protein quantification based on electrospray-differential mobility analysis coupled with a condensation particle counter (ES-DMA-CPC). The analytical performance of this method was assessed using the certified reference material NIMCmAb, and the uncertainty of measurement was evaluated. The method was applied to the quantification of three other protein reference materials and one highly purified protein, including myoglobin, bovine serum albumin, IgG monoclonal antibody, and one highly purified fibrinogen, with a molecular weight range between 17 kDa and 340 kDa. In addition, when compared with isotope dilution mass spectrometry (IDMS) and UV‒VIS spectrophotometry approaches, the ES-DMA-CPC method showed good agreement with IDMS method for the quantification of these protein reference materials. Our proposed method provided an accurate quantification of proteins, especially those with large molecular weights. Moreover, our method could be a potential primary method for protein quantification and serve as a complement to IDMS method.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

Evaluation and Screening of Biopharmaceuticals using Multi-Angle Dynamic Light Scattering

Biopharmaceuticals are large, complex and labile therapeutic molecules prone to instability due to various factors during manufacturing. To ensure their safety, quality and efficacy, a wide range of critical quality attributes (CQAs) such as product concentration, aggregation, particle size, purity and turbidity have to be met. Size exclusion chromatography (SEC) is the gold standard to measure protein aggregation and degradation. However, other techniques such as dynamic light scattering (DLS) are employed in tandem to measure the particle size distribution (PSD) and polydispersity of biopharmaceutical formulations. In this study, the application of multi-angle dynamic light scattering (MADLS) was evaluated for the determination of particle size, particle concentration and aggregation in 3 different protein modalities, namely bovine serum albumin (BSA) and two biopharmaceuticals including a monoclonal antibody (mAb) and an enzyme. The obtained calibration curve (R² > 0.95) for the particle number concentration of the 3 proteins and the observed correlation between MADLS and SEC (R² = 0.9938) for the analysis of aggregation in the enzyme can be employed as a 3-in-1 approach to assessing particle size, concentration and aggregation for the screening and development of products while also reducing the number of samples and experiments required for analysis prior to other orthogonal tests.

Coupling of multivariate curve resolution-alternating least squares and mechanistic hard models to investigate antibody purification from human plasma using ion exchange chromatography

A two steps proposal for the purification of immunoglobulin G from human blood plasma is investigated. The first step is precipitation using cold ethanol based on the Cohn method with some modification and the second step is a chromatographic separation by DEAE-Sepharose FF resin as a weak anion exchanger. The presence of interferent in the region3 of chromatographic fractions, which is co-eluted with IgG, restricts the application of the mechanistic chromatography model. Therefore, multivariate cure resolution-alternating least squares (MCR-ALS) as a soft method is employed on measured absorbance data matrix from eluted fractions to recover pure concentration and spectral profiles. Besides, possible solutions for resolved concentration and spectral profiles are investigated. The reaction-dispersive model as a mechanistic hard model for the column is utilized for the evaluation of the ion exchange chromatography. Using a genetic algorithm as a global optimization method, mobile phase modulator (MPM) adsorption model parameters such as β, k_des,0, and K_eq,0, were fitted to the concentration profiles from MCR-ALS as 1.96, 2.87×10^-4 m³ mol^-1s^-1, and 1883, respectively. Furthermore, a new resampling incorporated non-parametric statistics is conducted to assess parameters' uncertainty. Values of 2.00, 1.10×10^-3 m³ mol^-1s^-1, and 549.80 are estimated median, and values of 0.05, 2.5×10^-3, and 691.00 are calculated interquartile range (IQR) for β, k_des,0, and K_eq,0, respectively. Finally, results show three and two outliers for β and k_des,0, respectively.