An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality.

The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data-driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black-box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black-box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.

Fed-batch performance profiles for mAb production using different intensified N - 1 seed strategies are CHO cell-line dependent.

Recent optimizations of cell culture processes have focused on the final seed scale-up step (N - 1 stage) used to inoculate the production bioreactor (N-stage bioreactor) to enable higher inoculation cell densities (2-20 × 106 cells/mL), which could shorten the production culture duration and/or increase the volumetric productivity. N - 1 seed process intensification can be achieved by either non-perfusion (enriched-batch or fed-batch) or perfusion culture to reach those higher final N - 1 viable cell densities (VCD). In this study, we evaluated how different N - 1 intensification strategies, specifically enriched-batch (EB) N - 1 versus perfusion N - 1, affect cell growth profiles and monoclonal antibody (mAb) productivity in the final N-stage production bioreactor operated in fed-batch mode. Three representative Chinese Hamster Ovary (CHO) cell lines producing different mAbs were cultured using either EB or perfusion N - 1 seeds and found that the N-stage cell growth and mAb productivities were comparable between EB N - 1 and perfusion N - 1 conditions for two of the cell lines but were very different for the third. In addition, within the two similar cell growth cell lines, differences in cell-specific productivity were observed. This suggests that the impact of the N - 1 intensification process on production was cell-line dependent. This study revealed that the N - 1 intensification strategy and the state of seeds from the different N - 1 conditions may affect the outcome of the N production stage, and thus, the choice of N - 1 intensification strategy could be a new target for future upstream optimization of mAb production.

Strategies for controlling afucosylation in monoclonal antibodies during upstream manufacturing.

Core fucosylation is a highly prevalent and significant feature of N-glycosylation in therapeutic monoclonal antibodies produced by mammalian cells where its absence (afucosylation) plays a key role in treatment safety and efficacy. Notably, even slight changes in the level of afucosylation can have a considerable impact on the antibody-dependent cell-mediated cytotoxicity. Therefore, implementing control over afucosylation levels is important in upstream manufacturing to maintain consistent quality across batches of product, since standard downstream processing does not change afucosylation. In this review, the influences and strategies to control afucosylation are presented. In particular, there is emphasis on upstream manufacturing culture parameters and media supplementation, as these offer particular advantages as control strategies over alternative approaches such as cell line engineering and chemical inhibitors. The review discusses the relationship between the afucosylation influences and the underlying cellular metabolism to promote increased process understanding. Also, briefly highlighted is the value of empirical and mechanistic models in evaluating and designing control methods for core fucosylation.

Effects of process parameters on tablet critical quality attributes in continuous direct compression: a case study of integrating data-driven statistical models and mechanistic compaction models.

Continuous manufacturing of oral-dosage drug products is increasing the need for rigorous process understanding both from a process design and control perspective. The purpose of this study is to develop a methodology that analyzes the effects of upstream process parameters on continuous tablet compaction and then correlates associated upstream variables to the final tablet attributes (e.g. relative density and hardness). The impact of three process parameters (system throughput, blender speed, and compaction force) on tablet attributes is investigated using a full factorial experimental design. As expected, the compaction force was found to be the most significant process parameter. However, importantly, throughput was discovered to have a non-negligible impact which was previously unaccounted for. This impact is proposed to be related to differing levels of powder pre-compression. An empirical model for this relationship is regressed and incorporated into a flowsheet model. The flowsheet model is then used to develop an in silico design space which is compared favorably to that built from experiments. Moreover, in the future, the in silico design space based on the validated flowsheet model can provide better manufacturing flexibility and make control strategy development simpler.

Primary concentration - The critical step in implementing the wastewater based epidemiology for the COVID-19 pandemic: A mini-review.

The recent outbreak of a novel coronavirus SARS-CoV-2 has posed a significant global public health threat and caused dramatic social and economic disruptions. A new research direction is attracting a significant amount of attention in the academic community of environmental sciences and engineering, in which rapid community-level monitoring could be achieved by applying the methodology of wastewater based epidemiology (WBE). Given the fact that the development of a mass balance on the total number of viral RNA copies in wastewater samples and the infected stool specimens is the heart of WBE, the result of the quantitative RNA detection in wastewater has to be highly sensitive, accurate, and reliable. Thus, applying effective concentration methods before the subsequent RNA extraction and RT-qPCR detection is a must-have procedure for the WBE. This review provides new insights into the primary concentration methods that have been adopted by the eighteen recently reported COVID-19 wastewater detection studies, along with a brief discussion of the mechanisms of the most commonly used virus concentration methods, including the PEG-based separation, electrostatically charged membrane filtration, and ultrafiltration. In the end, two easy and well-proven concentration strategies are recommended as below, aiming to maximize the practical significance and operational effectiveness of the SARS-CoV-2 virus concentration from wastewater samples.

Treatment train approaches for the remediation of per- and polyfluoroalkyl substances (PFAS): A critical review.

Per- and polyfluoroalkyl substances (PFAS) have recently drawn great attention due to their ubiquitous presence in aquatic environments and potential toxicity to human health and the environment. A number of recent studies have demonstrated that "passive" removal approaches, such as adsorption, filtration, and reverse osmosis or "active" degradation technologies, such as enhanced photolysis, electrochemical oxidation, and sonochemical destruction, are all able to individually conduct remedial measures for PFAS contamination at some level. However, drawbacks, specifically high energy consumption, low cost-efficiency, and extreme operating conditions, are commonly observed from these studies which significantly suppress the future for commercialization of these innovative technologies. Since 2015, a new trend of PFAS remediation has emerged that uses multiple synergetic technologies simultaneously (known as treatment train processes) to effectively achieve in-situ remediation of PFAS. This paper provides new insight of the recently reported treatment train studies selected from approximately 150 different publications with regards to the remediation of PFAS and discusses their innovative designs, remediation performances, present limits, and possible improvements. Based on a comprehensive review of the current treatment train studies, this review work proposes a new design that consists of three individual technologies, namely, nanofiltration, electrochemical anodic oxidation, and electro-Fenton degradation, to maximize economic and environmental benefits of PFAS remedial measures.

A Thermodynamic Balance Model for Liquid Film Drying Kinetics of a Tablet Film Coating and Drying Process.

A tablet film coating and drying process was assessed by an experimentally validated thermodynamic balance model. Mass conservation equations were derived for the process air and the aqueous coating solution. Thermodynamic behavior of the solution was described by evaporation at the tablet surface and penetration into the tablet. Energy balance equations including heat loss to the atmosphere were coupled to the mass conservation equation. Experimental data using the ConsiGma™ coater (GEA, Belgium) were used for both parameter estimation and model validation. The results showed the proposed model can investigate primitive outlet variables and further internal variables representing evaporation and penetration. A sensitivity analysis revealed that evaporation depended more on the input parameters while penetration hinges on the tablet properties, particularly on the tablet volume affecting the tablet porosity.

Assessment of spatial heterogeneity in continuous twin screw wet granulation process using three-compartmental population balance model.

In this study, a novel three-compartmental population balance model (PBM) for a continuous twin screw wet granulation process is developed, combining the techniques of PBM and regression process modeling. The developed model links screw configuration, screw speed, and blend throughput with granule properties to predict the granule size distribution (GSD) and volume-average granule diameter. The granulator screw barrel was divided into three compartments along barrel length: wetting compartment, mixing compartment, and steady growth compartment. Different granulation mechanisms are assumed in each compartment. The proposed model therefore considers spatial heterogeneity, improving model prediction accuracy. An industrial data set containing 14 experiments is applied for model development. Three validation experiments show that the three-compartmental PBM can accurately predict granule diameter and size distribution at randomly selected operating conditions. Sixteen combinations of aggregation and breakage kernels are investigated in predicting the experimental GSD to best judge the granulation mechanism. The three-compartmental model is compared with a one-compartmental model in predicting granule diameter at different experimental conditions to demonstrate its advantage. The influence of the screw configuration, screw speed and blend throughput on the volume-average granule diameter is analyzed based on the developed model.

Modeling and simulation of continuous powder blending applied to a continuous direct compression process.

Continuous manufacturing techniques are increasingly being adopted in the pharmaceutical industry and powder blending is a key operation for solid-dosage tablets. A modeling methodology involving axial and radial tanks-in-series flowsheet models is developed to describe the residence time distribution (RTD) and blend uniformity of a commercial powder blending system. Process data for a six-component formulation processed in a continuous direct compression line (GEA Pharma Systems) is used to test the methodology. Impulse tests were used to generate experimental RTDs which are used along with parameter estimation to determine the number of axial tanks in the flowsheet. The weighted residual from the parameter estimation was less than the χ2 value at a 95% confidence indicating a good fit between the model and measured data. In-silico impulse tests showed the tanks-in-series modeling methodology could successfully describe the RTD behavior of the blenders along with blend uniformity through the use of radial tanks. The simulation output for both impulse weight percentage and blend uniformity were within the experimentally observed variance.

Kinetic Modeling of Mammalian Cell Culture Bioprocessing: The Quest to Advance Biomanufacturing.

Kinetic modeling is the most suitable framework to describe the dynamic behavior of mammalian cell culture although its industrial application is still in its infancy. Herein, the authors reviewed mammalian bioprocess relevant kinetic models, and found that the simple unstructured-unsegregated approach utilizing empirical Monod-type kinetics based on limiting substrates and inhibitory metabolites is commonly used due to its traceability and simple formalism. Notably, the available kinetic models are typically small to moderate in size, and the development of large-scale models is severely hampered by the scarcity of kinetic data and limitations in current parameter estimation methods. The recent availability of abundant high-throughput multi-omics datasets from mammalian cell cultures have now paved the way to improve parameterization of kinetic models, and integrate regulatory, signaling, and product quality related intracellular events, as well as cellular metabolism within the modeling framework. Ultimately, the authors foresee that multi-scale modeling is the way forward in building predictive kinetic models of mammalian cell culture to advance biomanufacturing.

Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures.

Chinese hamster ovary (CHO) cells have been widely used for producing many recombinant therapeutic proteins. Constraint-based modeling, such as flux balance analysis (FBA) and metabolic flux analysis (MFA), has been developing rapidly for the quantification of intracellular metabolic flux distribution at a systematic level. Such methods would produce detailed maps of flows through metabolic networks, which contribute significantly to better understanding of metabolism in cells. Although these approaches have been extensively established in microbial systems, their application to mammalian cells is sparse. This review brings together the recent development of constraint-based models and their applications in CHO cells. The further development of constraint-based modeling approaches driven by multi-omics datasets is discussed, and a framework of potential modeling application in cell culture engineering is proposed. Improved cell culture system understanding will enable robust developments in cell line and bioprocess engineering thus accelerating consistent process quality control in biopharmaceutical manufacturing.

QTL mapping with different genetic systems for nine non-essential amino acids of cottonseeds.

Amino acid is an important nutrient resource for both human and animals. Using a set of 188 RILs population derived from an elite hybrid cross of upland cotton cultivars 'HS46' × 'MARCABUCAG8US-1-88' and their immortal F2 (IF2) with reciprocal backcrosses BC1F1 and BC2F1 (BC) populations in two environments, the QTLs located on the embryo genome and maternal plant genome for nine amino acids of cottonseed were studied across environments. The QTL Network-CL-2.0-seed software was used to analyze the QTLs and their genetic effects for nine amino acids. A total of 56 QTLs for nine amino acids were detected in both populations, with many having over 5% of phenotypic variation. Ten of the total QTLs could be simultaneously found in the IF2 and BC populations. For most QTLs, the genetic effects from embryo genome were more important than those from maternal plant genome for the performance of nine amino acids. Significant embryo additive main effects and maternal additive main effect with their environment interaction effects from many QTLs were also found in present experiment. Some QTLs with larger phenotypic variation were important for improving the amino-acid contents in cottonseeds.

Combined metabolomics and proteomics reveals hypoxia as a cause of lower productivity on scale-up to a 5000-liter CHO bioprocess.

Large-scale bioprocessing is key to the successful manufacturing of a biopharmaceutical. However, cell viability and productivity are often lower in the scale-up from laboratory to production. In this study, we analyzed CHO cells, which showed lower percent viabilities and productivity in a 5-KL production scale bioreactor compared to a 20-L bench-top scale under seemingly identical process parameters. An increase in copper concentration in the media from 0.02 µM to 0.4 µM led to a doubling of percent viability in the production scale albeit still at a lower level than the bench-top scale. Combined metabolomics and proteomics revealed the increased copper reduced the presence of reactive oxygen species (ROS) in the 5-KL scale process. The reduction in oxidative stress was supported by the increased level of glutathione peroxidase in the lower copper level condition. The excess ROS was shown to be due to hypoxia (intermittent), as evidenced by the reduction in fibronectin with increased copper. The 20-L scale showed much less hypoxia and thus less excess ROS generation, resulting in little to no impact to productivity with the increased copper in the media. The study illustrates the power of 'Omics in aiding in the understanding of biological processes in biopharmaceutical production.

QTL mapping based on different genetic systems for essential amino acid contents in cottonseeds in different environments.

Cottonseeds are rich in various essential amino acids. However, the inheritance of them at molecular level are still not defined across various genetic systems. In the present study, using a newly developed mapping model that can analyze the embryo and maternal main effects as well as QTL × environment interaction effects on quantitative quality trait loci (QTLs) in cottonseeds, a study on QTL located in the tetraploid embryo and tetraploid maternal plant genomes for essential amino acid contents in cottonseeds under different environments was carried out, using the immortal F2 (IF2) populations from a set of 188 recombinant inbred lines derived from an intraspecific hybrid cross of two upland cotton germplasms HS46 and MARKCBUCAG8US-1-88 as experimental materials. The results showed a total of 35 QTLs associated with these quality traits in cottonseeds. Nineteen QTLs were subsequently mapped on chromosome 5, 6 and 8 in sub-A genome and chromosome 15, 18, 22 and 23 in sub-D genome. Eighteen QTLs were also found having QTL × environment (QE) interaction effects. The genetic main effects from QTLs located on chromosomes in the embryo and maternal plant genomes and their QE effects in different environments were all important for these essential amino acids in cottonseeds. The results suggested that the influence of environmental factors on the expression of some QTLs located in different genetic systems should be considered when improving for these amino acids. This study can serve as the foundation for the improvement of these essential amino acids in cottonseeds.

[Determination of amino acid contents in cottonseeds using near infrared reflectance spectroscopy].

A total of 445 samples with great variability in amino acid contents were harvested for different seasons in different regions for developing calibration equations of amino acid content in cottonseeds. The spectral data of cotton kernel powder was processed using the first derivative mathematical treatment combined with SNV and de-trend, as well as modified partial least squares (MPLS) regression method. The chemometric models for 17 amino acids present in cottonseed were developed, and 12 of them were excellent for the determination of related amino acids, namely asparagic acid, threonine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, phenylalanine, lysine, histidine, and arginine, with RPDc of 3.735-7.132 and determination coefficient (r2) of 0.910-0.979 in external validation. For those 12 amino acids, their values predicted by NIRS are comparable to those obtained by the chemical method with good accuracy. The RPDc of serine, methionine, tyrosine and proline were 2.205 -2.814, and their determination coefficient (r2) were 0.800-0.830 in external validation. For those 4 amino acids, the values from NIRS are not so accurate as chemical analysis, but could be used in sample screening in cotton breeding program. While the equation for cystine was useless as its RPDc was only 1.358, which was not suitable for estimating its content in cottonseeds.