Advancements in CHO metabolomics: techniques, current state and evolving methodologies.

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

A novel algorithm for optimizing hydrocyclone operations in the decontamination of potentially toxic elements in soils.

We present the Three-Parameter Penalized Attributive Analysis for Upgrading (3PPAA-U) method as a tool for selecting the Best Upgrading Condition (BUC) in process engineering. Conventional approaches tend to consider only maximizing recovery (ε) and minimizing yield (γc); in contrast, the proposed 3PPAA-U introduces and seeks to maximize a third parameter, the grade (λ). This multi-parameter approach has not yet been explored in existing literature. In addition to controlling multiple parameters, the method is also superior to others as it includes inverse standard deviation weighting to avoid the distortion of results due to data dispersion. This reduces the possibility of drawing conclusions based on extreme values. Furthermore, the method can be used with a target-to-distance correction to optimize separation for multi-component feeds. To illustrate our method, we present a practical application of 3PPAA-U. Soil contaminated with potentially toxic elements (PTEs) was subject to hydrocycloning under 12 different experimental conditions. Results of these 12 experiments were compared using 3PPAA-U and conventional methods to identify the best upgrading conditions (BUC). Analysis reveals that the 3PPAA-U approach offers a simple and effective criterion for selecting BUC. Furthermore, 3PPAA-U has uses beyond soil remediation. It offers a versatile tool for optimizing operations across various processing and manufacturing environments offering a way to manage factors such as concentration, temperature, pressure, pH, Eh, grain size, and even broader environmental and economic considerations.

From biomass-derived fructose to γ-valerolactone: Process design and techno-economic assessment.

This work proposes a process design and techno-economic assessment for the production of γ-valerolactone from lignocellulosic derived fructose at industrial scale, with the aim of exploring its feasibility, identifying potential obstacles, and suggesting improvements in the context of France. First, the conceptual process design is developed, the process modelled and optimized. Second, different potential scenarios for the energy supply to the process are analyzed by means of a set of economic key performance indicators, aimed at highlighting the best potential profitability scenario for the sustainable exploitation of waste biomass in the context analyzed. The lowest Minimum Selling Price for GVL is obtained at 10 kt/y plant fueled by biomass, i.e. 1.89 €/kg, along with the highest end-of-live revenue, i.e. 113 M€. Finally, a sensitivity and uncertainties analysis, based on Monte Carlo simulations, are carried out on the results in order to test their robustness with respect to key input parameters.

Process design and techno-economic analysis of activated carbon derived from anthracite coal.

Activated carbon (AC), renowned for its versatile applications in water treatment, air purification, and industrial processes, is a critical component in environmental remediation and resource recovery strategies. This study encompasses the process modeling of AC production using anthracite coal as a precursor, involving multiple activation stages at different operating conditions, coupled with a detailed techno-economic analysis aimed at assessing the operational feasibility and financial viability of the plant. The economic analysis explores the investigation of economic feasibility by performing a detailed cashflow and sensitivity analysis to identify key parameters influencing the plant's economic performance, including raw material and energy prices, operational and process parameters. Capital and operational costs are meticulously evaluated, encompassing raw material acquisition, labor, energy consumption, and equipment investment. Financial metrics like Net Present Value (NPV), Internal Rate of Return (IRR), and payout period (POP) are employed, and the results show that AC selling price, raw material cost and plant capacity are the most influential parameters determining the plant's feasibility. The minimum AC production cost of 1.28 $/kg is obtained, corresponding to coal flow rate of 14,550 kg/h. These findings provide valuable insights for stakeholders, policymakers, and investors seeking to engage in activated carbon production from anthracite.

An Integrated Process Analysis for Producing Glycerol Carbonate from CO2 and Glycerol.

Glycerol carbonate (GC) is one of the most attractive green chemicals involved in several applications such as polymer synthesis, e. g., the production of polyurethanes and polycarbonates. This relevant chemical can be produced, in a green way, using CO2 (from carbon capture) and glycerol (a byproduct from biodiesel manufacturing). Therefore, in this work, a comprehensive analysis of the GC production process is conducted based on the following synthesis route: urea-dimethyl carbonate-GC using carbon dioxide and glycerol as the main raw materials where the synthesis pathway was efficiently integrated using Aspen Plus. A techno-economic analysis was performed in order to estimate the required capital investment and operating cost for the whole GC process, providing insights on individual capital cost requirements for the urea, dimethyl carbonate, and GC production sections. A total capital cost of $192.1 MM, and a total operating cost of $225.7 MM/y were estimated for the process. The total annualized cost was estimated as $1,558 USD/t of GC produced, competitive with current market price.

Aqueous-phase product treatment and monetization options of wet waste hydrothermal liquefaction: Comprehensive techno-economic and life-cycle GHG emission assessment unveiling research opportunities.

While wet waste hydrothermal liquefaction technology has a high biofuel yield, a significant amount of the carbon and nitrogen in the feedstock reports to the aqueous-phase product. Pretreatment of this stream before sending to a conventional wastewater plant is essential or at the very least, advisable. In this work, techno-economic and life-cycle assessments were conducted for the state-of-technology baseline and four aqueous-phase product treatment and monetization options based on experimental data. These options can cut minimum fuel selling prices by up to 13 % and life-cycle greenhouse gas emissions by up to 39 % compared to the baseline. These findings highlight the substantial influence of aqueous produce treatment strategies on the entire wet waste hydrothermal liquefaction process, demonstrating the potential for optimizing economic viability and environmental impact through further research and development of milder treatment methods and diversified by-product valorization pathways.

Implementation of mDoE-methods to a microcarrier-based expansion processes for mesenchymal stem cells.

The need for advanced therapy medicinal products (ATMPs) has gained increased attention in recent years. In this respect, a well-designed cell expansion process is needed to efficiently manufacture the required number of cells with the desired product quality. This step is challenging due to the biological complexity of the respective primary cell (e.g., mesenchymal stem cells (MSC)) and the usage of microcarrier-based expansion systems. One accelerating approach for process design is model-assisted Design of Experiments (mDoE) combining mathematical process models and statistical tools. In this study, the mDoE workflow was used for the development of an expansion processes with human immortalized mesenchymal stem cells (hMSC-TERT) and the aim of maximizing cell yield assuming only a limited amount of prior knowledge at a very early stage of development. First, suitable microcarriers for expansion in shake flasks were screened and the differentiation of the cells was proven. Second, initial experiments were performed to generate prior knowledge, which was then used to set up the mathematical model and to estimate the model parameters. Finally, the mDoE was used to determine and evaluate the design space to be performed experimentally. Overall, a cell expansion process using microcarriers in a shake flask culture was successfully implemented and a significant increase in cell yield (up to 6,2-fold) was achieved compared to literature.

Process development and characterization for integrated continuous bioprocesses-Highlights from N-mAb.

The N-mAb case study was produced by the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) to support teaching and learning for both industry and to accelerate adoption of advanced manufacturing process technologies such as integrated continuous bioprocesses (ICB) for mAbs. Similar to the A-mAb case study, N-mAb presents the evolution of an integrated control strategy, from early clinical through process validation and commercial manufacturing with a focus on elements that are unique to integrated continuous bioprocesses. This publication presents a summary of the process design and characterization chapters to allow a greater focus on the unique elements relevant to that phase of development.

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities.

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant.

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.

General Planar Ideal Flow Solutions with No Symmetry Axis.

Bulk ideal flows constitute a wide class of solutions in plasticity theory. Ideal flow solutions concern inverse problems. In particular, the solution determines part of the boundary of a region where it is valid. Bulk planar ideal flows exist in the case of (i) isotropic rigid/plastic material obeying an arbitrary pressure-independent yield criterion and its associated flow rule and (ii) the double sliding and rotation model based on the Mohr-Coulomb yield criterion. In the latter case, the intrinsic spin must vanish. Both models are perfectly plastic, and the complete equation systems are hyperbolic. All available specific solutions for both models describe flows with a symmetry axis. The present paper aims at general solutions for flows with no symmetry axis. The general structure of the solutions consists of two rigid regions connected by a plastic region. The characteristic lines between the plastic and rigid regions must be straight, which partly dictates the general structure of the characteristic nets. The solutions employ Riemann's method in regions where the characteristics of both families are curvilinear. Special solutions that do not have such regions are considered separately. In any case, the solutions are practically analytical. A numerical technique is only necessary to evaluate ordinary integrals. The solutions found determine the tool shapes that produce ideal flows. In addition, the distribution of pressure over the tool's surface is calculated, which is important for predicting the wear of tools.

Design of a Femtosecond Laser Percussion Drilling Process for Ni-Based Superalloys Based on Machine Learning and the Genetic Algorithm.

Femtosecond laser drilling is extensively used to create film-cooling holes in aero-engine turbine blade processing. Investigating and exploring the impact of laser processing parameters on achieving high-quality holes is crucial. The traditional trial-and-error approach, which relies on experiments, is time-consuming and has limited optimization capabilities for drilling holes. To address this issue, this paper proposes a process design method using machine learning and a genetic algorithm. A dataset of percussion drilling using a femtosecond laser was primarily established to train the models. An optimal method for building a prediction model was determined by comparing and analyzing different machine learning algorithms. Subsequently, the Gaussian support vector regression model and genetic algorithm were combined to optimize the taper and material removal rate within and outside the original data ranges. Ultimately, comprehensive optimization of drilling quality and efficiency was achieved relative to the original data. The proposed framework in this study offers a highly efficient and cost-effective solution for optimizing the femtosecond laser percussion drilling process.

Practical Advice on Scientific Design of Freeze-Drying Process: 2023 Update.

OBJECTIVE: The purpose of this paper is to re-visit the design of three steps in the freeze-drying process, namely freezing, primary drying, and secondary drying steps. Specifically, up-to-date recommendations for selecting freeze-drying conditions are provided based on the physical-chemical properties of formulations and engineering considerations. METHODS AND RESULTS: This paper discusses the fundamental factors to consider when selecting freezing, primary drying, and secondary drying conditions, and offers mathematical models for predicting the duration of each segment and product temperature during primary drying. Three simple heat/mass transfer primary drying (PD) models were tested, and their ability to predict product temperature and sublimation time showed good agreement. The PD models were validated based on the experimental data and utilized to tabulate the primary drying conditions for common pharmaceutical formulations, including amorphous and partially crystalline products. Examples of calculated drying cycles, including all steps, for typical amorphous and crystalline formulations are provided. CONCLUSIONS: The authors revisited advice from a seminal paper by Tang and Pikal (Pharm Res. 21(2):191-200, 2004) on selecting freeze-drying process conditions and found that the majority of recommendations are still applicable today. There have been a number of advancements, including methods to promote ice nucleation and computer modeling for all steps of freeze-drying process. The authors created a database for primary drying and provided examples of complete freeze-drying cycles design. The paper may supplement the knowledge of scientists and formulators and serve as a user-friendly tool for quickly estimating the design space.

A novel mechanistic modelling approach for microbial selection dynamics: Towards improved design and control of raceway reactors for purple bacteria.

Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.

Validation of model-based design of experiments for continuous wet granulation and drying.

This paper presents an application case of model-based design of experiments for the continuous twin-screw wet granulation and fluid-bed drying sequence. The proposed framework consists of three previously developed models. Here, we are testing the applicability of previously published unit operation models in this specific part of the production line to a new active pharmaceutical ingredient. Firstly, a T-shaped partial least squares regression model predicts d-values of granules after wet granulation with different process settings. Then, a high-resolution full granule size distribution is computed by a hybrid population balance and partial least squares regression model. Lastly, a mechanistic model of fluid-bed drying simulates drying time and energy efficiency, using the outputs of the first two models as a part of the inputs. In the application case, good operating conditions were calculated based on material and formulation properties as well as the developed process models. The framework was validated by comparing the simulation results with three experimental results. Overall, the proposed framework enables a process designer to find appropriate process settings with a less experimental workload. The framework combined with process knowledge reduced 73.2% of material consumption and 72.3% of time, especially in the early process development phase.

Exploration and evaluation of modular concepts for the design of full-scale pharmaceutical manufacturing facilities.

The design of biopharmaceutical processes is predominantly driven by the domain of experimental process design. This approach can be further improved by combining multiple domain information such as experiments, unit models, and flowsheet models. Approaches consisting of methods and flowsheet models provide the framework for exploring, analyzing, and ultimately evaluating the combinatorial space of all possible designs within the molecule-to-manufacturing value chain. In recent years, modular process designs are of interest in the pharmaceutical industry because of the shift toward multiproduct, mutiprocess processes. Therefore, a systematic approach for how to evaluate the utilization of the modular plug-n-play concept provides metrics that can propel modular design from a viable design alternative to the selected alternative for full-scale manufacturing. The objective of this paper is to present such an in silico approach for the evaluation of modular designs. The approach is presented as a systematic method and then, is exemplified through the manufacture of an active pharmaceutical ingredient (API). The application of the method shows how to transition from a typical design-for-purpose design alternative to a modular design through the utilization of data, modeling, simulation, and uncertainty/sensitivity analyses for quantification of various selection metrics such as process robustness and flexibility.

Improved process design for monoclonal antibody charge variants separation with multicolumn counter-current solvent gradient purification.

The multicolumn counter-current solvent gradient purification (MCSGP) method has proven effective in addressing the issue of elution profile overlap for difficult-to-separate proteins, leading to improved purity and recovery. However, during the MCSGP process, the flow rate and proportion of loaded proteins undergo changes, causing a significant discrepancy between the elution profiles of batch process design and the actual MCSGP process. This mismatch negatively impacts the purity and recovery of the target protein. To address this challenge, an improved process design (reDesign) was proposed with the first-run MCSGP to mimic the actual continuous process and obtain elution profiles that closely resemble the real ones. The reDesign was demonstrated with both a model protein mixture and a sample of monoclonal antibody (mAb) with charge variants. For model protein mixture, the reDesign-based MCSGP process (reMCSGP) showed a remarkable improvement in recovery, increasing from 83.6% to 97.8% while maintaining a purity of more than 95%. For mAb sample, the recovery of reMCSGP improved significantly to 93.9%, surpassing the performance of normal MCSGP processes at a given purity level of more than 84%. In general, the new process design strategy developed in this work could generate a more representative elution profile that closely mirrors actual conditions in continuous processes, which enhances the separation performance of MCSGP.

Model-assisted process development, characterization and design of continuous chromatography for antibody separation.

Continuous manufacturing in monoclonal antibody production has generated increased interest due to its consistent quality, high productivity, high equipment utilization, and low cost. One of the major challenges in realizing continuous biological manufacturing lies in implementing continuous chromatography. Given the complex operation mode and various operation parameters, it is challenging to develop a continuous process. Due to the process parameters being mainly determined by the breakthrough curves and elution behaviors, chromatographic modeling has gradually been used to assist in process development and characterization. Model-assisted approaches could realize multi-parameter interaction investigation and multi-objective optimization by integrating continuous process models. These approaches could reduce time and resource consumption while achieving a comprehensive and systematic understanding of the process. This paper reviews the application of modeling tools in continuous chromatography process development, characterization and design. Model-assisted process development approaches for continuous capture and polishing steps are introduced and summarized. The challenges and potential of model-assisted process characterization are discussed, emphasizing the need for further research on the design space determination strategy and parameter robustness analysis method. Additionally, some model applications for process design were highlighted to promote the establishment of the process optimization and process simulation platform.

Design and Optimization of a Novel Hybrid Membrane-Electrochemical Hydrogen Pump Process for Recovering Helium from NRU off Gas.

Due to the low boiling point of helium, the nitrogen-rich off gas of the nitrogen rejection unit (NRU) in the liquefied natural gas (LNG) plant usually contains a small amount of CH4, approximately 1-4% He, and associated gases, such as H2. However, it is difficult to separate hydrogen and helium. Here, we propose two different integrated processes coupled with membrane separation, pressure swing adsorption (PSA), and the electrochemical hydrogen pump (EHP) based on different sequences of hydrogen gas removal. Both processes use membrane separation and PSA in order to recover and purify helium, and the EHP is used to remove hydrogen. The processes were strictly simulated using UniSim Design, and an economic assessment was conducted. The results of the economic assessment show that flowsheet #2 was more cost-effective due to the significant reduction in the capacity of the compressor and PSA because of the pre-removal of hydrogen. Additionally, using the response surface methodology (RSM), a Box-Behnken design experiment was conducted, and an accurate and reliable quadratic response surface regression model was fitted through variance analysis. The optimized operating parameters for the integrated process were determined as follows: the membrane area of M101 was 966.6 m2, the permeate pressure of M101 was 100 kPa, and the membrane area of M102 was 41.2 m2. The maximum recovery fraction was 90.66%, and the minimum cost of helium production was 2.21 $/kg. Thus, proposed flowsheet #2 has prospects and value for industrial application.

Non-covalent binding tags for batch and flow biocatalysis.

Enzyme immobilization offers considerable advantage for biocatalysis in batch and continuous flow reactions. However, many currently available immobilization methods require that the surface of the carrier is chemically modified to allow site specific interactions with their cognate enzymes, which requires specific processing steps and incurs associated costs. Two carriers (cellulose and silica) were investigated here, initially using fluorescent proteins as models to study binding, followed by assessment of industrially relevant enzyme performance (transaminases and an imine reductase/glucose oxidoreductase fusion). Two previously described binding tags, the 17 amino acid long silica-binding peptide from the Bacillus cereus CotB protein and the cellulose binding domain from the Clostridium thermocellum, were fused to a range of proteins without impairing their heterologous expression. When fused to a fluorescent protein both tags conferred high avidity specific binding with their respective carriers (low nanomolar Kd values). The CotB peptide (CotB1p) induced protein aggregation in the transaminase and imine reductase/glucose oxidoreductase fusions when incubated with the silica carrier. The Clostridium thermocellum cellulose binding domain (CBDclos) allowed immobilization of all the proteins tested, but immobilization led to loss of enzymatic activity in the transaminases (< 2-fold) and imine reductase/glucose oxidoreductase fusion (> 80%). A transaminase-CBDclos fusion was then successfully used to demonstrate the application of the binding tag in repetitive batch and a continuous-flow reactor.