Data fusion-based assessment of raw materials in mammalian cell culture

In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non‐destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near‐infrared, Raman, 2D fluorescence, and X‐ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality. Biotechnol. Bioeng. 2012; 109: 2819–2828. © 2012 Wiley Periodicals, Inc.     

Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics

Fourier transform infrared (FT‐IR) spectroscopy combined with multivariate statistical analyses was investigated as a physicochemical tool for monitoring secreted recombinant antibody production in cultures of Chinese hamster ovary (CHO) and murine myeloma non‐secreting 0 (NS0) cell lines. Medium samples were taken during culture of CHO and NS0 cells lines, which included both antibody‐producing and non‐producing cell lines, and analyzed by FT‐IR spectroscopy. Principal components analysis (PCA) alone, and combined with discriminant function analysis (PC‐DFA), were applied to normalized FT‐IR spectroscopy datasets and showed a linear trend with respect to recombinant protein production. Loadings plots of the most significant spectral components showed a decrease in the C–O stretch from polysaccharides and an increase in the amide I band during culture, respectively, indicating a decrease in sugar concentration and an increase in protein concentration in the medium. Partial least squares regression (PLSR) analysis was used to predict antibody titers, and these regression models were able to predict antibody titers accurately with low error when compared to ELISA data. PLSR was also able to predict glucose and lactate amounts in the medium samples accurately. This work demonstrates that FT‐IR spectroscopy has great potential as a tool for monitoring cell cultures for recombinant protein production and offers a starting point for the application of spectroscopic techniques for the on‐line measurement of antibody production in industrial scale bioreactors. Biotechnol. Bioeng. 2010; 106: 432–442. © 2010 Wiley Periodicals, Inc.     

Fingerprint detection and process prediction by multivariate analysis of fed‐batch monoclonal antibody cell culture data

This work presents a sequential data analysis path, which was successfully applied to identify important patterns (fingerprints) in mammalian cell culture process data regarding process variables, time evolution and process response. The data set incorporates 116 fed‐batch cultivation experiments for the production of a Fc‐Fusion protein. Having precharacterized the evolutions of the investigated variables and manipulated parameters with univariate analysis, principal component analysis (PCA) and partial least squares regression (PLSR) are used for further investigation. The first major objective is to capture and understand the interaction structure and dynamic behavior of the process variables and the titer (process response) using different models. The second major objective is to evaluate those models regarding their capability to characterize and predict the titer production. Moreover, the effects of data unfolding, imputation of missing data, phase separation, and variable transformation on the performance of the models are evaluated. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1633–1644, 2015     

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches

Understanding variability in raw materials and their impacts on product quality is of critical importance in the biopharmaceutical manufacturing processes. For this purpose, several spectroscopic techniques have been studied for raw material characterization, providing fast and nondestructive ways to measure quality of raw materials. However, investigations of correlation between spectra of raw materials and cell culture performance have been scarce due to their complexity and uncertainty. In this study, near-infrared spectra and bioassays of multiple soy hydrolysate lots manufactured by different vendors were analyzed using chemometrics approaches in order to address variability of raw materials as well as correlation between raw material properties and corresponding cell culture performance. Principal component analysis revealed that near-infrared spectra of different soy lots contain enough physicochemical information about soy hydrolysates to allow identification of lot-to-lot variability as well as vendor-to-vendor differences. The identified compositional variability was further analyzed in order to estimate cell growth and protein production of two mammalian cell lines under the condition of varying soy dosages using partial least square regression combined with optimal variable selection. The performance of the resulting models demonstrates the potential of near-infrared spectroscopy as a robust lot selection tool for raw materials while providing a biological link between chemical composition of raw materials and cell culture performance.     

Biochemical impact of solar radiation exposure on human keratinocytes monitored by Raman spectroscopy; effects of cell culture environment

Understanding and amelioration of the effects of solar radiation exposure are critical in preventing the occurrence of skin cancer. Towards this end, many studies have been conducted in 2D cell culture models under simplified and unrealistic conditions. 3D culture models better capture the complexity of in vivo physiology, although the effects of the 3D extracellular matrix have not been well studied. Monitoring the instantaneous and resultant cellular responses to exposure, and the influence of the 3D environment, could provide an enhanced understanding of the fundamental processes of photocarcinogenesis. This work presents an analysis of the biochemical impacts of simulated solar radiation (SSR) occurring in immortalised human epithelial keratinocytes (HaCaT), in a 3D skin model, compared to 2D culture. Cell viability was monitored using the Alamar Blue colorimetric assay (AB), and the impact of the radiation exposure, at the level of the biomolecular constituents (nucleic acids and proteins), were evaluated through the combination of Raman microspectroscopy and multivariate statistical analysis. The results suggest that SSR exposure induces alterations of the conformational structure of DNA as an immediate impact, whereas changes in the protein signature are primarily seen as a subsequent response.     

Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy

A transmission near infrared (NIR) spectroscopic method has been developed for the nondestructive determination of drug content in tablets with less than 1% weight of active ingredient per weight of formulation (m/m) drug content. Tablets were manufactured with drug concentrations of ∼0.5%, 0.7%, and 1.0% (m/m) and ranging in drug content from 0.71 to 2.51 mg per tablet. Transmission NIR spectra were obtained for 110 tablets that constituted the training set for the calibration model developed with partial least squares regression. The reference method for the calibration model was a validated UV spectrophotometric method. Several data preprocessing methods were used to reduce the effect of scattering on the NIR spectra and base the calibration model on spectral changes related to the drug concentration changes. The final calibration model included the spectral range from 11 216 to 8662 cm⁻¹ the standard normal variate (SNV), and first derivative spectral pretreatments. This model was used to predict an independent set of 48 tablets with a root mean standard error of prediction (RMSEP) of 0.14 mg, and a bias of only −0.05 mg per tablet. The study showed that transmission NIR spectroscopy is a viable alternative for nondestructive testing of low drug content tablets, available for the analysis of large numbers of tablets during process development and as a tool to detect drug agglomeration and evaluate process improvement efforts. Published: March 24, 2006     

Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n = 149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 μm. The results show the NIR technique to have promise as a semi-quantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows.     

Monitoring the biochemical changes occurring to human keratinocytes exposed to solar radiation by Raman spectroscopy

Solar radiation exposure is recognised to be a significant contributor to the development of skin cancer. Monitoring the simultaneous and consecutive mechanisms of interaction could provide a greater understanding of the process of photocarcinogenesis. This work presents an analysis of the biochemical and morphological changes occurring to immortalised human epithelial keratinocyte (HaCaT) cell cultures exposed to simulated solar radiation (SSR). Cell viability was monitored with the aid of the Alamar Blue assay, morphological examination was done with haematoxylin and eosin staining (H&E) and changes to the biochemical constituents (nucleic acids and proteins) as a result of the radiation insult were demonstrated through a combination of Raman microspectroscopy and multivariate analysis of spectral patterns. The spectral results suggest that SSR induces changes to the conformational structure of DNA as an immediate result of the radiation, whereas alteration in the protein signature is mostly seen as a later response.     

近红外光谱技术预测猪肉的pH、嫩度和蒸煮损失

以冷却猪肉为研究对象,评价近红外光谱（NIR）技术用于肉类物理特性预测的可行性以及不同的光谱处理方法和建模方法对预测准确性的影响。试样取自排酸24h的同一批猪胴体的小里脊肉,采集4000—10000cm-1的光谱。经外部验证的偏最小二乘（PLS）模型在预测pH时表现出良好的相关性（Rc^2=0．88,Rp^2=0．80,SEC=0．08,SEP=0．084）,嫩度与蒸煮损失模型的相关性分别是Rc^2=0．50和0．57,R;=0．34和0．50。在各种光谱预处理方法中,平滑处理结合多元散射校正（MSC）或标准正态变量变换（SNV）的效果最好。     

An assessment of seed quality and its influence on productivity estimation in an industrial antibiotic fermentation

This study investigates the benefits of including seed quality information into data-based models for final productivity estimation in an industrial antibiotic fermentation process. Multiway principal component analysis is applied to assess the seed quality using routinely gathered plant data. Multiway partial least-squares regression is then used to estimate the final productivity using data from the main fermentation only. The issue of selecting appropriate process variables as inputs is investigated. Subsequently, seed characteristics are included into the estimation models to assess the benefits of including information from this stage for productivity estimation. It is shown that it is possible to extract seed fermentation features related to the final productivity both at pilot and production scales. It is postulated that significant influential variations are mirrored in monitored variables during the main fermentation, and therefore seed quality is implicitly accounted for.     

Novel approach for the prediction of cell densities and viability in standardized translucent cell culture biochips with near infrared spectroscopy

Near infrared spectroscopy is a rapid and nondestructive method for compositional analysis of biological material. The technology is widely used within bioreactors and possesses potential as a standardized method for quality control in miniaturized microfluidic cell culture systems. Here, we established a method for quantification of cell density and viability of adherent HepaRG cells cultured in a translucent, miniaturized cell culture biochip. The newly developed statistical models for interpretation of near infrared spectroscopy from biochips are the basis for a novel method of fast, continuous, and contact‐free analysis of cell viability and real‐time monitoring of cell growth. The technique thus paves the way for a robust and reliable high‐throughput analysis of biochip‐embedded cell cultures.     

Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms

The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr ² of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr ² of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q₁₂₀ (percentage dissolved in 120 minutes), respectively. Published: September 20, 2005 The opinions expressed in this paper are of the authors' personal views. They do not necessarily reflect the views or policies of the FDA.     

Application of near‐infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein

Control of raw materials based on an understanding of their impact on product attributes has been identified as a key aspect of developing a control strategy in the Quality by Design (QbD) paradigm. This article presents a case study involving use of a combined approach of Near‐infrared (NIR) spectroscopy and Multivariate Data Analysis (MVDA) for screening of lots of basal medium powders based on their impact on process performance and product attributes. These lots had identical composition as per the supplier and were manufactured at different scales using an identical process. The NIR/MVDA analysis, combined with further investigation at the supplier site, concluded that grouping of medium components during the milling and blending process varied with the scale of production and media type. As a result, uniformity of blending, impurity levels, chemical compatibility, and/or heat sensitivity during the milling process for batches of large‐scale media powder were deemed to be the source of variation as detected by NIR spectra. This variability in the raw materials was enough to cause unacceptably large variability in the performance of the cell culture step and impact the attributes of the resulting product. A combined NIR/MVDA approach made it possible to finger print the raw materials and distinguish between good and poor performing media lots. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Rapid and quantitative detection of the microbial spoilage in chicken meat by diffuse reflectance spectroscopy (600-1100 nm)

AIMS: To evaluate the feasibility of visible and short-wavelength near-infrared (SW-NIR) diffuse reflectance spectroscopy (600-1100 nm) to quantify the microbial loads in chicken meat and to develop a rapid methodology for monitoring the onset of spoilage. METHODS AND RESULTS: Twenty-four prepackaged fresh chicken breast muscle samples were prepared and stored at 21 degrees C for 24 h. Visible and SW-NIR was used to detect and quantify the microbial loads in chicken breast muscle at time intervals of 0, 2, 4, 6, 8, 10, 12 and 24 h. Spectra were collected in the diffuse reflectance mode (600-1100 nm). Total aerobic plate count (APC) of each sample was determined by the spread plate method at 32 degrees C for 48 h. Principal component analysis (PCA) and partial least squares (PLS) based prediction models were developed. PCA analysis showed clear segregation of samples held 8 h or longer compared with 0-h control. An optimum PLS model required eight latent variables for chicken muscle (R = 0.91, SEP = 0.48 log CFU g(-1)). CONCLUSIONS: Visible and SW-NIR combined with PCA is capable of perceiving the change of the microbial loads in chicken muscle once the APC increases slightly above 1 log cycle. Accurate quantification of the bacterial loads in chicken muscle can be calculated from the PLS-based prediction method. SIGNIFICANCE AND THE IMPACT OF THE STUDY: Visible and SW-NIR spectroscopy is a technique with a considerable potential for monitoring food safety and food spoilage. Visible and SW-NIR can acquire a metabolic snapshot and quantify the microbial loads of food samples rapidly, accurately, and noninvasively. This method would allow for more expeditious applications of quality control in food industries.     

Host cell protein quantification by fourier transform mid infrared spectroscopy (FT‐MIR)

Process development in up‐ and downstream processing requires enhanced, non‐time‐consuming, and non‐expensive monitoring techniques to track product purity, for example, the level of endotoxins, viral particles, and host cell proteins (HCPs). Currently, HCP amounts are measured by laborious and expensive HCP‐enzyme‐linked immunosorbent assay (ELISA) assays best suited for measuring HCP amounts in the low concentration regime. The measurement of higher HCP amounts using this method requires dilution steps, adding dilution errors to the measurement. In this work we evaluated the suitability of attenuated total reflection spectroscopy for HCP quantification in process development, using clarified cell culture fluid from monoclonal antibody producing Chinese hamster ovary‐cells after treatment with different polyelectrolytes for semi‐selective clarification. Forty undiluted samples were chosen for multivariate data analysis in the middle infrared range and predicted HCP‐values were in good agreement with results obtained by an ELISA‐assay, suggesting the suitability of this new method for HCP‐quantification. As this method is able to quantify HCP titers ranging from approximately at least 20,000–200,000 ng mL^?1, it is suitable especially for monitoring of process development steps with higher HCP concentrations, omitting dilution errors associated with ELISA assays. Biotechnol. Bioeng. 2013; 110: 252–259. © 2012 Wiley Periodicals, Inc.     

Interest of locally weighted regression to overcome nonlinear effects during in situ NIR monitoring of CHO cell culture parameters and antibody glycosylation

Animal cell culture processes have become the standard platform to produce therapeutic proteins such as recombinant monoclonal antibodies (mAb). Since the mAb quality could be subject to significant changes depending on manufacturing process conditions, real time monitoring and control systems are required to ensure mAb specifications mainly glycosylation and patient safety. Up to now, real time monitoring glycosylation of proteins has received scarce attention. In this article, the use of near infrared (NIR) to monitor mAb glycosylation has been reported for the first time. Whereas monitoring models are mainly constructed using linear partial least squares regressions (PLSR), evidences presented in this study indicate nonlinearity relationship between in situ captured spectra and compound concentrations, compromising the PLSR performances. A novel and simple approach was proposed to fit nonlinearity using the locally weighted regression (LWR). The LWR models were found to be more appropriate for handling information contained in spectra so that real time monitoring of cultures were accurately performed. Moreover, for the first time, the LWR calibration models allowed mAb glycosylation to be monitored, in a real time manner, by using in situ NIR spectroscopy. These results represent a further step toward developing active-control feedback of animal cell processes, particularly for ensuring properties of biologics.     

Rapid characterization and quality control of complex cell culture media solutions using raman spectroscopy and chemometrics

The use of Raman spectroscopy coupled with chemometrics for the rapid identification, characterization, and quality assessment of complex cell culture media components used for industrial mammalian cell culture was investigated. Raman spectroscopy offers significant advantages for the analysis of complex, aqueous‐based materials used in biotechnology because there is no need for sample preparation and water is a weak Raman scatterer. We demonstrate the efficacy of the method for the routine analysis of dilute aqueous solution of five different chemically defined (CD) commercial media components used in a Chinese Hamster Ovary (CHO) cell manufacturing process for recombinant proteins.The chemometric processing of the Raman spectral data is the key factor in developing robust methods. Here, we discuss the optimum methods for eliminating baseline drift, background fluctuations, and other instrumentation artifacts to generate reproducible spectral data. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were then employed in the development of a robust routine for both identification and quality evaluation of the five different media components. These methods have the potential to be extremely useful in an industrial context for “in‐house” sample handling, tracking, and quality control. Biotechnol. Bioeng. 2010;107: 290–301. © 2010 Wiley Periodicals, Inc.     

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Multi‐component, multi‐scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single‐scale and combination‐scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single‐scale and combination‐scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale‐dependencies that hindered model quality in cross‐scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross‐scale applications predicting against manufacturing scale batches. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:566–577, 2015