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.     

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.     

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.     

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     

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.     

The importance and potential of artificial insemination in CANDES (companion animals, non-domestic, endangered species)

Artificial insemination (AI) is the least invasive assisted reproductive technology, and is therefore of great interest to breeders of companion animals, non-domestic, and endangered species (CANDES). This most fundamental artificial breeding technique circumvents physical or behavioral impediments to natural mating and provides the means for genetic exchange between populations without transfer of live animals. In addition, because oocytes grow, mature and are fertilized in vivo and embryos are not subjected to in vitro culture conditions, AI eliminates the epigenetic effects on the female gamete that are inherent in more invasive assisted reproductive technologies. Although the management of CANDES differs significantly from current livestock husbandry practices, the cattle industry is a powerful example of the potential for AI to enhance the genetic health and sustainability of animal populations. Ultimately, successful AI requires sperm of adequate quality and quantity, oocytes that have attained nuclear maturation and cytoplasmic competence, operational gamete transport systems, accurate timing, and proper placement of sperm in the female reproductive tract. Increased understanding of semen collection, evaluation and preservation techniques, estrus synchronization and superovulation, estrus and ovulation detection, and insemination instrumentation is needed for each CANDES before AI success rates will approach those of the livestock industry. Concentrated, collaborative research in these areas must be encouraged among private breeders, universities and zoological institutions to realize the full potential of AI in the management of CANDES.     

Large scale demonstration of a process analytical technology application in bioprocessing: Use of on‐line high performance liquid chromatography for making real time pooling decisions for process chromatography

Process Analytical Technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community because of the potential for continuous real time quality assurance resulting in improved operational control and compliance. In previous publications, we have demonstrated feasibility of applications involving use of high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) for real‐time pooling of process chromatography column. In this article we follow a similar approach to perform lab studies and create a model for a chromatography step of a different modality (hydrophobic interaction chromatography). It is seen that the predictions of the model compare well to actual experimental data, demonstrating the usefulness of the approach across the different modes of chromatography. Also, use of online HPLC when the step is scaled up to pilot scale (a 2294 fold scale‐up from a 3.4 mL column in the lab to a 7.8 L column in the pilot plant) and eventually to manufacturing scale (a 45930 fold scale‐up from a 3.4 mL column in the lab to a 158 L column in the manufacturing plant) is examined. Overall, the results confirm that for the application under consideration, online‐HPLC offers a feasible approach for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes. The observations demonstrate that the proposed analytical scheme allows us to meet two of the key goals that have been outlined for PAT, i.e., “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 application presented here can be extended to other modes of process chromatography and/or HPLC analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

The cornerstone of integrating circulating tumor DNA into cancer management

Recent circulating tumor DNA (ctDNA) research has demonstrated its potential as a non-invasive biomarker for cancer. However, the deployment of ctDNA assays in routine clinical practice remains challenging owing to variability in analytical approaches and the assessment of clinical significance. A well-developed, analytically valid ctDNA assay is a prerequisite for integrating ctDNA into cancer management, and an appropriate analytical technology is crucial for the development of a ctDNA assay. Other determinants including pre-analytical procedures, test validation, internal quality control (IQC), and continual proficiency testing (PT) are also important for the accuracy of ctDNA assays. In the present review, we will focus on the most widely used ctDNA detection technologies and the key quality management measures used to assure the accuracy of ctDNA assays. The aim of this review is to provide useful information for technology selection during ctDNA assay development and assure a reliable test result in clinical practice.     

Case study and application of process analytical technology (PAT) towards bioprocessing: II. Use of ultra-performance liquid chromatography (UPLC) 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. Two of the key goals that have been outlined for PAT are "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 for designing PAT applications for bioprocessing. We have previously shown that a commercially available online high performance liquid chromatography (HPLC) system can be used for analysis that can facilitate real-time decisions for column pooling based on product quality attributes (Rathore et al., 2008). In this article we test the feasibility of using a commercially available ultra- performance liquid chromatography (UPLC) system for real-time pooling of process chromatography columns. It is demonstrated that the UPLC system offers a feasible approach and meets the requirements of a PAT application. While the application presented here is of a reversed phase assay, the approach and the hardware can be easily applied to other modes of liquid chromatography.     

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.     

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.

     

Turning quantity into quality: novel quality assurance strategies for data produced by high-throughput genomics technologies

The pharmaceutical industry is facing the challenge of managing the exponential increase in volume, diversity and complexity of data generated by high-throughput technologies such as genome sequencing, gene-expression profiling, protein-expression profiling, metabolic profiling and high-throughput screening. These novel ‘genomics’ technologies are expected to reshape the approach of life science companies to research. Unfortunately, in many cases genomics technologies have been used uncritically, and some preliminary results have been disappointing. The lack of standardized data validation and quality assurance processes is recognized as one of the major hurdles for successfully implementing genomics technologies. This is particularly important for industrialized drug discovery processes, because more and more key conclusions and far-reaching decisions in the pharmaceutical industry are based on data that is generated automatically. Therefore, automated, specialized quality-control systems that can spot erroneous data that might obscure important biological effects are needed urgently. In this article, special emphasis is placed on DNA microarray technologies, a key genomics technology that suffers from severe problems with data quality. A generic, automatable data-quality-assurance workflow is discussed that will ultimately improve the quality of the drug candidates and, at the same time, reduce overall drug-development costs.     

Towards robust cell culture processes — Unraveling the impact of media preparation by spectroscopic online monitoring

Biopharmaceutical manufacturing processes can be affected by variability in cell culture media, e.g. caused by raw material impurities. Although efforts have been made in industry and academia to characterize cell culture media and raw materials with advanced analytics, the process of industrial cell culture media preparation itself has not been reported so far. Within this publication, we first compare mid‐infrared and two‐dimensional fluorescence spectroscopy with respect to their suitability as online monitoring tools during cell culture media preparation, followed by a thorough assessment of the impact of preparation parameters on media quality. Through the application of spectroscopic methods, we can show that media variability and its corresponding root cause can be detected online during the preparation process. This methodology is a powerful tool to avoid batch failure and is a valuable technology for media troubleshooting activities. Moreover, in a design of experiments approach, including additional liquid chromatography–mass spectrometry analytics, it is shown that variable preparation parameters such as temperature, power input and preparation time can have a strong impact on the physico‐chemical composition of the media. The effect on cell culture process performance and product quality in subsequent fed‐batch processes was also investigated. The presented results reveal the need for online spectroscopic methods during the preparation process and show that media variability can already be introduced by variation in media preparation parameters, with a potential impact on scale‐up to a commercial manufacturing process.     

Multivariate data analysis of growth medium trends affecting antibody glycosylation

Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data-generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in-house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N-glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in-process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.     

Factors affecting commercial application of embryo technologies in New Zealand: a modelling approach

New reproductive technologies include sexed sperm and embryo-based technologies. The technology of sperm sexing, for various reasons, is not available in New Zealand and its use has not been modelled. Embryo technologies are however already in use on a limited scale and various scenarios for their use in both the dairy and beef industries in New Zealand have been modelled. This review briefly discusses the various technologies available and some of their potential strengths and weaknesses. In the dairy industry, modelling has been used to simulate the production of breeding bulls for large breeding companies and the production of replacement heifers in dairy herds. For the beef industry, similar modelling has been carried out to determine the opportunities for more efficient beef production.All the models confirmed that at current levels of performance, embryo-based reproductive technologies are usually not profitable in New Zealand except in niche market situations where the returns from the resulting offspring are significantly greater than can be obtained from natural mating or artificial insemination (AI) reproduction systems. This is confirmed by the low uptake of these technologies in this country to date. Even if performance lifts to levels similar to AI, profitability is likely to occur only if the costs of pregnancies to embryo-based reproductive technologies can occur at prices less than two to four times greater than AI or natural mating. This break-even requirement depends on the returns that can be achieved and the advantages that can be captured by the technology over and above those available from AI or natural mating. Two new uses for reproductive technologies in dairy cattle could be the proliferation of novel or rare genotypes from gene discovery programs and improving the female reproductive rate for optimal marker assisted selection. In both these uses the technology is not at present competing with AI or natural mating. The challenge exists therefore for the biological scientists to satisfy these requirements, coupled with the ethical and human factors involved in the introduction of any new technology.Potential end users of the technologies have been surveyed. They are quite positive about the technologies provided they can use them profitably and are keen to obtain more information about them.     

Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg

Abstract

Artificial neural networking (ANN) seems to be a promising soft sensor for implementing current approaches of quality by design (QbD) and process analytical technologies (PAT) in the biopharmaceutical industry. In this study, we aimed to implement best-fitted ANN architecture for online prediction of the biomass amount of recombinant Pichia pastoris (P. pastoris) – expressing intracellular hepatitis B surface antigen (HBsAg) – during the fed-batch fermentation process using methanol as a sole carbon source. For this purpose, at the induction phase of methanol fed-batch fermentation, carbon evolution rate (CER), dissolved oxygen (DO), and methanol feed rate were selected as input vectors and total wet cell weight (WCW) was considered as output vector for the ANN. The obtained results indicated that after training recurrent ANN with data sets of four fed-batch runs, this toolbox could predict the WCW of the next fed-batch fermentation process at each specified time point with high accuracy. The R-squared and root-mean-square error between actual and predicted values were found to be 0.9985 and 13.73, respectively. This verified toolbox could have major importance in the biopharmaceutical industry since recombinant P. pastoris is widely used for the large-scale production of HBsAg.     

Helias A. Udo De Haes: A Practical Scientist

Goal, Scope and Background The importance of the social dimension of sustainable development increased significantly during the last decade of the twentieth century. Industry has subsequently experienced a shift in stakeholder pressures from environmental to social-related concerns, where new developments in the form of projects and technologies are undertaken. However, the measurement of social impacts and the calculation of suitable indicators are less well developed compared to environmental indicators in order to assess the potential liabilities associated with undertaken projects and technologies. The aim of this paper is to propose a Social Impact Indicator (SII) calculation procedure based on a previously introduced Life Cycle Impact Assessment (LCIA) calculation procedure for environmental Resource Impact Indicators (RIIs), and to demonstrate the practicability of the SII procedure in the context of the process industry in South Africa. Methods A framework of social sustainability criteria has been introduced for the South African process industry. The social sub-criteria of the framework are further analyzed, based on project and technology management expertise in the South African process industry, to determine whether the criteria should be addressed at project or technology management level or whether they should rather form part of an overall corporate governance policy for new projects and technologies. Furthermore, the proposed indicators for criteria that are considered appropriate for project or technology evaluation purposes are constrained by the type of information that is available, i.e. the calculation methodology relies on the availability of regional or national social information where the project will be implemented, as well as the availability of project- or technology-specific social information during the various phases of the project or technology development life cycle. Case studies in the process industry and statistical information for South Africa are subsequently used to establish information availability for the SII calculation procedure, demonstrate the SII method together with the RII method, and determine the practical use of the SII method. Results and Conclusion The case studies establish that social footprint information as well as project- and technology social data are not readily available in the South African process industry. Consequently, the number of mid-point categories that can be evaluated are minimal, which results in an impaired social picture when compared to the environmental dimension. It is concluded that a quantitative social impact assessment method cannot be applied for project and technology life cycle management purposes in industry at present. Recommendation and Perspective Following the outcomes of the case studies in the South African process industry, it is recommended that checklists and guidelines be used during project and technology life cycle management practices. Similar to the environmental dimension, it is envisaged that such checklists and guidelines would improve the availability of quantitative data in time, and would therefore make the SII procedure more practical in the future.     

Determination of protein concentration in downstream biomanufacturing processes by in-line index of refraction

Protein concentration determination is a necessary in-process control for the downstream operations within biomanufacturing. As production transitions from batch mode to an integrated continuous bioprocess paradigm, there is a growing need to move protein concentration quantitation from off-line to in-line analysis. One solution to fulfill this process analytical technology need is an in-line index of refraction (IoR) sensor to measure protein concentration in real time. Here the performance of an IoR sensor is evaluated through a series of experiments to assess linear response, buffer matrix effects, dynamic range, sensor-to-sensor variability, and the limits of detection and quantitation. The performance of the sensor was also tested in two bioprocessing scenarios, ultrafiltration and capture chromatography. The implementation of this in-line IoR sensor for real-time protein concentration analysis and monitoring has the potential to improve continuous bioprocess manufacturing.     

Process analytical technology implementation for protein refolding: GCSF as a case study

Process analytical technology is gaining interest in the biopharmaceutical industry as a means to enable consistency in processing and thereby in product quality via process control. Protein refolding is known to be significantly impacted by critical process parameters and feed material attributes including composition and pH of the solubilisation and refolding buffers. Hence, to achieve robust process control and product quality, these attributes and parameters need to be monitored. This paper presents an approach towards statistical process control and monitoring of protein refolding, from buffer preparation to refold quenching, during manufacturing of therapeutic proteins from Escherichia coli based systems. The proposed approach utilises measurements of online redox potential, temperature, and pH for development of a statistical model. The model has then been integrated with LabView to permit real-time monitoring of the refolding process. The proposed system has been demonstrated to successfully identify process deviations and thereby enable process control for manufacturing product of consistent quality.     

Implementing artificial insemination as an effective tool for ex situ conservation of endangered avian species

Approximately 503 of the known species of birds are classified as ‘endangered’ or ‘critical’. Captive propagation programs have proven useful in maintaining genetic diversity and restoring wild populations of certain species, including the Peregrine falcon, California condor and Whooping crane. Artificial insemination (AI) has the potential of solving problems inherent to reproductive management of small, closed populations of endangered birds, including dealing with demographic instability, physical and behavioral disabilities, sexual incompatibility, lack of synchrony, and need to maintain gene diversity. In this review, we address the necessary methods and factors that allow AI to be applied effectively to manage rare bird populations. It is clear that semen availability and quality are the greatest limiting factors to implementing consistently successful AI for birds. Behavioral sensitivity to animal handling and the ability to minimize stress in individual birds also are keys to success. Multiple, deep vaginal inseminations can improve fertility, particularly when semen quality is marginal. Laparoscopic methods of semen transfer also have produced fertile eggs. All of these practices leading to successful AI remain dependent on having adequate basic knowledge on female reproductive status, copulatory behavior, endocrine profiles and duration of fertility, especially as related to oviposition. The overall greatest challenge and highest priority is defining these normative traits, which are highly species-specific.