PAT Initiative Research Articles

Monitoring yeast fermentations by nonlinear infrared technology and chemometrics-understanding process correlations and indirect predictions.

Fermentation processes are still compromised by a lack of monitoring strategies providing integrated process data online, ensuring process understanding, control, and thus, optimal reactor efficiency. The crucial demand for online monitoring strategies, not only encouraged by the PAT initiative but also motivated by modern paradigms such as circular economy and sustainability, has driven research and industry to provide "next-generation process technology": in other words, technology tailored toward industrial needs. Mid-infrared (MIR) spectroscopy as such is superior to near-infrared (NIR) spectroscopy since it provides significantly enhanced selectivity. However, due to high costs and a lack of instrumental robustness, MIR spectroscopy is outcompeted by NIR when it comes to industrial application. The lack of chemometric expertise, model understanding, and practical guidance might add to the slow acceptance of industrial MIR application. This work demonstrates the use of novel MIR, so-called non-linear infrared (NLIR) technology and the importance of model understanding, exemplarily investigated on a lab-scale yeast fermentation process. The six analytes glucose, ethanol, glycerol, acetate, ammonium, and phosphate were modeled by partial least squares (PLS) based on spectral data, demonstrating the potential of the novel technology facilitating online data acquisition and the necessity of investigating indirect predictions. KEY POINTS: • NLIR spectra were acquired online during a yeast fermentation process • PLS models were constructed for six components based on uncorrelated samples • Glucose, ethanol, ammonium, and phosphates were modeled with errors of less than 15% • Acetate and glycerol were shown to rely on indirect predictions.

Facilitating smart HACCP strategies with Process Analytical Technology

The critical feature to any food management system, be it safety or quality focused, is timely and accurate product and process data. The HACCP framework is one of a systematic preventive approach that addresses hazards as a means of prevention rather than finished product inspection. The PAT initiative is a framework which also aims to change product assurance from end-product testing to preventive control through timely measurements of critical product and process parameters. The continued emergence of sensitive yet affordable sensors capable of direct measurement of biological, chemical and physical risks associated with foods during production, processing or over their shelf-life undoubtedly will support a higher temporal and spatial profiling of foods.

MALDI-TOF mass spectrometry biotyping: At line monitoring of recombinant CHO cell cultures

Background The cultivation of mammalian cells, especially Chinese Hamster Ovary (CHO), has found widespread acceptance for the production of biopharmaceuticals. Process monitoring enforced by the PAT initiative of the FDA underwent major changes from examining the bioreactor/culture as a “black-box” towards integrated methods, helping to gain deeper process understanding by assessing the cells themselves. Previously, we presented first results of CHO batch cultures assessed in order to monitor cell stress and early apoptosis by Intact Cell MALDI-TOF Mass Spectrometry (ICM MS) biotyping, a method originally used in clinical microbiology [1]. Now, we broadened its application and addressed additional CHO cell lines (DXB11 and CHOK1), different cultivation-formats (uncontrolled Erlenmeyer flasks vs. lab scale bioreactors) and operation modes (Batch vs. Perfusion). Furthermore, we transferred the method to a compact, “low-priced” mass spectrometer (MicroFlex, Bruker Daltonik, Bremen, Germany). The experimental aim was to establish characteristic MS patterns indicative for different apoptotic states with emphasis on detection of early apoptosis ubiquitously applicable independent of CHOsubline, cultivation-format, -mode or instrumental equipment (MS).

Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance-based modeling.

The production of biopharmaceuticals requires highly sophisticated, complex cell based processes. Once a process has been developed, acceptable ranges for various control parameters are typically defined based on process characterization studies often comprising several dozens of small scale bioreactor cultivations. A lot of data is generated during these studies and usually only the information needed to define acceptable ranges is processed in more detail. Making use of the wealth of information contained in such data sets, we present here a methodology that uses performance data (such as metabolite profiles) to forecast the product quality and quantity of mammalian cell culture processes based on a toolbox of advanced statistical methods. With this performance based modeling (PBM) the final product concentration and 12 quality attributes (QAs) for two different biopharmaceutical products were predicted in daily intervals throughout the main stage process. The best forecast was achieved for product concentration in a very early phase of the process. Furthermore, some glycan isoforms were predicted with good accuracy several days before the bioreactor was harvested. Overall, PBM clearly demonstrated its capability of early process endpoint prediction by only using commonly available data, even though it was not possible to predict all QAs with the desired accuracy. Knowing the product quality prior to the harvest allows the manufacturer to take counter measures in case the forecasted quality or quantity deviates from what is expected. This would be a big step towards real-time release, an important element of the FDA's PAT initiative.

Model-based design space determination of peptide chromatographic purification processes

Operating a chemical process at fixed operating conditions often leads to suboptimal process performances. It is important in fact to be able to vary the process operating conditions depending upon possible changes in feed composition, products requirements or economics. This flexibility in the manufacturing process was facilitated by the publication of the PAT initiative from the U.S. FDA [1]. In this work, the implementation of Quality-by-design in the development of a chromatographic purification process is discussed. A procedure to determine the design space of the process using chromatographic modeling is presented. Moreover, the risk of batch failure and the critical process parameters (CPP) are assessed by modeling. The ideal cut strategy is adopted and therefore only yield and productivity are considered as critical quality attributes (CQA). The general trends in CQA variations within the design space are discussed. The effect of process disturbances is also considered. It is shown that process disturbances significantly decrease the design space and that only simultaneous and specific changes in multiple process parameters (i.e. critical process parameters (CPP) lead to batch failure. The reliability of the obtained results is proven by comparing the model predictions to suitable experimental data. The case study presented in this work proves the reliability of process development using a model-based approach.

NIR analysis for batch process of ethanol precipitation coupled with a new calibration model updating strategy

Ethanol precipitation plays a major role in the pretreatment of Flos Lonicerae Japonicae of Qingkailing injection, and is also one of the most popular purification techniques in Chinese herbal medicines. In order to monitor and have a better understanding of the ethanol precipitation process, a PLS model was built based on NIR spectroscopy and HPLC analysis of chlorogenic acid content within the framework of FDA's PAT initiative. Nevertheless, due to the complex mechanism of and the raw materials’ natural variability introduced into the ethanol precipitation process, it was unable to foresee the variations in new batches which may jeopardize the robustness of the established model. Therefore, based on the simple interval calculation (SIC) theory, a new model expansion updating strategy which could continuously expand the variation coverage of the calibration model along with the batch proceeding of ethanol precipitation process was proposed. Effects of model updating were validated by an individual batch with 60 samples. After two times of updating, the root mean squared error of prediction (RMSEP) decreased from 0.268mgmL−1 to 0.199mgmL−1, while the insiders in the object status plot (OSP) increased from 44 to 58, demonstrating the good performance of the proposed approach.

Using Compartment Modeling to Investigate Mixing Behavior of a Continuous Mixer

In this paper, the development of a compartment model to simulate mixing within a continuous blender is reported. The main benefit of the method is that it can generate extensive modeling predictions in very short computational time. The model can also be used to explore the effect of sampling parameters on estimated mixing performance, a topic that has been central to pharmaceutical manufacturing for the past 15 years and that remains a central issue in the PAT initiative. However, this method requires more input than conventional particle dynamics methods. Thus, we investigate the effects of modeling parameters on mixing performance to develop general guidance needed to adapt this modeling framework to any continuous process. An experimental technique based on longitudinal sampling is used to examine the content uniformity of the blend along the continuous mixer. The model compares favorably with continuous mixing experiments, capture the effects of feeding rate variability, active product ingredient concentration, and blender processing angle, while effectively capturing and making explicit the effect of sampling parameters such as number of samples and sample size. The modeling approach provides a convenient tool for process design.

Bioprocess monitoring and computer control: Key roots of the current PAT initiative

This review article has been written for the journal, Biotechnology and Bioengineering, to commemorate the 70th birthday of Daniel I.C. Wang, who served as doctoral thesis advisor to each of the co-authors, but a decade apart. Key roots of the current PAT initiative in bioprocess monitoring and control are described, focusing on the impact of Danny Wang's research as a professor at MIT. The history of computer control and monitoring in biochemical processing has been used to identify the areas that have already benefited and those that are most likely to benefit in the future from PAT applications. Past applications have included the use of indirect estimation methods for cell density, expansion of on-line/at-line and on-line/in situ measurement techniques, and development of models and expert systems for control and optimization. Future applications are likely to encompass additional novel measurement technologies, measurements for multi-scale and disposable bioreactors, real time batch release, and more efficient data utilization to achieve process validation and continuous improvement goals. Dan Wang's substantial contributions in this arena have been one key factor in steering the PAT initiative towards realistic and attainable industrial applications.

Combining process and spectroscopic data to improve batch modeling

AbstractPharmaceutical production is at present characterized by static processes where quality is guaranteed by controlling the purity of the final product. Achieving better control throughout the process, as a means for improving product quality, is one of the objectives of the PAT initiative by the FDA. A data set consisting of 11 batches characterized by UV spectroscopy together with process data was used in this study. Design of experiments was used to introduce controlled process variation in test batches. The objective was to investigate possible advantages of MSPC using a combined data set, compared to separate models of the respective data sets. Individual models for the separate data sets show that they contain complementary information. A major advantage of combining spectroscopic and process data is that deviations that would go unnoticed using just an individual model can be detected and interpreted. All process manipulations were detected by the combined data set model. Implementation of these methods to batch processes in primary and secondary pharmaceutical production is feasible. An enhanced understanding of the process together with control tools should lead to a well‐understood process and, ultimately, real time release. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Optimization and Control of Industrial Microbial Cultivation Processes

AbstractCompared to the immense achievements in fundamental molecular biological sciences, the improvements in the fermentation and downstream processing technologies used in industry have been less spectacular over the last decade. Hence, there is a misbalance between new cellular systems and production technologies, resulting in a decreasing annual rate of approved production processes. In its PAT initiative the U.S. Food and Drug Administration identifies the potential for continuous improvement and makes concrete suggestions how this can be achieved. Here, some of these suggestions were applied to recombinant protein production with Escherichia coli and Pichia pastoris cultures. Concretely, the development of process operational procedures is discussed that allow a more tight supervision of the processes and the automatic control in cases where processes deviate from their set‐point profiles.

Current Thoughts on the Pat Initiative

Current Thoughts on the Pat Initiative