Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development—A review

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.     

Bioprocessing in the Digital Age: The Role of Process Models

In this age of technology, the vision of manufacturing industries built of smart factories is not a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards digitalization and automation. Despite its tremendous growth reaching a sales value of worth $188 billion in 2017, the biopharmaceutical sector distinctly lags in this transition. Currently, the challenges are innovative market disruptions such as personalized medicine as well as increasing commercial pressure for faster and cheaper product manufacturing. Improvements in digitalization and data analytics have been identified as key strategic activities for the next years to face these challenges. Alongside, there is an emphasis by the regulatory authorities on the use of advanced technologies, proclaimed through initiatives such as Quality by Design (QbD) and Process Analytical Technology (PAT). In the manufacturing sector, the biopharmaceutical domain features some of the most complex and least understood processes. Thereby, process models that can transform process data into more valuable information, guide decision‐making, and support the creation of digital and automated technologies are key enablers. This review summarizes the current state of model‐based methods in different bioprocess related applications and presents the corresponding future vision for the biopharmaceutical industry to achieve the goals of Industry 4.0 while meeting the regulatory requirements.     

Implementation of chemometrics,design of experiments,and neural network analysis for prior process knowledge assessment,failure modes and effect analysis,scale-down model development,and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.     

Case study and application of process analytical technology (PAT) towards bioprocessing: use of on-line high-performance liquid chromatography (HPLC) for making real-time pooling decisions for process chromatography

Process analytical technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community due to the potential for continuous real time quality assurance resulting in improved operational control and compliance. This paper presents a PAT application for one of the most commonly used unit operation in bioprocessing, namely liquid chromatography. Feasibility of using a commercially available online-high performance liquid chromatography (HPLC) system for real-time pooling of process chromatography column is examined. Further, experimental data from the feasibility studies are modeled and predictions of the model are compared to actual experimental data. It is found that indeed for the application under consideration, the online-HPLC offers a feasible approach for analysis that can facilitate real-time decisions for column pooling based on product quality attributes. It is shown that implementing this analytical scheme allows us to meet two of the key goals that have been outlined for PAT, that is, "variability is managed by the process" and "product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions." Finally, the implications of implementing such a PAT application in a manufacturing environment are discussed. The application presented here can be extended to other modes of process chromatography and/or HPLC analysis.     

Case study and application of process analytical technology (PAT) towards bioprocessing: Use of tryptophan fluorescence as at‐line tool for making pooling decisions for process chromatography

Process analytical technology (PAT) has been gaining momentum in the biopharmaceutical community due to the potential for continuous real time quality assurance resulting in improved operational control and compliance. Two imperatives for implementing any PAT tool are that “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.” Recently, we have been examining the feasibility of applying different analytical tools to bioprocessing unit operations. We have previously demonstarted that commercially available online‐high performance liquid chromatography and ultra performance liquid chromatography systems can be used for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes (Rathore et al., 2008 a,b). In this article, we review an at‐line tool that can be used for pooling of process chromatography columns. We have demonstrated that our tryptophan fluorescence method offers a feasible approach and meets the requirements of a PAT application. It is significantly faster than the alternative of fractionation, offline analysis followed by pooling. Although the method as presented here is not an online method, this technique may offer better resolution for certain applications and may be a more optimal approach as it is very conducive to implementation in a manufacturing environment. This technique is also amenable to be used as an online tool via front face fluorescence measurements done concurrently with product concentration determination by UV. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.     

Process analytical technology (PAT) for biopharmaceutical products: Part I. concepts and applications

Process analytical technology (PAT) has been gaining momentum in the biotech community due to the potential for continuous real‐time quality assurance resulting in improved operational control and compliance. In this two part series, we address PAT as it applies to processes that produce biotech therapeutic products. In the first part, we address evolution of the underlying concepts and applications in biopharmaceutical manufacturing. We also present a literature review of applications in the areas of upstream and downstream processing to illustrate how implementation of PAT can help realize advanced approaches to ensuring product quality in real time. In the second part, we will explore similar applications in the areas of drug product manufacturing, rapid microbiology, and chemometrics as well as evolution of PAT in biotech processing. Biotechnol. Bioeng. 2010; 105: 276–284. Published 2009 Wiley Periodicals, Inc.     

Single‐step affinity purification of enzyme biotherapeutics: A platform methodology for accelerated process development

Downstream sample purification for quality attribute analysis is a significant bottleneck in process development for non‐antibody biologics. Multi‐step chromatography process train purifications are typically required prior to many critical analytical tests. This prerequisite leads to limited throughput, long lead times to obtain purified product, and significant resource requirements. In this work, immunoaffinity purification technology has been leveraged to achieve single‐step affinity purification of two different enzyme biotherapeutics (Fabrazyme® [agalsidase beta] and Enzyme 2) with polyclonal and monoclonal antibodies, respectively, as ligands. Target molecules were rapidly isolated from cell culture harvest in sufficient purity to enable analysis of critical quality attributes (CQAs). Most importantly, this is the first study that demonstrates the application of predictive analytics techniques to predict critical quality attributes of a commercial biologic. The data obtained using the affinity columns were used to generate appropriate models to predict quality attributes that would be obtained after traditional multi‐step purification trains. These models empower process development decision‐making with drug substance‐equivalent product quality information without generation of actual drug substance. Optimization was performed to ensure maximum target recovery and minimal target protein degradation. The methodologies developed for Fabrazyme were successfully reapplied for Enzyme 2, indicating platform opportunities. The impact of the technology is significant, including reductions in time and personnel requirements, rapid product purification, and substantially increased throughput. Applications are discussed, including upstream and downstream process development support to achieve the principles of Quality by Design (QbD) as well as integration with bioprocesses as a process analytical technology (PAT). © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:708–717, 2014     

Multi-angle light scattering as a process analytical technology measuring real-time molecular weight for downstream process control

For many protein therapeutics including monoclonal antibodies, aggregate removal process can be complex and challenging. We evaluated two different process analytical technology (PAT) applications that couple a purification unit performing preparative hydrophobic interaction chromatography (HIC) to a multi-angle light scattering (MALS) system. Using first principle measurements, the MALS detector calculates weight-average molar mass, M_w and can control aggregate levels in purification. The first application uses an in-line MALS to send start/stop fractionation trigger signals directly to the purification unit when preset M_w criteria are met or unmet. This occurs in real-time and eliminates the need for analysis after purification. The second application uses on-line ultra-high performance size-exclusion liquid chromatography to sample from the purification stream, separating the mAb species and confirming their M_w using a µMALS detector. The percent dimer (1.5%) determined by the on-line method is in agreement with the data from the in-line application (M_w increase of approximately 2750 Da). The novel HIC-MALS systems demonstrated here can be used as a powerful tool for real-time aggregate monitoring and control during biologics purification enabling future real time release of biotherapeutics.     

Cover Image,Volume 116, Number 9, September 2019

The new and rapid advancement in the complexity of biologics drug discovery has been driven by a deeper understanding of biological systems combined with innovative new therapeutic modalities, paving the way to breakthrough therapies for previously intractable diseases. These exciting times in biomedical innovation require the development of novel technologies to facilitate the sophisticated, multifaceted, high-paced workflows necessary to support modern large molecule drug discovery. A high-level aspiration is a true integration of “lab-on-a-chip” methods that vastly miniaturize cellulmical experiments could transform the speed, cost, and success of multiple workstreams in biologics development. Several microscale bioprocess technologies have been established that incrementally address these needs, yet each is inflexibly designed for a very specific process thus limiting an integrated holistic application. A more fully integrated nanoscale approach that incorporates manipulation, culture, analytics, and traceable digital record keeping of thousands of single cells in a relevant nanoenvironment would be a transformative technology capable of keeping pace with today's rapid and complex drug discovery demands. The recent advent of optical manipulation of cells using light-induced electrokinetics with micro- and nanoscale cell culture is poised to revolutionize both fundamental and applied biological research. In this review, we summarize the current state of the art for optical manipulation techniques and discuss emerging biological applications of this technology. In particular, we focus on promising prospects for drug discovery workflows, including antibody discovery, bioassay development, antibody engineering, and cell line development, which are enabled by the automation and industrialization of an integrated optoelectronic single-cell manipulation and culture platform. Continued development of such platforms will be well positioned to overcome many of the challenges currently associated with fragmented, low-throughput bioprocess workflows in biopharma and life science research.     

Disposable bioprocessing: The future has arrived

Increasing cost pressures are driving the rapid adoption of disposables in bioprocessing. While well ensconced in lab‐scale operations, the lower operating/ validation costs at larger scale and relative ease of use are leading to these systems entering all stages and operations of a typical biopharmaceutical manufacturing process. Here, we focus on progress made in the incorporation of disposable equipment with sensor technology in bioprocessing throughout the development cycle. We note that sensor patch technology is mostly being adapted to disposable cell culture devices, but future adaptation to downstream steps is conceivable. Lastly, regulatory requirements are also briefly assessed in the context of disposables and the Process Analytical Technologies (PAT) and Quality by Design (QbD) initiatives. Biotechnol. Bioeng. 2009;102: 348–356. © 2008 Wiley Periodicals, Inc.     

High-throughput and automation advances for accelerating single-cell cloning,monoclonality and early phase clone screening steps in mammalian cell line development for biologics production

Mammalian cell line development is a multistep process wherein timelines for developing clonal cells to be used as manufacturing cell lines for biologics production can commonly extend to 9 months when no automation or modern molecular technologies are involved in the workflow. Steps in the cell line development workflow involving single-cell cloning, monoclonality assurance, productivity and stability screening are labor, time and resource intensive when performed manually. Introduction of automation and miniaturization in these steps has reduced the required manual labor, shortened timelines from months to weeks, and decreased the resources needed to develop manufacturing cell lines. This review summarizes the advances, benefits, comparisons and shortcomings of different automation platforms available in the market for rapid isolation of desired clonal cell lines for biologics production.     

Scheduling data analytics work with performance guarantees: queuing and machine learning models in synergy

In today’s scaled out systems, co-scheduling data analytics work with high priority user workloads is common as it utilizes better the vast hardware availability. User workloads are dominated by periodic patterns, with alternating periods of high and low utilization, creating promising conditions to schedule data analytics work during low activity periods. To this end, we show the effectiveness of machine learning models in accurately predicting user workload intensities, essentially by suggesting the most opportune time to co-schedule data analytics work. Yet, machine learning models cannot predict the effects of performance interference when co-scheduling is employed, as this constitutes a “new” observation. Specifically, in tiered storage systems, their hierarchical design makes performance interference even more complex, thus accurate performance prediction is more challenging. Here, we quantify the unknown performance effects of workload co-scheduling by enhancing machine learning models with queuing theory ones to develop a hybrid approach that can accurately predict performance and guide scheduling decisions in a tiered storage system. Using traces from commercial systems we illustrate that queuing theory and machine learning models can be used in synergy to surpass their respective weaknesses and deliver robust co-scheduling solutions that achieve high performance.     

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications

Implementing real‐time product quality control meets one or both of the key goals outlined in FDA's PAT guidance: “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.” The first part of the paper presented an overview of PAT concepts and applications in the areas of upstream and downstream processing. In this second part, we present principles and case studies to illustrate implementation of PAT for drug product manufacturing, rapid microbiology, and chemometrics. We further present our thoughts on how PAT will be applied to biotech processes going forward. The role of PAT as an enabling component of the Quality by Design framework is highlighted. Integration of PAT with the principles stated in the ICH Q8, Q9, and Q10 guidance documents is also discussed. Biotechnol. Bioeng. 2010; 105: 285–295. Published 2009 Wiley Periodicals, Inc.     

高通量微型生物反应器的研究进展

近年来,哺乳动物细胞培养技术发展迅猛,基于此技术的生物制药行业更是异军突起。在激烈的生物药市场竞争中,缩短研发时间和降低研发成本是制胜的关键。与传统的生物反应器相比,高通量微型生物反应器具有操作简单、运行通量高、实验重复性好等优点,可大大缩短研发周期,降低人力、物力成本,因此成为了生物制药行业最新的研究热点之一。目前,已成功应用于生物药物研发的微型生物反应器有Simcell ^TM、Ambr 15 ^TM、Ambr 250 ^TM等,分别适用于工艺开发中的不同阶段。以上述三种微型生物反应器为例,介绍高通量微型反应器在哺乳动物细胞培养工艺开发中的研究现状及发展前景。     

An automated robotic platform for rapid profiling oligosaccharide analysis of monoclonal antibodies directly from cell culture

Oligosaccharides attached to Asn297 in each of the C_H2 domains of monoclonal antibodies play an important role in antibody effector functions by modulating the affinity of interaction with Fc receptors displayed on cells of the innate immune system. Rapid, detailed, and quantitative N-glycan analysis is required at all stages of bioprocess development to ensure the safety and efficacy of the therapeutic. The high sample numbers generated during quality by design (QbD) and process analytical technology (PAT) create a demand for high-performance, high-throughput analytical technologies for comprehensive oligosaccharide analysis. We have developed an automated 96-well plate-based sample preparation platform for high-throughput N-glycan analysis using a liquid handling robotic system. Complete process automation includes monoclonal antibody (mAb) purification directly from bioreactor media, glycan release, fluorescent labeling, purification, and subsequent ultra-performance liquid chromatography (UPLC) analysis. The entire sample preparation and commencement of analysis is achieved within a 5-h timeframe. The automated sample preparation platform can easily be interfaced with other downstream analytical technologies, including mass spectrometry (MS) and capillary electrophoresis (CE), for rapid characterization of oligosaccharides present on therapeutic antibodies.     

In-line monitoring of protein concentration with MIR spectroscopy during UFDF

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real-time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid-infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in-line monitoring, Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in-line fashion through a single-use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one-point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.     

Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives

Microalgae are well known for their ability to accumulate lipids intracellularly, which can be used for biofuels and mitigate CO₂ emissions. However, due to economic challenges, microalgae bioprocesses have maneuvered towards the simultaneous production of food, feed, fuel, and various high-value chemicals in a biorefinery concept. On-line and in-line monitoring of macromolecules such as lipids, proteins, carbohydrates, and high-value pigments will be more critical to maintain product quality and consistency for downstream processing in a biorefinery to maintain and valorize these markets. The main contribution of this review is to present current and prospective advances of on-line and in-line process analytical technology (PAT), with high-selectivity – the capability of monitoring several analytes simultaneously – in the interest of improving product quality, productivity, and process automation of a microalgal biorefinery. The high-selectivity PAT under consideration are mid-infrared (MIR), near-infrared (NIR), and Raman vibrational spectroscopies. The current review contains a critical assessment of these technologies in the context of recent advances in software and hardware in order to move microalgae production towards process automation through multivariate process control (MVPC) and software sensors trained on “big data”. The paper will also include a comprehensive overview of off-line implementations of vibrational spectroscopy in microalgal research as it pertains to spectral interpretation and process automation to aid and motivate development.     

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.     

SpeedScreen: label-free liquid chromatography-mass spectrometry-based high-throughput screening for the discovery of orphan protein ligands

A high-throughput screening methodology tailored to the discovery of ligands for known and orphan proteins is presented. With this method, labeling of neither target protein nor screened compounds is required, as the ligands are affinity selected by incubation of the protein with mixtures of compounds in aqueous binding buffer. Unbound small-molecular-weight compounds are removed from the target protein:ligand complex by rapid size-exclusion chromatography in the 96-well format. The protein fraction is analyzed subsequently by liquid chromatography-mass spectrometry for detection and identification of the bound ligand. This screening method was validated with known protein:ligand model systems and optimized for selection of high-affinity binders in an industrial screening environment. All sample handling steps and the analytics are rapid, robust, and largely automated, adopting this technology to the needs of present high-throughput screening processes. This affinity-selection technology, termed SpeedScreen, is currently an integral part of our lead discovery process.