QbD/PAT for bioprocessing: moving from theory to implementation

Harnessing near-infrared and Raman spectral sensing and artificial intelligence for real-time monitoring and precision control of bioprocess

Effective monitoring and control of bioprocesses are critical for industrial biomanufacturing. This study demonstrates the integration of near-infrared and Raman spectroscopy for real-time monitoring and precise control of gentamicin fermentation. The orthogonal method reduced redundant features and improved spectral model performance by 9.2-100.4 % in terms of the coefficient of determination (R2). The combinatorial spectral model outperformed single-source models in external validation (R2 > 0.99). An AI-based platform, combining dual-sensors data collection, ML-based prediction, and automated feeding control, was developed for fully automated fed-batch fermentation. This platform dynamically adjusted feeding rates, maintained low glucose concentrations (5 g/L) with accuracy and coefficient of variation below 2 %, and increased gentamicin C1a concentration (346.5 mg/L) by 33.0 % compared to traditional intermittent feeding. These findings underscore the transformative potential of combinatorial spectroscopy and machine learning for real-time bioprocess monitoring, offering a scalable solution for enhancing industrial fermentation efficiency and product titer.

Advances and opportunities in process analytical technologies for viral vector manufacturing

Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

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.

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.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

The Potential of Bacilli-Derived Biosurfactants as an Additive for Biocontrol against Alternaria alternata Plant Pathogenic Fungi

Fungal diseases caused by Alternaria alternata constitute a significant threat to the production and quality of a wide range of crops, including beans, fruits, vegetables, and grains. Traditional methods for controlling these diseases involve synthetic chemical pesticides, which can negatively impact the environment and human health. Biosurfactants are natural, biodegradable secondary metabolites of microorganisms that have also been shown to possibly have antifungal activity against plant pathogenic fungi, including A. alternata being sustainable alternatives to synthetic pesticides. In this study, we investigated the potential of biosurfactants of three bacilli (Bacillus licheniformis DSM13, Bacillus subtilis DSM10, and Geobacillus stearothermophilus DSM2313) as a biocontrol agent against A. alternata on beans as a model organism. For this fermentation, we describe using an in-line biomass sensor monitoring both permittivity and conductivity, which are expected to correlate with cell concentration and products, respectively. After the fermentation of biosurfactants, we first characterised the properties of the biosurfactant, including their product yield, surface tension decrement capability, and emulsification index. Then, we evaluated the antifungal properties of the crude biosurfactant extracts against A. alternata, both in vitro and in vivo, by analysing various plant growth and health parameters. Our results showed that bacterial biosurfactants effectively inhibited the growth and reproduction of A. alternata in vitro and in vivo. B. licheniformis manufactured the highest amount of biosurfactant (1.37 g/L) and demonstrated the fastest growth rate, while G. stearothermophilus produced the least amount (1.28 g/L). The correlation study showed a strong positive relationship between viable cell density VCD and OD600, as well as a similarly good positive relationship between conductivity and pH. The poisoned food approach in vitro demonstrated that all three strains suppressed mycelial development by 70–80% when applied with the highest tested dosage of 30%. Regarding in vivo investigations, B. subtilis post-infection treatment decreased the disease severity to 30%, whereas B. licheniformis and G. stearothermophilus post-infection treatment reduced disease severity by 25% and 5%, respectively. The study also revealed that the plant’s total height, root length, and stem length were unaffected by the treatment or the infection.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Employing QbD strategies to assess the impact of cell viability and density on the primary recovery of monoclonal antibodies

Quality by Design (QbD) is one of the most important tools for the implementation of Process Analytical Technology (PAT) in biopharmaceutical production. For optimal characterization of a monoclonal antibody (mAb) upstream process a stepwise approach was implemented. The upstream was divided into three process stages, namely inoculum expansion, production, and primary recovery, which were investigated individually. This approach enables analysis of process parameters and associated intermediate quality attributes as well as systematic knowledge transfer to subsequent process steps. Following previous research, this study focuses on the primary recovery of the mAb and thereby marks the final step toward a holistic characterization of the upstream process. Based on gained knowledge during the production process evaluation, the cell viability and density were determined as critical parameters for the primary recovery. Directed cell viability adjustment was achieved using cytotoxic camptothecin in a novel protocol. Additionally, the cell separation method was added to the Design of Experiments (DoE) as a qualitative factor and varied between filtration and centrifugation. To assess the quality attributes after cell separation, the bioactivity of the mAb was analyzed using a cell-based assay and the purity of the supernatant was evaluated by measurement of process related impurities (host cell protein proportion, residual DNA). Multivariate data analysis of the compiled data confirmed the hypothesis that the upstream process has no significant influence on the bioactivity of the mAb. Therefore, process control must be tuned towards high mAb titers and purity after the primary recovery, enabling optimal downstream processing of the product. To minimize amounts of host cell proteins and residual DNA the cell viability should be maintained above 85% and the cell density should be controlled around 15 × 106 cells/ml during the cell removal. Thereby, this study shows the importance of QbD for the characterization of the primary recovery of mAbs and highlights the useful implementation of the stepwise approach over subsequent process stages.

Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.

A novel framework of surrogate-based feasibility analysis for establishing design space of twin-column continuous chromatography

Multi-column periodic counter-current chromatography (PCC) has attracted wide attention for the primary capture for the purpose of achieving continuous biomanufacturing. Consequently, determining the design space of the continuous capture process is very important to facilitate process understanding and improving product quality. In this work, we proposed a novel approach to identify the design space of continuous chromatography to balance the computational complexity and model predictions. Specifically, surrogate-based feasibility analysis with adaptive sampling is applied to establish the design space of twin-column CaptureSMB process. The surrogate model is constructed based on the developed mechanistic model for the identification of the design space. The effects of process variables (including interconnected loading time, interconnected flowrate, and batch flowrate) on the design space are comprehensively examined based on an active set strategy. Besides, essential factors like recovery-regeneration time and constraints of column performance parameters (yield, productivity, and capacity utilization) are thoroughly investigated. The impact of design variables such as column length is also studied.

Taking the pulse of bioprocesses: at-line and in-line monitoring of mammalian cell cultures

Real-time and near real-time monitoring of cell culture processes are critical to the evolving process analytical technology (PAT) paradigm for upstream bioprocessing. The responses measured from these analytical instruments can enable rapid feedback to perturbations that can otherwise lead to batch failures. Historically, real-time monitoring of bioreactor processes has been relegated to parameters such as pH, dissolved oxygen, and temperature. Other analytical results, such as cell growth and metabolites, are provided through manual daily sampling. In order to reduce sample error and increase throughput, real-time and near real-time instruments have been developed. Here we discuss recent advances in these technologies. This article aims to focus on other developing at-line and in-line technologies that enable monitoring of bioreactor processes, including dielectric spectroscopy, NIR, off-gas spectrometry, integrated at-line HPLC, and nanofluidic devices for monitoring cell growth and health, metabolites, titer, and product quality.

Bioprocess Control: Current Progress and Future Perspectives

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

Towards in silico Process Modeling for Vaccines

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Monitoring online biomass with a capacitance sensor during scale-up of industrially relevant CHO cell culture fed-batch processes in single-use bioreactors

In 2004, the FDA published a guideline to implement process analytical technologies (PAT) in biopharmaceutical processes for process monitoring to gain process understanding and for the control of important process parameters. Viable cell concentration (VCC) is one of the most important key performance indicator (KPI) during mammalian cell cultivation processes. Commonly, this is measured offline. In this work, we demonstrated the comparability and scalability of linear regression models derived from online capacitance measurements. The linear regressions were used to predict the VCC and other familiar offline biomass indicators, like the viable cell volume (VCV) and the wet cell weight (WCW), in two different industrially relevant CHO cell culture processes (Process A and Process B). Therefore, different single-use bioreactor scales (50–2000 L) were used to prove feasibility and scalability of the in-line sensor integration. Coefficient of determinations of 0.79 for Process A and 0.99 for Process B for the WCW were achieved. The VCV was described with high coefficients of determination of 0.96 (Process A) and 0.98 (Process B), respectively. In agreement with other work from the literature, the VCC was only described within the exponential growth phase, but resulting in excellent coefficients of determination of 0.99 (Process A) and 0.96 (Process B), respectively. Monitoring these KPIs online using linear regression models appeared to be scale-independent, enabled deeper process understanding (e.g. here demonstrated in monitoring, the feeding profile) and showed the potential of this method for process control.

Implementing Process Analytical Technology for the Production of Recombinant Proteins in Escherichia coli Using an Advanced Controller Scheme

The performance of a bioreactor in meeting process goals is affected by the microorganism used, medium composition, and operating conditions. A typical bioreactor uses a supervisory control and data acquisition (SCADA) system for control, and a combination of software and hardware tools for real-time data analysis. However, when the process is disrupted by utility or instrumentation failure, typical process controllers may be unable to reinstate normal operating conditions before the cells in the reactor shift to unfavorable metabolic regimes. The objective of this study is to examine how the response of a controller affects process recovery when disruptive incidences occur under a process analytical technology (PAT) framework. The process used for this investigation is the production of lethal toxin-neutralizing factor (LTNF) by Escherichia coli (E. coli), which is controlled by a decoupled input-output-linearizing controller (DIOLC). The performance of the DIOLC is compared to a proportional integral derivative (PID) controller subjected to the same conditions. The disruptions are introduced manually and the effect of controller action on process recovery and LTNF synthesis is measured in terms of peak purity and concentration. It is observed that DIOLC performs better after reinstating operating conditions and results in a meaningful improvement in performance.

QbD Based Media Development for the Production of Fab Fragments in E. coli

Ranibizumab is a biotherapeutic Fab fragment used for the treatment of age-related macular degeneration and macular oedema. It is currently expressed in the gram-negative bacterium, Escherichia coli. However, low expression levels result in a high manufacturing cost. The protein expression can be increased by manipulating nutritional requirements (carbon source, nitrogen source, buffering agent), process parameters (pH, inducer concentration, agitation, temperature), and the genetic make-up of the producing strain. Further, understanding the impact of these factors on product quality is a requirement as per the principles of Quality by Design (QbD). In this paper, we examine the effect of various media components and process parameters on the expression level and quality of the biotherapeutic. First, risk analysis was performed to shortlist different media components based on the literature. Next, experiments were performed to screen these components. Eight components were identified for further investigation and were examined for their effect and interactions using a Fractional Factorial experimental design. Sucrose, biotin, and pantothenate were found to have the maximum effect during Fab production. Furthermore, cyanocobalamin glutathione and biotin-glutathione were the most significant interactions observed. Product identification was performed with Liquid Chromatography–Mass Spectrometry (LC-MS), the expression level was quantified using Bio-layer Interferometry, Reverse Phase-HPLC, and SDS-PAGE, and product quality were measured by RP-HPLC. Overall, a five-fold enhancement of the target protein titer was obtained (from 5 mg/L to 25 mg/L) using the screened medium components vis-a-vis the basal medium, thereby demonstrating the efficacy of the systematic approach purported by QbD.

Development of L-Asparaginase Biobetters: Current Research Status and Review of the Desirable Quality Profiles

L-Asparaginase (ASNase) is a vital component of the first line treatment of acute lymphoblastic leukemia (ALL), an aggressive type of blood cancer expected to afflict over 53,000 people worldwide by 2020. More recently, ASNase has also been shown to have potential for preventing metastasis from solid tumors. The ASNase treatment is, however, characterized by a plethora of potential side effects, ranging from immune reactions to severe toxicity. Consequently, in accordance with Quality-by-Design (QbD) principles, ingenious new products tailored to minimize adverse reactions while increasing patient survival have been devised. In the following pages, the reader is invited for a brief discussion on the most recent developments in this field. Firstly, the review presents an outline of the recent improvements on the manufacturing and formulation processes, which can severely influence important aspects of the product quality profile, such as contamination, aggregation and enzymatic activity. Following, the most recent advances in protein engineering applied to the development of biobetter ASNases (i.e., with reduced glutaminase activity, proteolysis resistant and less immunogenic) using techniques such as site-directed mutagenesis, molecular dynamics, PEGylation, PASylation and bioconjugation are discussed. Afterwards, the attention is shifted toward nanomedicine including technologies such as encapsulation and immobilization, which aim at improving ASNase pharmacokinetics. Besides discussing the results of the most innovative and representative academic research, the review provides an overview of the products already available on the market or in the latest stages of development. With this, the review is intended to provide a solid background for the current product development and underpin the discussions on the target quality profile of future ASNase-based pharmaceuticals.

Mechanistic modeling based process analytical technology implementation for pooling in hydrophobic interaction chromatography

A major challenge in chromatography purification of therapeutic proteins is batch-to-batch variability with respect to impurity levels and product concentration in the feed. Mechanistic model can enable process analytical technology (PAT) implementation by predicting impact of such variations and thereby improving the robustness of the resulting process and controls. This article presents one such application of mechanistic model of hydrophobic interaction chromatography (HIC) as a PAT tool for making robust pooling decisions to enable clearance of aggregates for a monoclonal antibody (mAb) therapeutic. Model predictions were performed before the actual chromatography experiments to facilitate feedforward control. The approach has been successfully demonstrated for four different feeds with varying aggregate levels (3.84%-5.54%) and feed concentration (0.6 mg/mL-1 mg/mL). The resulting pool consistently yielded a product with 1.32 ± 0.03% aggregate vs. a target of 1.5%. A comparison of the traditional approach involving column fractionation with the proposed approach indicates that the proposed approach results in achievement of satisfactory product purity (98.68 ± 0.03% for mechanistic model based PAT controlled pooling vs. 98.64 ± 0.16% for offline column fractionation based pooling). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2758, 2019.

Raw material quality assessment approaches comparison in pectin production

Different opportunities are explored to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production as fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal, and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2762, 2019.

Implementation of quality by design toward processing of food products

Quality by design (QbD) is a systematic approach that begins with predefined objectives and emphasizes product and process understanding and process control. It is an approach based on principles of sound science and quality risk management. As the food processing industry continues to embrace the idea of in-line, online, and/or at-line sensors and real-time characterization for process monitoring and control, the existing gaps with regard to our ability to monitor multiple parameters/variables associated with the manufacturing process will be alleviated over time. Investments made for development of tools and approaches that facilitate high-throughput analytical and process development, process analytical technology, design of experiments, risk analysis, knowledge management, and enhancement of process/product understanding would pave way for operational and economic benefits later in the commercialization process and across other product pipelines. This article aims to achieve two major objectives. First, to review the progress that has been made in the recent years on the topic of QbD implementation in processing of food products and second, present a case study that illustrates benefits of such QbD implementation.

Knowledge management in a waste based biorefinery in the QbD paradigm

Shifting resource base from fossil feedstock to renewable raw materials for production of chemical products has opened up an area of novel applications of industrial biotechnology-based process tools. This review aims to provide a concise and focused discussion on recent advances in knowledge management to facilitate efficient and optimal operation of a biorefinery. Application of quality by design (QbD) and process analytical technology (PAT) as tools for knowledge creation and management at different levels has been highlighted. Role of process integration, government policies, knowledge exchange through collaboration, and use of databases and computational tools have also been touched upon.

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

Bridging the gap between PAT concepts and implementation: An integrated software platform for fermentation

Bioreactor control significantly impacts both the amount and quality of the product being manufactured. The complexity of the control strategy that is implemented increases with reactor size, which may vary from thousands to tens of thousands of litres in commercial manufacturing. The Process Analytical Technology (PAT) initiative has highlighted the need for having robust monitoring tools and effective control schemes that are capable of taking real time information about the critical quality attributes (CQA) and the critical process parameters (CPP) and executing immediate response as soon as a deviation occurs. However, the limited flexibility that present commercial software packages offer creates a hurdle. Visual programming environments have gradually emerged as potential alternatives to the available text based languages. This paper showcases development of an integrated programme using a visual programming environment for a Sartorius BIOSTAT® B Plus 5L bioreactor through which various peripheral devices are interfaced. The proposed programme facilitates real-time access to data and allows for execution of control actions to follow the desired trajectory. Major benefits of such integrated software system include: (i) improved real time monitoring and control; (ii) reduced variability; (iii) improved performance; (iv) reduced operator-training time; (v) enhanced knowledge management; and (vi) easier PAT implementation.