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Raman spectroscopy offers an attractive platform for real-time monitoring and control of metabolites and feeds in cell culture processes, including mammalian cell culture for biopharmaceutical production. However, specific cell culture processes may generate substantial concentrations of chemical species and byproducts with high levels of autofluorescence when excited with the standard 785 nm wavelength. Shifting excitation further toward the near-infrared allows reduction or elimination of process autofluorescence. We demonstrate such a reduction in a highly autofluorescent mammalian cell culture process. Using the Kaiser RXN2-1000 platform, which utilizes excitation at 993 nm, we developed multivariate glucose models in a cell culture process which was previously impossible using 785 nm excitation. Additionally, the glucose level in the production bioreactor was controlled entirely by Raman adaptive feeding, allowing for maintenance of glucose levels at an arbitrary set point for the duration of the culture. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1574-1580, 2018.

Machine learning-based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e-commerce and digital marketing. In this study, a kNN-based (k-nearest neighbors) similarity method is proposed for rapid biopharmaceutical process diagnosis and process performance monitoring. Our proposed application measures the spatial distance between batches, identifies the most similar historical batches, and ranks them in order of similarity. The proposed method considers the similarity in both multivariate and univariate feature spaces and measures batch deviations to a benchmarking batch. The feasibility and effectiveness of the proposed method are tested on a drug manufacturing process at Biogen.

A promising approach to rescue cardiac function after a myocardial infarction (MI) is to apply an engineered heart tissue (EHT) onto the infarcted area. After the onset of MI, a dynamic inflammatory environment develops comprising of the temporal recruitment of macrophages (Mϕs), and their interactions with the cells of the damaged myocardium. There is limited knowledge about the interactions between this inflammatory environment and the cells that could potentially be used to create an EHT, such as pluripotent stem cell derived-cardiomyocytes. In the present study, a cell-based system was used to study the in vitro interactions between lipopolysaccharide (LPS) and interferon-gamma (IFNγ)-activated Mϕs, and interleukin 4 (IL4) and interleukin 13 (IL13)-activated Mϕs and human embryonic stem cell-derived cardiomyocytes (hESC-CMs). Using a co-culture system, gene expression profiles of key markers of both the Mϕs and the hESC-CMs were obtained, as well as the protein secretion. Additionally, the effects of Mϕ polarizing cytokines on hESC-CMs with or without the presence of Mϕs were studied. Mϕs co-cultured with hESC-CMs showed no significant changes in their gene expression profile after two days in culture. hESC-CMs, however, were noted to have an overall decrease in expression of cardiac-related genes upon exposure to both Mϕ subtypes in co-culture. Gene expression of Bone morphogenetic protein-2 (BMP2), Bone morphogenetic protein-4 (BMP4) and GATA-binding protein-4 (GATA4) were also affected by Mϕ exposure and by inflammatory signals such as LPS and IFNγ. This study represents an important step towards the design of advanced in vitro testing platforms to further study the effect of Mϕs and inflammatory signals on EHTs in vitro.

The mechanisms by which macrophages control the inflammatory response, wound healing, biomaterial-interactions, and tissue regeneration appear to be related to their activation/differentiation states. Studies of macrophage behavior in vitro can be useful for elucidating their mechanisms of action, but it is not clear to what extent the source of macrophages affects their apparent behavior, potentially affecting interpretation of results. Although comparative studies of macrophage behavior with respect to cell source have been conducted, there has been no direct comparison of the three most commonly used cell sources: murine bone marrow, human monocytes from peripheral blood (PB), and the human leukemic monocytic cell line THP-1, across multiple macrophage phenotypes. In this study, we used multivariate discriminant analysis to compare the in vitro expression of genes commonly chosen to assess macrophage phenotype across all three sources of macrophages, as well as those derived from induced pluripotent stem cells (iPSCs), that were polarized towards four distinct phenotypes using the same differentiation protocols: M(LPS,IFN) (aka M1), M(IL4,IL13) (aka M2a), M(IL10) (aka M2c), and M(-) (aka M0) used as control. Several differences in gene expression trends were found among the sources of macrophages, especially between murine bone marrow-derived and human blood-derived M(LPS,IFN) and M(IL4,IL13) macrophages with respect to commonly used phenotype markers like CCR7 and genes associated with angiogenesis and tissue regeneration like FGF2 and MMP9. We found that the genes with the most similar patterns of expression among all sources were CXCL-10 and CXCL-11 for M(LPS,IFN) and CCL17 and CCL22 for M(IL4,IL13). Human PB-derived macrophages and human iPSC-derived macrophages showed similar gene expression patterns among the groups and genes studied here, suggesting that iPSC-derived monocytes have the potential to be used as a reliable cell source of human macrophages for in vitro studies. These findings could help select appropriate markers when testing macrophage behavior in vitro and highlight those markers that may confuse interpretation of results from experiments employing macrophages from different sources.

We report new results suggesting the feasibility of Raman spectrometry as a tool by which to examine the variability of tablet coatings. Our experiments feature a probe that can operate with a revolving laser focus to average content and coating non-uniformity. Raman spectral changes are correlated with tablet exposure times in a pan coater by means of partial least squares (PLS) multivariate analysis. Statistical models are found to be improved by pre-processing schemes that emphasize spectral changes while minimizing the effects of background light scattering and fluorescence. These pre-processing techniques include multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation, used in concert with Savitzky-Golay second derivative smoothing (SGSD). The two approaches give comparable results yielding R2 values for PLS calibration and cross-calibrated prediction variance regression of 0.999 and 0.997, respectively. Correlation results and model residual values demonstrate that Raman spectroscopy serves sensitively to reflect the coating thickness of the tablets studied.

A near infrared spectroscopic method was developed to determine drug content in a 20% (wt/wt) ibuprofen and spray-dried hydrous lactose blend. A blending profile was obtained after blending for 0.5, 1, 3, 5, 10, and 20 minutes. Stream sampling was used to collect about 20 blend samples at each of the blending times from a laboratory scale V-blender. The samples collected were used to develop a near infrared calibration model. The calibration model was then used to determine the drug content of unknown samples from 2 validation blends. The validation blends were not included in the calibration model; they were used to evaluate the effectiveness of the calibration model. A total of 45 samples from the 2 validation blends were predicted by the near infrared calibration model and then analyzed by a validated UV spectrophotometric method. The root mean square error of prediction for the first validation blend was 5.69 mg/g and 3.30 mg/g for the samples from the second blend. A paired t test at the 95% confidence level did not indicate any differences between the drug content predicted by the near infrared spectroscopy (NIRS) method and the validated UV method for the 2 blends. The results show that the NIRS method could be developed while the blending profile is generated and used to thoroughly characterize a new formulation during development by analyzing a large number of samples. The new formulation could be transferred to a manufacturing plant with an NIRS method to facilitate blend uniformity analysis.

In this study, the feasibility of using Raman spectroscopy as a fast, non-invasive, non-destructive technique to monitor crystallization and polymorphic transformations during freeze-drying is assessed using mannitol as the model compound. In-line process monitoring was achieved by interfacing a Raman spectrometer with a fiber-optically coupled, long-working-distance probe to a freeze-drier. By analyzing the process data using principal component analysis, it was possible to extract valuable information pertaining to ice and mannitol crystallization points, the polymorphic form of mannitol, and dehydration of the mannitol hydrate. In conclusion, Raman spectroscopy is a potentially useful technique to monitor physical changes during freeze-drying.

Analytical techniques for rapid and nondestructive content uniformity determination of pharmaceutical solid dosage forms have been studied for several years in an effort to replace the traditional wet chemistry procedures, which are labor intensive and time consuming. Both Raman spectroscopy and near-infrared spectroscopy have been used for this purpose, and predictability errors are approaching those of the traditional techniques. In this study, a low-resolution Raman spectrometer was utilized to demonstrate the feasibility of both rapid at-line and on-line determination of tablet content uniformity. Additionally, sampling statistics were reviewed in an effort to determine how many tablets should be assayed for specific batch sizes. A good correlation was observed between assay values determined by high-performance liquid chromatography and Raman analysis. Due to rapid acquisition times for the Raman data, it was possible to analyze far more samples than with wet chemistry methods, leading to a better statistical description of variation within the batch. For at-line experiments, the sampling volume was increased by rotating the laser beam during the acquisition period. For the on-line experiments, the sampling volume was increased by sampling from a stream of tablets moving underneath the Raman probe on a conveyor system. Finally, an approach is proposed for monitoring content uniformity immediately following the compaction process. In conclusion, Raman spectroscopy has potential as a rapid, nondestructive technique for at- or on-line determination of tablet content uniformity.

We report a novel approach to the measurement of colored tablet coating thickness, which employs Raman spectroscopy with univariate and multivariate data analysis. Our results suggest that Raman sensing can serve as a viable non-invasive means to quantify tablet coating thickness in the presence of a fluorescent ingredient in the coating formulation (food colorant Alphazurine FG or D&C Blue No. 4). This study comparatively tests the advantage of several data transformation approaches, including mean centering, standard normal variate, and Savitzky–Golay smoothed second derivative as means of improving predictive models in the presence of fluorescence. By application of the partial least squares (PLS) calibration algorithm to establish optimum covariance between transformed spectral data and measured tablet coating thicknesses, we have been able to create predictive models with calibration errors as small as 4 μm for a training set that spans colored coating thicknesses from 50 to 151 μm.