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Bioprocess biomarker identification and diagnosis for industrial mAb production based on metabolic profiling and multivariate data analysis

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BIOPROCESS AND BIOSYSTEMS ENGINEERING 2025年第5期48卷 771-783页

作者： Shi, Yingting Wan, Yuxiang Yang, Jiayu Lu, Yuting Xie, Xinyuan Pan, Jianyang Wang, Haibin Qu, Haibin Zhejiang Univ Pharmaceut Informat Inst Coll Pharmaceut Sci Hangzhou 310058 Peoples R China BioRay Pharmaceut Co Ltd Taizhou 318000 Peoples R China

Monoclonal antibody (mAb) production is a complex bioprocess influenced by various cellular and metabolic factors. Understanding these interactions is critical for optimizing manufacturing and improving yields. In this study, we proposed a diagnostic and identification strategy using quantitative proton nuclear magnetic resonance (1H qNMR) technology-based pharmaceutical process-omics to analyze bioprocess variability and unveil significant metabolites affecting cell growth and yield during industrial mAb manufacturing. First, batch level model (BLM) and orthogonal partial least squares-discriminant analysis (OPLS-DA) identified glucose and lactate as primary contributors to culture run variability. Maintaining an optimal glucose set point was crucial for high-yield runs. Second, a partial least squares (PLS) regression model was established, which revealed viable cell density (VCD), along with glutamine, maltose, tyrosine, citrate, methionine, and lactate, as critical variables impacting mAb yield. Finally, hierarchical clustering analysis (HCA) highlighted one-carbon metabolism metabolites, such as choline, pyroglutamate, and formate, as closely associated with VCD. These findings provide a foundation for future bioprocess optimization through cell line engineering and media formulation adjustments, ultimately enhancing mAb production efficiency.

关键词： Industrial mAb production Bioprocess variability multivariate data analysis Biomarker identification

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A robust scale-down model development and process characterization for monoclonal antibody biomanufacturing using multivariate data analysis

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JOURNAL OF BIOTECHNOLOGY 2025年 401卷 11-20页

作者： Han, Sung-Hyuk Park, Seo-Young Cha, Hyun-Myoung Lee, Kwang-Bae Lim, Jin-Hyuk Lee, Dong-Yup GC Biopharm Upstream Proc Engn Mfg Sci & Technol 93 Ihyun Ro30Beon Gil Yongin 16924 Gyeonggi Do South Korea Sungkyunkwan Univ Sch Chem Engn 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea

Quality by Design (QbD) principles are extensively applied in biopharmaceutical manufacturing processes to ensure the consistent production of high-quality biotherapeutic products through achieving a deeper understanding of critical process parameters (CPPs), critical quality attributes (CQAs), and their interrelationships as well as establishing appropriate process control strategies. To do so, herein, we involve utilizing advanced multivariate data analysis (MVDA) in the context of scale-down model (SDM) development and validation as an ingenious approach for enhancing process efficiency and achieving greater regulatory compliance in the biomanufacturing of biologics. First, MVDA was applied to develop and evaluate several SDMs under various production conditions, including changes in scale-dependent parameters. This allowed the establishment of a practical SDM that closely approximated the process performance of manufacturing-scale batches. Furthermore, this approach enabled the identification not only of potential CPPs but also specific performance attributes such as ammonia, that had a significant impact on the CQAs. Moreover, it was deduced that the N-1 seed culture represents a critical process step influencing both quality and performance attributes in the upstream process from these approaches. This deduction was subsequently confirmed through experimental validation. Our findings offer valuable insights into streamlining the development of upstream biologics, particularly in terms of process characterization, thereby suggesting strategies for time and cost savings.

关键词： Chinese hamster ovary cell cultures Quality by design Scale-down model multivariate data analysis Process characterization

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Profiling of Australian Stingless Bee Honey Using multivariate data analysis of High-Performance Thin-Layer Chromatography Fingerprints

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CHEMOSENSORS 2025年第2期13卷 30-30页

作者： dos Santos, Mariana Mello Jacobs, Christina Vinsen, Kevin Islam, Md Khairul Sostaric, Tomislav Lim, Lee Yong Locher, Cornelia Univ Western Australia Ctr Optimisat Med Sch Allied Hlth Div Pharm Crawley WA 6009 Australia Univ Western Australia Int Ctr Radio Astron Res Crawley WA 6009 Australia Univ Western Australia Inst Paediat Perioperat Excellence Crawley WA 6009 Australia

The complex chemical composition of honey presents significant challenges for its analysis with variations influenced by factors such as botanical source, geographical location, bee species, harvest time, and storage conditions. This study aimed to employ high-performance thin-layer chromatography (HPTLC) fingerprinting, coupled with multivariate data analysis, to characterise the chemical profiles of Australian stingless bee honey samples from two distinct bee species, Tetragonula carbonaria and Tetragonula hockingsi. Using a mobile phase composed of toluene:ethyl acetate:formic acid (6:5:1) and two derivatisation reagents, vanillin-sulfuric acid and natural product reagent/PEG, HPTLC fingerprints were developed to reveal characteristic patterns within the samples. multivariate data analysis was employed to explore the similarities in the fingerprints and identify underlying patterns. The results demonstrated that the chemical profiles were more closely related to harvest time rather than bee species, as samples collected within the same month clustered together. The quality of the clustering results was assessed using silhouette scores. The study highlights the value of combining HPTLC fingerprinting with multivariate data analysis to produce valuable data that can aid in blending strategies and the creation of reference standards for future quality control analyses.

关键词： high-performance thin-layer chromatography (HPTLC) stingless bee honey Australia multivariate data analysis fingerprint chemometrics quality control

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multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

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JOURNAL OF SOLID STATE CHEMISTRY 2008年第5期181卷 1195-1203页

作者： Imamura, N. Mizoguchi, T. Yamauchi, H. Karppinen, M. Aalto Univ Dept Chem Inorgan Chem Lab FI-02015 Helsinki Finland Tokyo Inst Technol Mat & Struct Lab Yokohama Kanagawa 2268503 Japan

Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in anti ferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO(3). The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. (C) 2008 Elsevier Inc. All rights reserved.

关键词： multivariate data analysis chemometrics manganese perovskites magnetic properties structure-property relations

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multivariate data analysis of topographically modified α-melanotropin analogues using auto and cross auto covariances (ACC)

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QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS 2000年第3期19卷 264-269页

作者： Nyström, Å Andersson, PM Lundstedt, T Melacure Therapeut SE-75183 Uppsala Sweden Umetr AB SE-90719 Umea Sweden Umea Univ SE-90187 Umea Sweden

A new variation of auto and cross auto covariance calculations (ACC) was introduced to describe interactions occurring in circular amino acid sequences. A multivariate data analysis was made on ACC variables and responses from biological assays of alpha-melanotropin analogues. It was then revealed that circular alpha-melanotropin analogues were superior to linear in prolonging skin darkening. For one of the best performing peptides, the prolonged activity was connected to three interactions that all involved the stereochemistry of one amino acid;the methylated (6)Trp.

关键词： ACC multivariate data analysis alpha-melanotropin

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multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells

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BIOTECHNOLOGY AND BIOENGINEERING 2024年第7期121卷 2175-2192页

作者： Xu, Xingge Farnos, Omar Paes, Barbara C. M. F. Nesdoly, Sean Kamen, Amine A. McGill Univ Dept Bioengn 3480 Univ StMcConnell Engn BldgRoom 363 Montreal PQ H3A 0E9 Canada

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, em

关键词： adenoviral vectors production biphasic bioprocessing data fusion multisensor monitoring multivariate data analysis process analytical technology

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multivariate data analysis applied in alkali-based pretreatment of corn stover

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RESOURCES CONSERVATION AND RECYCLING 2017年 122卷 307-318页

作者： Li, Bin Ding, Li Xu, Huanfei Mu, Xindong Wang, Haisong Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Shandong Peoples R China Chinese Acad Sci Qingdao Inst Bioenergy & Bioproc Technol CAS Key Lab Biobased Mat Qingdao 266101 Shandong Peoples R China

In this paper, variables of Pulp Refining Instrument (PFI) refining assisted alkaline pretreatment and hydrolysis saccharification of corn stover were analyzed. The process parameters were characterized by multivariate data analysis methods including Principle Component analysis and Partial Least Square (PCA and PLS) to investigate the specific relationships of primary variables in alkali-based pretreatment of biomass. In this paper, pretreatment system was multivariate and the variables were inter-related to each other. Total alkaline charge and removal rate of lignin had greatest impact on the pretreatment and enzymatic hydrolysis. (C) 2016 Published by Elsevier B.V.

关键词： multivariate data analysis Lignocellulosic biomass Alkali-based pretreatment Enzymatic saccharification

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multivariate data analysis in electroanalytical chemistry

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ELECTROanalysis 2002年第22期14卷 1533-1542页

作者： Richards, E Bessant, C Saini, S Cranfield Univ Cranfield Ctr Analyt Sci Silsoe MK45 4DT Beds England

data analysis is becoming an increasingly important aspect of electroanalytical chemistry, as voltammetric techniques and electrode arrays become ever more popular as diagnostic tools. Modern data analysis techniques promise to help us make full use of the large amounts of data collected, allowing electroanalytical chemists to get more out of their existing instruments, and paving the way for new measurement approaches. Ibis article provides an overview of the most widely used multivariate techniques in electroanalysis, citing specific examples of how they have been applied, and looking at their relative merits. As in other areas of analytical science, no single technique is applicable to all applications and the running of controls and appreciation of the applications and limitations of each technique is essential.

关键词： multivariate data analysis calibration classification principal components neural networks genetic algorithms

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multivariate data analysis of UV spectra in monitoring elution and determining endpoint of chromatography using polyamide column

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JOURNAL OF SEPARATION SCIENCE 2013年第7期36卷 1231-1237页

作者： Yan, Binjun Qu, Haibin Zhejiang Univ Pharmaceut Informat Inst Hangzhou 310058 Zhejiang Peoples R China

Chromatography using polyamide column is one of the most critical purification operations that affect the safety and efficacy of traditional Chinese medicine (TCM) products. To ensure successful operation and reduce time and solvent consumption, UV spectroscopy combined with multivariate data analysis as an online analytical tool was developed for monitoring the polyamide column chromatography of the TCM Danshen. The process trajectories were established by principal component analysis of the UV spectra and used to determine the endpoint of the washing stage and investigate the impacts of the process conditions. The online analysis method developed determined the concentration of salvianolic acid B (an important compound in Danshen) in the effluent rapidly and precisely with a coefficient of determination of 0.9963 and helped to collect salvianolic acid B quantitatively for determining the endpoint of elution. The methodology proposed is an effective approach applicable in guiding successful operations in the chromatographic separation.

关键词： multivariate data analysis Polyamide column chromatography Traditional Chinese medicine UV spectroscopy

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multivariate data analysis applied in Hot-Mix asphalt plants

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RESOURCES CONSERVATION AND RECYCLING 2013年 73卷 1-10页

作者： Paranhos, Regis Sebben Petter, Carlos Otavio [a] Federal University of Pampa Brazil [b] Federal University of Rio Grande do Sul State Brazil

A multivariate data analysis was used with environmental data obtained from Hot-Mix asphalt (HMA) plants. The main objectives of the study were to describe and interpret the real "phenomena" that exist on HMA plants;also, to search for the best mixing conditions, i.e. production with less energy consumption and less emissions. Another important objective was to identify means to allow HMA plants to be designed and operated in better ways. Along the study, an approach of the manufacturing process is presented, allowing for an overall comprehension of the plant's operation. There is not only one parameter to be controlled and checked when HMA are being produced. In this context, a multivariate data analysis was carried out, and the main control parameters considered for prediction and regression analysis were Hot-Mix asphalt formula, fuel type, burner flow rate, fuel consumption, negative pressure of the drum, air temperature, moisture of aggregates, asphalt temperature and rate of production. "Global warming emissions", "organic emissions", and "energy consumption" were chosen as models of regression. Globally, plants powered by natural gas emit half the amount of CO2, nine times less NOx and eighteen times less CO than plants with fuel oil. Considering NOx and CO2 emissions, small plants are preferable over big plants. For organic emissions, if the asphalt temperature increases, TOC emissions are increased;however, if the rate of production increases, TOC emissions are decreased. Previously supposed as an important parameter, moisture of aggregates had no great importance in the statistical analysis. Likewise, the formula of HMA produced also had no great importance, despite many types of formulas studied. The main conclusions in regards to the means with which Hot-Mix asphalt plants can be designed and operated in better ways were: the manufacturing of hyper-mobile and small HMA plants as opposed to stationary large plants, and the use of burners with natural gas i

关键词： Hot-Mix asphalt Drum Mixer process multivariate data analysis Emissions

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