Advances in inline quantification of co-eluting proteins in chromatography: Process-data-based model calibration and application towards real-life separation issues

Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

Advanced control strategies for continuous capture of monoclonal antibodies based upon biolayer interferometry

The semi and fully continuous production of monoclonal antibodies (mAbs) has been gaining traction as a lower cost, and efficient production of mAbs to broaden patient access. To be truly flexible and adaptive to process demands, the industry has lacked sufficient advanced control strategies. The variation of the upstream product concentration typically cannot be handled by the downstream capture step, which is configured for a constant feed concentration and fixed binding capacity. This inflexibility leads to losses of efficiency and product yield. This study shows that these challenges can be overcome by a novel advanced control strategy concept that includes dynamic control throughout a perfusion bioreactor, with cell retention by alternating tangential flow, integrated with simulated moving bed (SMB) multi-column chromatography. The automation workflow and advanced control strategy were implemented through the use of a visual programming development environment. This enabled dynamic flow control across the upstream and downstream process integrated with a dynamic column loading of the SMB. A sensor prototype, based on continuous biolayer interferometry measurements was applied to detect mAb breakthrough within the last column flow-through to manage column switching. This novel approach provided higher specificity and lower background signal compared to commonly used spectroscopy methods, resulting in an optimized resin utilization while simultaneously avoiding product loss. The dynamic loading was found to provide a twofold increase of the mAb concentration in the eluate compared to a conservative approach with a predefined recipe with similar impurity removal. This concept shows that advanced control strategies can lead to significant process efficiency and yield improvement.

Column-free optical deconvolution of intrinsic fluorescence for a monoclonal antibody and its product-related impurities

The quantification of monoclonal antibody (mAb) aggregates and fragments using high pressure liquid chromatography-size exclusion chromatography (HPLC-SEC) typically requires off-line measurements that are time-consuming and therefore not compatible with real-time monitoring. However, it has been crucial to manufacturing and process development, and remains the industrial standard in the assessment of product-related impurities. Here we demonstrate that our previously established intrinsic time-resolved fluorescence (TRF) approach can be used to quantify the bioprocess critical quality attribute (CQA) of antibody product purity at various stages of a typical downstream process, with the potential to be developed for in-line bioprocess monitoring. This was directly benchmarked against industry-standard HPLC-SEC. Strong linear correlations were observed between outputs from TRF spectroscopy and HPLC-SEC, for the monomer and aggregate-fragment content, with R2 coefficients of 0.99 and 0.69, respectively. At total protein concentrations above 1.41 mg/mL, HPLC-SEC UV-Vis chromatograms displayed signs of detector saturation which reduced the accuracy of protein quantification, thus requiring additional sample dilution steps. By contrast, TRF spectroscopy increased in accuracy at these concentrations due to higher signal-to-noise ratios. Our approach opens the potential for reducing the time and labour required for validating aggregate content in mAb bioprocess stages from the several hours required for HPLC-SEC to a few minutes per sample.

Exploring features in chromatographic profiles as a tool for monitoring column performance

In the biopharmaceutical industry, chromatography resins have a finite number of uses before they start to age and degrade, typically due to losses of ligand integrity and/or density. The "health" of a column is predicted and validated by running multiple cycles on representative scale-down models and can be followed by real-time on-going validation during commercial production. Principal Component Analysis (PCA), Partial Least Square (PLS), Similarity Scores and Single One Point-MultiParameter Technique (SOP-MPT) along with machine learning principles were applied to explore the hypothesis that there is predictive capability of latent variables in chromatography absorbance profiles for process performance (step yield) and product quality (aggregates, fragments, host cell proteins (HCP) and DNA, and Protein A ligand). The first stage of this study is described in this paper: a MabSelect SuRe™ chromatography column was cycled with a method to establish the "normal" baseline for process performance and product quality, followed by runs using a harsher NaOH Cleaning in Place (CIP) procedure (with a higher NaOH concentration than that recommended by the vendor) to accelerate resin degradation. The different mathematical analytical tools correlated with resin degradation of the column (reflected in decreasing step yield and binding capacity with increasing running cycle), specifically when using the Wash, Elution and Strip phases of the chromatography method. Monomer, HCP and DNA content were not significantly impacted and therefore a correlation with product quality was inconsequential. Importantly, this work shows proof-of-concept that while more traditional methods of measuring resin integrity such as the height equivalent to a theoretical place (HETP) and Asymmetry (As) measurements could not detect changes in the integrity of the resin, PCA, PLS, Similarity Scores and SOP-MPT (to a lesser extent) applied to the absorbance data were capable of anticipating issues in the chromatography bed by identifying atypical outcomes.

Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise?

Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.

Continuous Countercurrent Chromatography in Protein Purification

Continuous countercurrent chromatography can be applied for both capture and polishing steps in the downstream processing of biopharmaceuticals. This chapter explains the concept of countercurrent operation, focusing on twin-column processes and how it can be used to alleviate the trade-offs of traditional batch chromatography with respect to resin utilization/productivity and yield/purity. CaptureSMB and MCSGP, the main twin-column continuous countercurrent chromatography processes, are explained, and the metrics by which they are compared to single-column chromatography are identified. Practical hints for process design and application examples are provided. Finally, regulatory aspects, scale-up, and UV-based process control are covered.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post translational modifications (PTMs), formulation and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

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.

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

Process chromatography is commonly used for purification of therapeutic proteins. Most chromatography skids that are used in such operations utilize single ultraviolet (UV) absorbance for monitoring and quantification of protein content. While the signal from such UV measurement is linear with respect to protein concentration at low values of protein concentrations, as the concentration increases across an eluting product peak, it goes manifold over the linear range, resulting in saturation of the UV signal and as a result incomplete quantification of the protein concentration. This can hamper our ability to decide on where to pool the process chromatography peak. It is evident that a simple, fast, and cost-effective methodology for on-line estimation of protein concentration is the need of the hour. In this paper, a multi-wavelength UV-based approach has been proposed for dilution-free on-line concentration estimation in the range of 0.8-100 g/L. Stable absorbance regions are picked up in the proposed approach from the multi-wavelength UV spectra, thereby offering a solution to the problem of saturation and non-linearity of the UV signal that is otherwise observed at higher concentrations. Further, using chemometrics tools such as principal component analysis (PCA) and partial least squares (PLS), the model has been validated for rapid quantification of protein concentration from the spectra. The predictions from the model were comparable to values measured using an existing UV-based offline method with an R² of>98%. The proposed process analytical technology (PAT) tool was successfully tested online and exhibited<8% variability and could effectively be used from capture to formulation to enable dilution-free online concentration measurement of IgG. The proposed tool is a simple, low-cost alternative to other methods and could enable integrated/continuous operations throughout the downstream train.

The design basis for the integrated and continuous biomanufacturing framework

An 8 ton per year manufacturing facility is described based on the framework for integrated and continuous bioprocessing (ICB) common to all known biopharmaceutical implementations. While the output of this plant rivals some of the largest fed-batch plants in the world, the equipment inside the plant is relatively small: the plant consists of four 2000 L single-use bioreactors and has a maximum flow rate of 13 L/min. The equipment and facility for the ICB framework is described in sufficient detail to allow biopharmaceutical companies, vendors, contract manufacturers to build or buy their own systems. The design will allow the creation of a global ICB ecosystem that will transform biopharmaceutical manufacturing. The design is fully backward compatible with legacy fed-batch processes. A clinical production scale is described that can produce smaller batch sizes with the same equipment as that used at the commercial scale. The design described allows the production of as little as 10 g to nearly 35 kg of drug substance per day.

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L^-1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L^-1 . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

Proof-of-concept analytical instrument for label-free optical deconvolution of protein species in a mixture

The adoption of process analytical technologies by the biopharmaceutical industry can reduce the cost of therapeutic drugs and facilitate investigation of new bioprocesses. Control of critical process parameters to retain critical product quality attributes within strict bounds is important for ensuring a consistently high product quality, but developing the sophisticated analytical technologies required has proven to be a major challenge. Here, we demonstrate a new optical technique for continuous monitoring of protein species as they are eluted from a chromatographic column, even when they fully co-elute with other protein species, without making any assumption about or peak-fitting to the elution profile. To achieve this, we designed and constructed a time-resolved intrinsic fluorescence lifetime chromatograph, and established an analytical framework for deconvolving and quantifying distinct but co-eluting protein species in real time. This proof-of-concept technology has potentially useful applications as a process analytical technology and more generally as an analytical technique for label-free quantification of proteins in mixtures.

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market.

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract.

Multi-attribute PAT for UF/DF of Proteins-Monitoring Concentration, particle sizes, and Buffer Exchange

Ultrafiltration/diafiltration (UF/DF) plays an important role in the manufacturing of biopharmaceuticals. Monitoring critical process parameters and quality attributes by process analytical technology (PAT) during those steps can facilitate process development and assure consistent quality in production processes. In this study, a lab-scale cross-flow filtration (CFF) device was equipped with a variable pathlength (VP) ultraviolet and visible (UV/Vis) spectrometer, a light scattering photometer, and a liquid density sensor (microLDS). Based on the measured signals, the protein concentration, buffer exchange, apparent molecular weight, and hydrodynamic radius were monitored. The setup was tested in three case studies. First, lysozyme was used in an UF/DF run to show the comparability of on-line and off-line measurements. The corresponding correlation coefficients exceeded 0.97. Next, urea-induced changes in protein size of glucose oxidase (GOx) were monitored during two DF steps. Here, correlation coefficients were ≥ 0.92 for static light scattering (SLS) and dynamic light scattering (DLS). The correlation coefficient for the protein concentration was 0.82, possibly due to time-dependent protein precipitation. Finally, a case study was conducted with a monoclonal antibody (mAb) to show the full potential of this setup. Again, off-line and on-line measurements were in good agreement with all correlation coefficients exceeding 0.92. The protein concentration could be monitored in-line in a large range from 3 to 120 g L^- 1. A buffer-dependent increase in apparent molecular weight of the mAb was observed during DF, providing interesting supplemental information for process development and stability assessment. In summary, the developed setup provides a powerful testing system for evaluating different UF/DF processes and may be a good starting point to develop process control strategies. Graphical Abstract Piping and instrumentation diagram of the experimental setup and data generated by the different sensors. A VP UV/Vis spectrometer (FlowVPE, yellow) measures the protein concentration. From the data of the light scattering photometer (Zetasizer, green) in the on-line measurement loop, the apparant molecular weight and z-average are calculated. The density sensor (microLDS) measures density and viscosity of the fluid in the on-line loop.

Fourier-transform infrared spectroscopy as a process analytical technology for near real time in-line estimation of the degree of PEGylation in chromatography

PEGylation of biological macromolecules is a well-established strategy to increase circulation half-life, decrease renal clearance and improve biocompatibility. PEGylation is a process in which polyethylene glycol (PEG) is covalently attached to a target molecule. The production of PEGylated biopharmaceuticals is usually executed by first producing and purifying the base molecule followed by the PEGylation reaction and purification of the modified molecule. Most PEGylated pharmaceuticals are produced by random PEGylation in batch mode and need to be purified as mainly the mono-PEGylated form is the desired drug product. In this work we propose a method to estimate the degree of PEGylation (DOP) of modified protein eluting from a chromatography column in near real-time. extended multiplicative signal correction (EMSC) is used in conjunction with asymmetric least squares (aaLS) to alleviate the influence of a salt gradient during ion exchange chromatography (IEX) on the spectral data. To convert the raw data obtained from spectral data to the actual DOP additional information obtained from off-line measurements is utilized. Once the signal correction is applied to in-line spectral data the DOP can be estimated without further use of off-line analytics. As the prerequisites for the application of this method are relatively easy to obtain it may also find use to speed up process development.

Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.

Factorization of preparative protein chromatograms with hard-constraint multivariate curve resolution and second-derivative pretreatment

Current biopharmaceutical production heavily relies on chromatography for protein purification. Recently, research has intensified towards finding suitable solutions to monitoring the chromatographic steps by multivariate spectroscopic sensors. Here, hard-constraint multivariate curve resolution (MCR) was investigated as a calibration-free method for factorizing bilinear preparative protein chromatograms into concentrations and spectra. Protein elutions were assumed to follow exponentially modified Gaussian (EMG) curves. In three case studies, MCR was applied to chromatograms of second-derivative ultraviolet and visible (UV-vis) spectra. The three case studies consisted of the separation of a ternary mixture (ribonuclease A, cytochrome c, and lysozyme), multiple binary chromatography runs of cytochrome c and lysozyme, and the separation of an antibody-drug conjugate (ADC) from unconjugated immunoglobulin G (IgG). In all case studies, good estimates of the elution curves were obtained. R² values compared to off-line analytics exceeded 0.90. The estimated spectra allowed for protein identification based on a protein spectral library. In summary, MCR was shown to be well able to factorize protein chromatograms without prior calibration. The method may thus substantially simplify analysis of multivariate protein chromatograms with multiple co-eluting species. It may be especially useful in process development.

An Integrated Approach to Aggregate Control for Therapeutic Bispecific Antibodies Using an Improved Three Column Mab Platform-Like Purification Process

Single chain variable fragment‐IgGs (scFv‐IgG) are a class of bispecific antibodies consisting of two single chain variable fragments (scFv) that are fused to an intact IgG molecule. A common trend observed for expression of scFv‐IgGs in mammalian cell culture is a higher level of aggregates (10%–30%) compared to mAbs, which results in lower purification yields in order to meet product quality targets. Furthermore, the high aggregate levels also pose robustness risks to a conventional mAb three column platform purification process which uses only the polishing steps (e.g., cation exchange chromatography [CEX]) for aggregate removal. Protein A chromatography with pH gradient elution, high performance tangential flow filtration (HP‐TFF) and calcium phosphate precipitation were evaluated at the bench scale as means of introducing orthogonal aggregate removal capabilities into other aspects of the purification process. The two most promising process variants, namely Protein A pH gradient elution followed by calcium phosphate precipitation were evaluated at pilot scale, demonstrating comparable performance. Implementing Protein A chromatography with gradient elution and/or calcium phosphate precipitation removed a sufficient portion of the aggregate burden prior to the CEX polishing step, enabling CEX to be operated robustly under conditions favoring higher monomer yield. From starting aggregate levels ranging from 15% to 23% in the condition media, levels were reduced to between 2% and 3% at the end of the CEX step. The overall yield for the optimal process was 71%. Results of this work suggest an improved three‐column mAb platform‐like purification process for purification of high aggregate scFv‐IgG bispecific antibodies is feasible. © 2018 The Authors. Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers. Biotechnol. Prog., 35: e2720, 2019

Process Analytical Approach towards Quality Controlled Process Automation for the Downstream of Protein Mixtures by Inline Concentration Measurements Based on Ultraviolet/Visible Light (UV/VIS) Spectral Analysis

Downstream of pharmaceutical proteins, such as monoclonal antibodies, is mainly done by chromatography, where concentration determination of coeluting components presents a major problem. Inline concentration measurements (ICM) by Ultraviolet/Visible light (UV/VIS)-spectral data analysis provide a label-free and noninvasive approach to significantly speed up the analysis and process time. Here, two different approaches are presented. For a test mixture of three proteins, a fast and easily calibrated method based on the non-negative least-squares algorithm is shown, which reduces the calibration effort compared to a partial least-squares approach. The accuracy of ICM for analytical separations of three proteins on an ion exchange column is over 99%, compared to less than 85% for classical peak area evaluation. The power of the partial least squares algorithm (PLS) is shown by measuring the concentrations of Immunoglobulin G (IgG) monomer and dimer under a worst-case scenario of completely overlapping peaks. Here, the faster SIMPLS algorithm is used in comparison to the nonlinear iterative partial least squares (NIPALS) algorithm. Both approaches provide concentrations as well as purities in real-time, enabling live-pooling decisions based on product quality. This is one important step towards advanced process automation of chromatographic processes. Analysis time is less than 100 ms and only one program is used for all the necessary communications and calculations.

Advances in downstream processing of biologics - Spectroscopy: An emerging process analytical technology

Process analytical technologies (PAT) for the manufacturing of biologics have drawn increased interest in the last decade. Besides being encouraged by the Food and Drug Administration's (FDA's) PAT initiative, PAT promises to improve process understanding, reduce overall production costs and help to implement continuous manufacturing. This article focuses on spectroscopic tools for PAT in downstream processing (DSP). Recent advances and future perspectives will be reviewed. In order to exploit the full potential of gathered data, chemometric tools are widely used for the evaluation of complex spectroscopic information. Thus, an introduction into the field will be given.

Real-time monitoring and control of the load phase of a protein A capture step

The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.

Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches

The biopharmaceutical industry is at a turning point moving toward a more customized and patient-oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high-throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry.

A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity

Within the Quality by Design (QbD) framework proposed by the International Conference on Harmonisation (ICH), high-throughput process development (HTPD) and mechanistic modeling are of outstanding importance for future biopharmaceutical chromatography process development. In order to compare the data derived from different column scales or batch chromatographies, the amount of adsorber has to be quantified with the same noninvasive method. Similarly, an important requirement for the implementation of mechanistic modeling is the reliable determination of column characteristics such as the ionic capacity Λ for ion-exchange chromatography with the same method at all scales and formats. We developed a method to determine the ionic capacity in column and batch chromatography, based on the adsorption/desorption of the natural, uv-detectable amino acid histidine. In column chromatography, this method produces results comparable to those of classical acid-base titration. In contrast to acid-base titration, this method can be adapted to robotic batch chromatographic experiments. We are able to convert the adsorber volumes in batch chromatography to the equivalent volume of a compressed column. In a case study, we demonstrate that this method increases the quality of SMA parameters fitted to batch adsorption isotherms, and the capability to predict column breakthrough experiments. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:666-677, 2016.

Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography

In chromatographic protein purification, process variations, aging of columns, or processing errors can lead to deviations of the expected elution behavior of product and contaminants and can result in a decreased pool purity or yield. A different elution behavior of all or several involved species leads to a deviating chromatogram. The causes for deviations are however hard to identify by visual inspection and complicate the correction of a problem in the next cycle or batch. To overcome this issue, a tool for root cause investigation in protein chromatography was developed. The tool combines a spectral deconvolution with inverse mechanistic modelling. Mid-UV spectral data and Partial Least Squares Regression were first applied to deconvolute peaks to obtain the individual elution profiles of co-eluting proteins. The individual elution profiles were subsequently used to identify errors in process parameters by curve fitting to a mechanistic chromatography model. The functionality of the tool for root cause investigation was successfully demonstrated in a model protein study with lysozyme, cytochrome c, and ribonuclease A. Deviating chromatograms were generated by deliberately caused errors in the process parameters flow rate and sodium-ion concentration in loading and elution buffer according to a design of experiments. The actual values of the three process parameters and, thus, the causes of the deviations were estimated with errors of less than 4.4%. Consequently, the established tool for root cause investigation is a valuable approach to rapidly identify process variations, aging of columns, or processing errors. This might help to minimize batch rejections or contribute to an increased productivity.