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In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell culture samples to determine virus contamination. Another application of microscopy is to verify clonality during cell line development. Conventionally, inspection of these microscopy images is performed manually by human analysts. This is both tedious and time… Show more

In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell culture samples to determine virus contamination. Another application of microscopy is to verify clonality during cell line development. Conventionally, inspection of these microscopy images is performed manually by human analysts. This is both tedious and time consuming. In this paper, we propose using supervised deep learning algorithms to automate the cell detection processes mentioned above. Show less

Modern biopharmaceutical facilities have modular designs to quickly pivot from producing one medicine to another.
• Prior product-specific history might not be available to develop multivariate statistical models to monitor new processes associated with new products real-time.
•Systematic digital framework based on steady state operation detection in conjunction with appropriate time series processing, fed to a statistical process monitoring model, is developed to monitor processes with… Show more

Modern biopharmaceutical facilities have modular designs to quickly pivot from producing one medicine to another.
• Prior product-specific history might not be available to develop multivariate statistical models to monitor new processes associated with new products real-time.
•Systematic digital framework based on steady state operation detection in conjunction with appropriate time series processing, fed to a statistical process monitoring model, is developed to monitor processes with no prior history real-time.
•The solution is deployed within a scalable machine learning digital platform to enable rapid scaling. Show less

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One… Show more

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real-time monitoring is through the application of just-in-time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman-based JITL method relies on the K-nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE–JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables. Show less

Digital innovation is a critical enabler for advancing a
molecule in the pipeline to make it into a product. Right
way of capturing data by using FAIR principles is one of the key requirements to ensure seamless data flow and enable other advanced applications such as machine learning and computational modeling across the value chain. These then enable speed to patients, efficiency in operations with increased productivity and support robust design. We are also focusing on establishing… Show more

Digital innovation is a critical enabler for advancing a
molecule in the pipeline to make it into a product. Right
way of capturing data by using FAIR principles is one of the key requirements to ensure seamless data flow and enable other advanced applications such as machine learning and computational modeling across the value chain. These then enable speed to patients, efficiency in operations with increased productivity and support robust design. We are also focusing on establishing the digital mindset across our organization. We have provided examples using machine learning and computational modeling taking technical development to the next level using digital innovation. Show less

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling… Show more

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. Show less

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity… Show more

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments. Show less

The articles in this special collection address topics on the use of artificial intelligence (AI) techniques and technologies as applied to drug discovery, automated gene editing with a single-cell platform using computer vision, and a specific application of machine learning (ML) breaching the biorelevance gap in antioxidant assays.

Spectroscopic methods play an instrumental role in the implementation of the U.S. Food and Drug Administration outlined process analytical technology for biopharmaceutical manufacturing. Industrial spectroscopic calibration models are typically developed in an offline setting using traditional methods, such as partial least squares and principal component regression. Apart from the limiting performances of these conventional models under time-varying operating conditions, these methods require… Show more

Spectroscopic methods play an instrumental role in the implementation of the U.S. Food and Drug Administration outlined process analytical technology for biopharmaceutical manufacturing. Industrial spectroscopic calibration models are typically developed in an offline setting using traditional methods, such as partial least squares and principal component regression. Apart from the limiting performances of these conventional models under time-varying operating conditions, these methods require access to extensive historical data, which are seldom available in biopharmaceutical manufacturing. In this article, we propose a novel spatiotemporal just-in-time learning (ST-JITL) based spectroscopic model calibration platform for automatic training and maintenance of calibration models using routine batch data. The proposed ST-JITL framework uses Gaussian processes (GPs) for local model calibration. A GP model not only exhibits superior performance over traditional methods but also provides credibility intervals around the model predictions. The efficacy of the ST-JITL based model calibration platform is demonstrated in predicting the critical performance parameters of an industrial cell culture process. Show less

This whitepaper summarizes the outcome of the first Advanced Process Control (APC) workshop in the pharmaceutical industry, presented by AIChE PD2M, and held in Washington DC, Sep 30 to Oct 01, 2019. Approximately 50 attendees from regulatory agencies, industry and academia had an opportunity to share perspectives and best practices on the business, technical and regulatory aspects of APC for both small and large molecule drug manufacturing. The event consisted of keynote talks, case studies… Show more

This whitepaper summarizes the outcome of the first Advanced Process Control (APC) workshop in the pharmaceutical industry, presented by AIChE PD2M, and held in Washington DC, Sep 30 to Oct 01, 2019. Approximately 50 attendees from regulatory agencies, industry and academia had an opportunity to share perspectives and best practices on the business, technical and regulatory aspects of APC for both small and large molecule drug manufacturing. The event consisted of keynote talks, case studies and panel discussions, filled with lively interactions that focused on: (a) Business drivers for APC in pharma; (b) Alignment on the definitions of key terminology; (c) Clarification of roles and relationships of APC with regards to popular initiatives such as Quality by Design (QbD), Process Analytical Technology (PAT), Real Time Release testing (RTRt), Continued Process Verification (CPV), continuous manufacturing and digital manufacturing; (d) APC manufacturing implementation considerations; (e) Quality system and regulatory considerations for APC implementation; (f) APC opportunities in modular manufacturing, process intensification, integrated continuous manufacturing. (g) standards, training, and collaboration. Show less

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real‐time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in… Show more

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real‐time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in. Furthermore, once models are calibrated, it is typical for the model performance to degrade over time due to various recipe changes, raw material variability, and process drifts. Maintaining the performance of industrial Raman models is further complicated due to the lack of a systematic approach to assessing the performance of Raman models. In this article, we propose a real‐time just‐in‐time learning (RT‐JITL) framework for … Show less

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real‐time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully… Show more

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real‐time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully calibrated. The existing calibration procedure is nontrivial to implement as it necessitates executing multiple carefully designed experiments for generating relevant calibration sets. Further, existing procedure yields calibration models that are reliable only in operating conditions they were calibrated in. This creates a unique challenge in clinical manufacturing where products have limited production history. In this paper, a novel … Show less

This article addresses the problem of real-time statistical batch process monitoring (BPM) for processes with limited production history; herein, referred to as the ‘Low-N’ problem. The Low-N problem is a longstanding, industry-wide problem in biopharmaceutical manufacturing that challenges the theoretical foundations and practical applicability of the existing BPM platform. In this article, we propose an approach to transition from a Low-N scenario to a Large-N scenario by generating an… Show more

This article addresses the problem of real-time statistical batch process monitoring (BPM) for processes with limited production history; herein, referred to as the ‘Low-N’ problem. The Low-N problem is a longstanding, industry-wide problem in biopharmaceutical manufacturing that challenges the theoretical foundations and practical applicability of the existing BPM platform. In this article, we propose an approach to transition from a Low-N scenario to a Large-N scenario by generating an arbitrarily large number of insilico batch data sets. The proposed method is a combination of hardware exploitation and algorithm development. To this effect, we propose a block-learning method for a Bayesian non-parametric model of a batch process, and then use probabilistic programming to generate an arbitrarily large number of dynamic insilico campaign data sets. The proposed solution not only alleviates the monitoring … Show less

Chemical engineering principles apply widely in development and production of biologics. This chapter describes in greater detail various unit operations that are typically involved in production of biologics and the specific fundamental principles associated with these unit operations. The complexity of proteins drugs and their biological production systems pose extraordinary challenges to the chemical engineers with responsibility for industrial‐scale manufacturing of these products. Design… Show more

Chemical engineering principles apply widely in development and production of biologics. This chapter describes in greater detail various unit operations that are typically involved in production of biologics and the specific fundamental principles associated with these unit operations. The complexity of proteins drugs and their biological production systems pose extraordinary challenges to the chemical engineers with responsibility for industrial‐scale manufacturing of these products. Design, development, and operation of these processes require complete understanding and appreciation for the intricacies of proteins and living cells. The chapter examines how concepts taught in traditional chemical engineering are applied to design equipment and develop processes that allow modern‐day mass manufacture of biotechnology products. Large‐scale protein manufacturing utilizes a series of unit operations to grow … Show less

Raman spectroscopy has taken large strides in recent years as more Raman vendors have developed handheld Raman units capable of carrying out raw material identification or verification. Raman spectroscopy is used as a fingerprinting tool to capture the unique Raman spectrum of each raw material. There are many benefits of adapting this new Process Analytical Technology (PAT) tool to the biopharmaceutical industry. Critical technical considerations and unique challenges that must be overcome to… Show more

Raman spectroscopy has taken large strides in recent years as more Raman vendors have developed handheld Raman units capable of carrying out raw material identification or verification. Raman spectroscopy is used as a fingerprinting tool to capture the unique Raman spectrum of each raw material. There are many benefits of adapting this new Process Analytical Technology (PAT) tool to the biopharmaceutical industry. Critical technical considerations and unique challenges that must be overcome to implement Raman spectroscopy in the biopharmaceutical industry are discussed. Show less

Agent-based modeling (ABM) is a novel modeling approach to address the complexity of systems that comprise heterogeneous interacting individuals. ABM is naturally hybrid for its ability to integrate quantitative and qualitative knowledge, deals with multiple levels of actions, and performs best when combined with conventional modeling approaches. In this study, a hybrid agent-based platform is developed using high performance computing to simulate a mammalian cell culture bioreactor. This… Show more

Agent-based modeling (ABM) is a novel modeling approach to address the complexity of systems that comprise heterogeneous interacting individuals. ABM is naturally hybrid for its ability to integrate quantitative and qualitative knowledge, deals with multiple levels of actions, and performs best when combined with conventional modeling approaches. In this study, a hybrid agent-based platform is developed using high performance computing to simulate a mammalian cell culture bioreactor. This platform enables communication of the agent-based model with the first principle models to account for quantitative changes in nutrient and metabolite concentrations and their distribution in the bioreactor. The model can predict viable cell density, and the cell cycle distributions along with the important nutrients and metabolites such as glucose and lactate. Integrating this cell culture agent-based model with highperformance computing leverages parallel processing to allow the ABM program to run faster, more efficiently, and with a higher capacity for the number of cells that can be modeled. The model is validated using bench-scale bioreactor experiments and showed good agreement with experimental data. Show less

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real‐time. The state‐of‐the‐art real‐time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry‐wide problem in… Show more

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real‐time. The state‐of‐the‐art real‐time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry‐wide problem in BPM, referred to as the “Low‐N” problem, wherein a product has a limited production history. The current best industrial practice to address the Low‐N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in … Show less

An Agent-based computational modeling approach was used to develop a model to simulate individual mammalian cell behavior and its cycle regulation in response to dynamic bioreactor conditions. The model can be used as an in silico cell cycle predictor when provided with data from ongoing bioreactor runs. Rules were developed to regulate the distinct cell cycle events as well as to apply decision-making at the critical cellular checkpoints. The model was constructed and validated using different… Show more

An Agent-based computational modeling approach was used to develop a model to simulate individual mammalian cell behavior and its cycle regulation in response to dynamic bioreactor conditions. The model can be used as an in silico cell cycle predictor when provided with data from ongoing bioreactor runs. Rules were developed to regulate the distinct cell cycle events as well as to apply decision-making at the critical cellular checkpoints. The model was constructed and validated using different sets of experimental cell culture conditions with cell culture parameters measured using a flow cytometer and other instrumentation. Show less

A hybrid simulation framework was proposed to predict the dynamics in cell culture bioreactors. The model is based on a multi-agent approach where CHO cells are considered as individuals (agents) following a rule base governing their behavior, while a flux balance model is embedded in agents to predict quantitative changes in nutrient and metabolite concentrations. The model takes the measured dissolved oxygen, and sodium data as input along with initial cell culture conditions and predicts the… Show more

A hybrid simulation framework was proposed to predict the dynamics in cell culture bioreactors. The model is based on a multi-agent approach where CHO cells are considered as individuals (agents) following a rule base governing their behavior, while a flux balance model is embedded in agents to predict quantitative changes in nutrient and metabolite concentrations. The model takes the measured dissolved oxygen, and sodium data as input along with initial cell culture conditions and predicts the dynamics of viable cell density, viability, concentrations of glucose and lactate. The model showed good agreement with the experimental findings from our laboratory for two sets of cell culture experiments. Show less

Predictive monitoring is a key feature of biopharmaceutical manufacturing; making predictions about the key process end points such as process performance indicators or quality attributes using a process model offers the unique advantages of process improvement and optimisation, and helps give insights into variability.

Transferring data in electronic format between suppliers and end users greatly facilitates information exchange and enhances information usability. This article documents a standard format for electronic-data (eData) exchange between suppliers and end users. Initially, eData will operate with information available in Certificates of Analysis (CoA) or Certificates of Conformance (CoC), though it can be extended to handle in-process information from the supplier and its incoming raw materials, as… Show more

Transferring data in electronic format between suppliers and end users greatly facilitates information exchange and enhances information usability. This article documents a standard format for electronic-data (eData) exchange between suppliers and end users. Initially, eData will operate with information available in Certificates of Analysis (CoA) or Certificates of Conformance (CoC), though it can be extended to handle in-process information from the supplier and its incoming raw materials, as appropriate. This information can complement information gathered using spectral inspection technology (such as near-infrared (NIR) and Raman) or key geometric or physical attributes (such as material strength). Exchanged information can be used to examine the impact of variability on process performance and product quality using multivariate analysis (MVA). However, the eData model is not initially intended as a replacement for formal CoA/CoC information. The project’s longterm goals include developing predictive models for adaptive process control, implementation of process analytical technology (PAT), better specification development, and control of rawmaterial variation at the supplier. These advances will take place in multiple stages and affect multiple knowledge elements by effectively employing big data capture and analytics. Show less

Filling process is one of the critical unit operations of manufacturing that requires thorough understanding of the influence of the solution properties on the process performance, especially accuracy and elegance, and the impact of the process parameters on the product quality attributes. The solution properties and the process performance are further influenced by the environmental conditions. Various fillers operate through different mechanisms/principles and they exert different nature of… Show more

Filling process is one of the critical unit operations of manufacturing that requires thorough understanding of the influence of the solution properties on the process performance, especially accuracy and elegance, and the impact of the process parameters on the product quality attributes. The solution properties and the process performance are further influenced by the environmental conditions. Various fillers operate through different mechanisms/principles and they exert different nature of stresses upon the drug product, identifying a right filler along with the optimal filling conditions is another key component in the design and development of a robust filling process. Quality by Design (QbD) approach offers various tools to scientifically better understand the product and process and helps in quality risk management. In this chapter, the application of various elements of QbD in the development and transfer of a commercial filling process of an X-mAb for prefilled syringe (PFS) presentation is demonstrated through a mock case study. Show less

Provides an authoritative, detailed and clear explanation of QbD principles and its applications/implications for the development and commercialization of biopharmaceutical drug product for the biotech and pharmaceutical industries
Covers dosage forms, liquid and lyophilized drug products
Demonstrates how QbD is used for formulation development ranging from screening of formulations, to developability assessment, to development of lyophilized and liquid formats

Application of multivariate modeling and monitoring to CIP and SIP processes

PAT in biopharmaceutical manufacturing

As with all of pharmaceutical production, the regulatory environment for the production of therapeutics has been changing as a direct result of the US FDA-initiated Quality by Design (QbD) guidelines and corresponding activities of the International Committee for Harmonization (ICH). Given the rapid growth in the biopharmaceutical area and the complexity of the molecules, the optimum use of which are still being developed, there is a great need for flexible and proactive teams in order to… Show more

As with all of pharmaceutical production, the regulatory environment for the production of therapeutics has been changing as a direct result of the US FDA-initiated Quality by Design (QbD) guidelines and corresponding activities of the International Committee for Harmonization (ICH). Given the rapid growth in the biopharmaceutical area and the complexity of the molecules, the optimum use of which are still being developed, there is a great need for flexible and proactive teams in order to satisfy the regulatory requirements during process development.


Process Analytical Technologies (PAT) applied in biopharmaceutical process development and manufacturing have received significant attention in recent years as an enabler to the QbD paradigm. PAT Applied in Biopharmaceutical Process Development and Manufacturing covers technological advances in measurement sciences, data acquisition, monitoring, and control. Technical leaders present real-life case studies in areas including measuring and monitoring raw materials, cell culture, purification, and cleaning and lyophilization processes via advanced PAT. They also explore how data are collected and analyzed using advanced analytical techniques such as multivariate data analysis, monitoring, and control in real-time.


Invaluable for experienced practitioners in PAT in biopharmaceuticals, this book is an excellent reference guide for regulatory officials and a vital training aid for students who need to learn the state-of-the-art in this interdisciplinary and exciting area. Show less

Abstract
Biopharmaceutical manufacturing processes are inherently complex due to their nonlinear bioprocess dynamics, variability in batch operations and manufacturing schedule, raw materials involved, and automatic process control. A typical processed lot generates large amounts of data that need to be analyzed and interpreted for process troubleshooting and continuous improvement purposes in addition to product release. Multivariate batch process modeling, monitoring and control approaches… Show more

Abstract
Biopharmaceutical manufacturing processes are inherently complex due to their nonlinear bioprocess dynamics, variability in batch operations and manufacturing schedule, raw materials involved, and automatic process control. A typical processed lot generates large amounts of data that need to be analyzed and interpreted for process troubleshooting and continuous improvement purposes in addition to product release. Multivariate batch process modeling, monitoring and control approaches in real-time are elaborated by providing industrial examples from the commercial manufacturing processes. Examples and opportunities in cell culture (e.g., bioreactor applications) and purification (e.g., large-scale chromatography) operations are summarized. Impact of process analytical technologies (PAT), soft-sensor development, first principles modeling applications and commercial-scale examples are presented. Show less

The concepts, applications, and practical issues of Quality by Design

Quality by Design (QbD) is a new framework currently being implemented by the FDA, as well as EU and Japanese regulatory agencies, to ensure better understanding of the process so as to yield a consistent and high-quality pharmaceutical product. QbD breaks from past approaches in assuming that drug quality cannot be tested into products; rather, it must be built into every step of the product creation process… Show more

The concepts, applications, and practical issues of Quality by Design

Quality by Design (QbD) is a new framework currently being implemented by the FDA, as well as EU and Japanese regulatory agencies, to ensure better understanding of the process so as to yield a consistent and high-quality pharmaceutical product. QbD breaks from past approaches in assuming that drug quality cannot be tested into products; rather, it must be built into every step of the product creation process.

Quality by Design: Perspectives and Case Studies presents the first systematic approach to QbD in the biotech industry. A comprehensive resource, it combines an in-depth explanation of basic concepts with real-life case studies that illustrate the practical aspects of QbD implementation.

In this single source, leading authorities from the biotechnology industry and the FDA discuss such topics as:

The understanding and development of the product's critical quality attributes (CQA)

Development of the design space for a manufacturing process

How to employ QbD to design a formulation process

Raw material analysis and control strategy for QbD

Process Analytical Technology (PAT) and how it relates to QbD

Relevant PAT tools and applications for the pharmaceutical industry

The uses of risk assessment and management in QbD

Filing QbD information in regulatory documents

The application of multivariate data analysis (MVDA) to QbD
Filled with vivid case studies that illustrate QbD at work in companies today, Quality by Design is a core reference for scientists in the biopharmaceutical industry, regulatory agencies, and students. Show less

Slight changes in raw material properties or operating conditions during critical periods of operation of batch and semi-batch polymerization reactors may have a strong influence on reaction mechanism and impact final product quality. Online process monitoring, fault detection, fault diagnosis, and product quality prediction in real-time ensure safe reactor operation and warn operators about excursions from normal operation that may lead to deterioration in product properties. Multivariate… Show more

Slight changes in raw material properties or operating conditions during critical periods of operation of batch and semi-batch polymerization reactors may have a strong influence on reaction mechanism and impact final product quality. Online process monitoring, fault detection, fault diagnosis, and product quality prediction in real-time ensure safe reactor operation and warn operators about excursions from normal operation that may lead to deterioration in product properties. Multivariate statistical process monitoring and quality prediction using multiway principal components analysis and multiway partial least squares have been successful in detecting abnormalities in process operation and product quality. When abnormal process operation is detected, fault diagnosis tools are used to determine the source cause of the deviation. Illustrative case studies are presented via simulated polyvinyl acetate polymerization. Show less

Real-time supervision of batch operations during the progress of a batch run offers many advantages over end-of-batch quality control. Process monitoring, quality estimation, and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling and fault diagnosis. Multivariate statistical techniques such as multiway partial… Show more

Real-time supervision of batch operations during the progress of a batch run offers many advantages over end-of-batch quality control. Process monitoring, quality estimation, and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling and fault diagnosis. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide a powerful modeling, monitoring, and supervision framework. Online process monitoring techniques are developed and extended to include predictions of end-of-batch quality measurements during the progress of a batch run. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator. Show less

An integrated online multivariate statistical process monitoring (MSPM), quality prediction, and fault diagnosis framework is developed for batch processes. Batch data from I batches, with J process variables measured at K time points generate a three-way array of size I × K × J. Unfolding this three-way array into a two-way matrix of size IK × J by preserving the variable direction is advantageous for developing online MSPM methods because it does not require estimation of future portions of… Show more

An integrated online multivariate statistical process monitoring (MSPM), quality prediction, and fault diagnosis framework is developed for batch processes. Batch data from I batches, with J process variables measured at K time points generate a three-way array of size I × K × J. Unfolding this three-way array into a two-way matrix of size IK × J by preserving the variable direction is advantageous for developing online MSPM methods because it does not require estimation of future portions of new batches. Two different multiway partial least squares (MPLS) models are developed. The first model (MPLSV) is developed between the data matrix (IK × J) and the local batch time (or an indicator variable) for online MSPM. The second model (MPLSB) is developed between the rearranged data matrix in the batch direction (I × KJ) and the final quality matrix for online prediction of end-of-batch quality. The problem of discontinuity in process variable measurements due to operation switching (or moving to a different phase) that causes problems in alignment and modeling is addressed. Control limits on variable contribution plots are used to improve fault diagnosis capabilities of the MSPM framework. Case studies from a simulated fed-batch penicillin fermentation illustrate the implementation of the methodology. Show less

Illustrating techniques in model development, signal processing, data reconciliation, process monitoring, quality assurance, intelligent real-time process supervision, and fault detection and diagnosis, Batch Fermentation offers valuable simulation and control strategies for batch fermentation applications in the food, pharmaceutical, and chemical industries. The book provides approaches for determining optimal reference trajectories and operating conditions; estimating final product quality;… Show more

Illustrating techniques in model development, signal processing, data reconciliation, process monitoring, quality assurance, intelligent real-time process supervision, and fault detection and diagnosis, Batch Fermentation offers valuable simulation and control strategies for batch fermentation applications in the food, pharmaceutical, and chemical industries. The book provides approaches for determining optimal reference trajectories and operating conditions; estimating final product quality; modifying, adjusting, and enhancing batch process operations; and designing integrated real-time intelligent knowledge-based systems for process monitoring and fault diagnosis. Show less

Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide an efficient modeling and supervision framework. A new type of MPLS modeling technique that is especially suitable for online real-time process monitoring and the multivariate monitoring charts are presented. This online process monitoring technique is also… Show more

Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide an efficient modeling and supervision framework. A new type of MPLS modeling technique that is especially suitable for online real-time process monitoring and the multivariate monitoring charts are presented. This online process monitoring technique is also extended to include predictions of end-of-batch quality measurements during the progress of a batch run. Process monitoring, quality estimation and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator. Show less

Simulation software based on a detailed unstructured model for penicillin production in a fed-batch fermentor has been developed. The model extends the mechanistic model of Bajpai and Reuss by adding input variables such as pH, temperature, aeration rate, agitation power, and feed flow rate of substrate and introducing the CO2 evolution term. The simulation package was then used for monitoring and fault diagnosis of a typical penicillin fermentation process.

The monitoring of intermediate phases of production is as important as monitoring and control of the final stage. Here a framework is proposed for monitoring overall process performance at the end of each batch.

A morphologically structured model is proposed to describe penicillin production in fed-batch cultivations. The model accounts for the effects of dissolved oxygen on cell growth and penicillin production and variations in volume fractions of abiotic and biotic phases due to biomass formation. Penicillin production is considered to occur in the subapical hyphal cell compartment and to be affected by availability of glucose and oxygen. As it stands, the model provides a wide range of… Show more

A morphologically structured model is proposed to describe penicillin production in fed-batch cultivations. The model accounts for the effects of dissolved oxygen on cell growth and penicillin production and variations in volume fractions of abiotic and biotic phases due to biomass formation. Penicillin production is considered to occur in the subapical hyphal cell compartment and to be affected by availability of glucose and oxygen. As it stands, the model provides a wide range of applicability in terms of operating conditions. The model has been tested for various conditions and has given satisfactory results. A series of glucose feeding profiles have been considered to demonstrate the capabilities of the proposed model. It is concluded that the model may be valuable for the interpretation of experimental data collected specifically for metabolic flux analysis during fed-batch cultivation because the elements of measured specific production rates are determined from measurements of the concentrations of the components and their mass balances. The proposed model may be further used for developing control strategies and model order reduction algorithms. Show less