Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing

Quantification of drug contents in molded tablets via transmission low-frequency Raman spectroscopy

Orally disintegrating tablets (ODTs) are useful for elderly and pediatric patients suffering from difficulty in swallowing conventional tablets. Among various ODT preparing processes, wet tableting involves a process of the low-pressure compression of wet granules followed by drying, which is promising for the industrial production of medicines. Currently, the quality of wet molded tablets is analyzed via conventional high-performance liquid chromatography-based content uniformity tests. However, owing to the low compression pressure required for the wet tableting process, integrating spectroscopic probes with the tableting process can be a potential technique for real-time quality assurance. This study explores the use of transmission low-frequency Raman spectroscopy as a nondestructive and efficient approach for quantifying active pharmaceutical ingredients (APIs) in molded tablets. Acetaminophen and D-mannitol were used as the model API and excipient, respectively. Tablets were prepared by compressing under 150 and 300 N followed by analysis using the partial least squares regression method. The distinct spectral features of acetaminophen and D-mannitol enabled accurate quantification with good linear correlations (R2 = 0.98) and low root mean square prediction errors (1.59 for 150 N; 1.22 for 300 N). The method was effective under compressed forces, highlighting it as a real-time, nondestructive tool for quality control in pharmaceutical manufacturing. This approach aligns with continuous manufacturing and quality by design.

Comparing machine learning methods on Raman spectra from eight different spectrometers

In biotechnology, Raman Spectroscopy is becoming increasingly popular as a process analytical technology (PAT) for measuring substrates, metabolites, and product-related concentrations. By recording the vibrational modes of molecular bonds, it provides information non-invasively in a high-dimensional spectrum. Machine learning models are used to transform these spectral data into meaningful concentrations of species. Typically, one assumes a linear relationship between intensity and concentrations and learns these relationships using a partial least squares (PLS) model. However, in biological cultivations with a very large number of components, nonlinear models such as convolutional neural networks (CNN) offer significant advantages. In this work, we show that training one CNN on spectra from eight different spectrometers significantly outperforms PLS models. Specifically, we created samples with known concentrations of glucose, sodium acetate and magnesium sulfate and measured more than 2200 spectra of these samples with eight different spectrometers. We trained one CNN on the spectra from all eight datasets simultaneously. This shows great potential for laboratories with data from more than one spectrometer as they do not need to spend extra effort in calibrating individual PLS models, but they can use a joint CNN, which even improves the overall accuracy. In addition, we compare the eight different spectrometers against each other. The results suggest that three spectrometers are better suited for quantifying glucose, sodium acetate, and magnesium sulfate given the models.

Triplet Network for One-Shot Raman Spectrum Recognition

Raman spectroscopy is widely used for material detection due to its specificity, but its application to spectral recognition often faces limitations due to insufficient training data, unlike fields such as image recognition. Traditional machine learning or basic neural networks are commonly used, but they have limited ability to achieve high precision. We have proposed a novel approach that combines the Triplet network (TN) and K-nearest neighbor (KNN) techniques to address this issue. TN maps the Raman spectral sequences to a 128-dimensional Euclidean space to extract features, enabling the features in the new space to more accurately represent the similarities or differences between spectra, and then utilizes the KNN algorithm to perform classification tasks in this feature space. Our method exhibits superior performance in recognizing unknown Raman spectra with minimal training samples per class. We employed a handheld Raman spectrometer with an excitation wavelength of 785 nm to collect the Raman spectra of 36 samples, including 28 safe materials and eight hazardous materials. Using only one spectrum as a support set for each category, the hazardous samples were successfully distinguished from the safe samples with an accuracy of 99.6%. Additionally, our model offers adaptability without requiring exhaustive retraining when adding new prediction classes. In situations with high background fluorescence, the TN performs better in measuring the distance between spectra of the same class than traditional distance measurement methods.

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical process parameters and critical quality attributes. Surface-enhanced Raman spectroscopy (SERS) holds great promise but faces challenges like intensity fluctuations, compromising reproducibility. Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary "ON/OFF" signals based on defined intensity thresholds, which allows single-molecule event visualization and reduces false positives. Through integration with deep learning, this platform enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks. Furthermore, we demonstrate its accuracy and reproducibility for studying AMBIC 1.1 mammalian cell culture media. These results highlight its rapidity, accuracy, and precision, paving the way for widespread adoption and scale-up as a novel PAT tool in biomanufacturing and diagnostics.

White Light Spectroscopy Characteristics and Expansion Dynamic Behavior of Primary T-Cells: A Possibility of Online, Real-Time, and Sampling-Less CAR T-Cell Production Monitoring

The production of advanced therapy medicinal products (ATMP) is a long and highly technical process, resulting in a high cost per dose, which reduces the number of eligible patients. There is a critical need for a closed and sample-free monitoring system to perform the numerous quality controls required. Current monitoring methods are not optimal, mainly because they require the system to be opened up for sampling and result in material losses. White light spectroscopy has emerged as a technique for sample-free control compatible with closed systems. We have recently proposed its use to monitor cultures of CEM-C1 cell lines. In this paper, we apply this method to T-cells isolated from healthy donor blood samples. The main differences between cell lines and human primary T-cells lie in the slightly different shape of their absorption spectra and in the dynamics of cell expansion. T-cells do not multiply exponentially, resulting in a non-constant generation time. Cell expansion is described by a power-law model, which allows for the definition of instantaneous generation times. A correlation between the linear asymptotic behavior of these generation times and the initial cell concentration leads to the hypothesis that this could be an early predictive marker of the final culture concentration. To the best of our knowledge, this is the first time that such concepts have been proposed.

Open-source Raman spectra of chemical compounds for active pharmaceutical ingredient development

Raman spectroscopy is utilised extensively in pharmaceutical analysis for tasks such as drug discovery, quality control and active pharmaceutical ingredient (API) development. Despite this, access to open-source Raman spectral datasets for modelling and analysis is often a challenge. In laboratory settings, small spectral libraries are typically compiled for one-shot identification of intermediates or unknown chemicals, which restricts availability to comprehensive and high-quality reference data. In this work, we introduce a new open-source Raman dataset consisting of pure chemical compounds commonly employed in the development of APIs. By curating and publishing this dataset, we aim to provide the scientific community with access to high-quality, reusable data. Containing 3,510 samples spanning 32 compounds, this data can be utilised for referencing and can potentially facilitate in the development of more accurate and generalisable calibration models when access to reference data is limited.

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) struggle to achieve rapid and highly accurate monitoring of small molecule critical process parameters and critical quality attributes. While Raman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), is constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. Herein, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary "ON/OFF" signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of single-molecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1, a chemically-defined, serum-free complete media for mammalian cell culture. The obtained highly accurate and reproducible results demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

To Acquire or Not to Acquire: Evaluating Compressive Sensing for Raman Spectroscopy in Biology

Raman spectroscopy has revolutionized the field of chemical biology by providing detailed chemical and compositional information with minimal sample preparation. Despite its advantages, the technique suffers from low throughput due to the weak Raman effect, necessitating long acquisition times and expensive equipment. This limitation is particularly acute in time-sensitive applications like bioprocess monitoring and dynamic studies. Compressive sensing offers a promising solution by reducing the burden on measurement hardware, lowering costs, and decreasing measurement times. It allows for the collection of sparse data, which can be computationally reconstructed later. This paper explores the practical application of compressive sensing in spontaneous Raman spectroscopy across various biological samples. We demonstrate its benefits in scenarios requiring portable hardware, rapid acquisition, and minimal storage, such as skin hydration prediction and cellular studies involving drug molecules. Our findings highlight the potential of compressive sensing to overcome traditional limitations of Raman spectroscopy, paving the way for broader adoption in biological research and clinical diagnostics.

Calibration free approaches for rapid polymorph discrimination via low frequency (THz) Raman spectroscopy

Application of multivariate curve resolution to non-invasive Raman spectra has been investigated for rapid on-line analysis of crystallisation processes and high-throughput screening. Exploring quantification of mefenamic acid solid forms (form I, form II, and dimethylformamide solvate) from the Raman spectra indicated excellent agreement with off-line X-ray analysis.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

Raman spectroscopy as a tool for characterisation of quality parameters in Norwegian grown apples during ripening

This study shows for the first time the feasibility of Raman spectroscopy as a non-destructive method to follow the ripening process of apple fruits. Two different varieties of apples were studied: 'Aroma' and 'Elstar'. By visual inspection, Raman spectra showed that the starch content was higher in 'Elstar' apples compared to 'Aroma'. The degradation of starch over time could be detected in the Raman spectra, indicating that the method can be used to monitor the ripening process. The ripeness markers starch index, soluble solids content (SSC), and the sugars glucose, fructose and sucrose were determined with traditional destructive methods. Cross validated calibration models based on Raman spectroscopy were obtained for all quality parameters, and test set validation offered good results, with R2 in the range 0.4-0.86 for 'Aroma' and 0.4-0.95 for 'Elstar', respectively. The regression coefficients showed that the calibrations relied on Raman bands associated with starch and different sugars. The results suggest that Raman spectroscopy in the future could be used to determine the optimal time of harvesting and to sort apples into different degrees of ripeness.

In-line monitoring of bioreactor by Raman spectroscopy: Direct use of a standard-based model through cell-scattering correction

Raman spectroscopy and machine learning have become popular in in-line monitoring of bioreactors. However, traditional modeling processes typically entail extensive fermentation batches to collect learning datasets, which are significantly time-consuming and laborious. In addition, these models are limited to configurations with the same conditions as the training batches. The present work proposes a reproducible and adaptable modeling approach by combining standard spectra as a training dataset, with a simple means of correcting for cell scattering. Alcoholic fermentation by Saccharomyces cerevisiae is used as a benchmark. Initially, a partial least squares (PLS) regression model was developed based on the spectra of pure solutions of glucose and ethanol. Then, a mathematical expression was defined to estimate yeast concentration, allowing the correction of Raman intensity attenuated by cell scattering. The corrected spectra demonstrate close alignment with reference spectra in both shape and intensity. Validation of the methodology was conducted across numerous batches and one fed-batch bioreactor. As a result, the developed method enables the simultaneous monitoring of glucose, ethanol, and yeast concentrations, effectively addressing the challenge of implementing an independent standards based PLS model to manage the intricate compositional dynamics in bio-processes. The conclusion underscores the effectiveness of the proposed method and offers new prospects in biotechnological industries.

Completely noninvasive multi-analyte monitoring system for cell culture processes

Although online monitoring of dissolved O2, pH, and dissolved CO2 is critical in bioprocesses, nearly all existing technologies require some level of direct contact with the cell culture environment, posing risks of contamination. This study addresses the need for an accurate, and completely noninvasive technique for simultaneous measurement of these analytes. A "non-contact" technique for simultaneous monitoring of dissolved O2, pH, and dissolved CO2 was developed. Instead of direct contact with the culture media, the measurements were made through permeable membranes via either a sampling port in the culture vessel wall or a flow cell. The efficacy of the "non-contact" technique was validated in Escherichia coli (E.coli), Chinese hamster ovary (CHO) culture processes, and dynamic environments created by sparging gases in cell culture medium. The measurements obtained through the developed techniques were comparable to those obtained through control methods. The noninvasive monitoring system can offer accurate, and contamination-minimized monitoring of critical process parameters including dissolved O2, pH, and dissolved CO2. These advancements will enhance the control and optimization of cell culture processes, promising improved cell culture performance.

Exploring the Correlation between LC-MS Multi-Attribute Method and Conventional Chromatographic Product Quality Assays through Multivariate Data Analysis

Biotherapeutics are subject to inherent heterogeneity due to the complex biomanufacturing processes. Numerous analytical techniques have been employed to identify, characterize, and monitor critical quality attributes (CQAs) to ensure product safety, and efficacy. Mass spectrometry (MS)-based multi-attribute method (MAM) has become increasingly popular in biopharmaceutical industry due to its potential to replace multiple traditional analytical methods. However, the correlation between MAM and conventional methods remains to be fully understood. Additionally, the complex analytical workflow and limited throughput of MAM restricts its implementation as a quality control (QC) release assay. Herein, we present a simple, robust, and rapid MAM workflow for monitoring CQAs. Our rapid approach allowed us to create a database from ~700 samples, including site-specific post-translational modifications (PTMs) quantitation results using MAM and data from traditional charge variant and oxidation characterization methods. To gain insights from this database, we employ multivariate data analysis (MVDA) to thoroughly exploit the data. By applying partial least squares regression (PLSR) models, we demonstrate the ability to quantitatively predict charge variants in ion exchange chromatography (IEX) assay and oxidation abundances in hydrophobic-interaction chromatography (HIC) assay using MAM data, highlighting the interconnectivity between MAM and traditional product quality assays. These findings help evaluate the suitability of MAM as a replacement for conventional methods for release, and more importantly, contribute to enhanced process and product understanding.

Surface-Enhanced, Low-Frequency Raman Spectroscopy: A Sensitive Screening Tool for Structural Characterization of Pharmaceuticals

Surface-enhanced, low-frequency Raman spectroscopy (SELFRS) was explored for its potential as a structural screening tool within pharmaceutical applications, including facile small-scale multicomponent analysis. Paracetamol was used as the model drug, and its crystallization behavior with or without the presence of a templating agent (benzoic acid) was investigated using commercial silver-based SERS substrates. The Raman imaging was carried out using two different LFR-enabled instruments employing 532 and 785 nm incident lasers, where each of the setups showed certain affinity for differentiating lattice vibrations of the polymorphic forms of interest: form I and form II. A comparison of SELFRS, SERS, and their combination using chemometrics showed the potential for the LFR spectral range to improve surface-enhanced measurements either individually or in combination with the typically-used fingerprint region without the need to alter the experimental configuration. Additionally, the use of crystallization additives that helped to drive the formation of metastable form II was shown using SELFRS to provide additional mechanistic understanding of the template-assisted crystallization processes.

Freeze-drying-induced mutarotation of lactose detected by Raman spectroscopy

Freeze-drying enables delicate, heat-sensitive biomaterials to be stored in a dry form even at room temperature. However, exposure to physicochemical stress induced by freeze-drying presents challenges for maintaining material characteristics and functionality upon reconstitution, for which reason excipients are required. Although wide variety of different excipients are available for pharmaceutical applications, their protective role in the freeze-drying is not yet fully understood. In this study our aim was to use molecular dynamics simulations to screen the properties of different sugars and amino acids, which could be combined with plant-based nanofibrillated cellulose (NFC) hydrogel to provide protective matrix system for future freeze-drying for pharmaceuticals and biologics. The changes in the NFC-based formulations before and after freeze-drying and reconstitution were evaluated using non-invasive Timegate PicoRaman spectroscopy and traditional characterization methods. We continued to the freeze-drying with the NFC hydrogel formulations including lactose with and without glycine, which showed the highest attraction preferences on NFC surface in silico. This formulation enabled successful freeze-drying and subsequent reconstitution with preserved physicochemical and rheological properties. Raman spectroscopy gave us insights of the molecular-level changes during freeze-drying, especially the mutarotation of lactose. This research showed the potential of integrating in silico screening and non-invasive spectroscopical method to design novel biomaterial-based formulations for freeze-drying. The research provided insights of the molecular-level interactions and orientational changes of the excipients, which might be crucial in future freeze-drying applications of pharmaceuticals and biologics.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.

Ergot alkaloid control in biotechnological processes and pharmaceuticals (a mini review)

The control of ergot alkaloids in biotechnological processes is important in the context of obtaining new strain producers and studying the mechanisms of the biosynthesis, accumulation and secretion of alkaloids and the manufacturing of alkaloids. In pharmaceuticals, it is important to analyze the purity of raw materials, especially those capable of racemization, quality control of dosage forms and bulk drugs, stability during storage, etc. This review describes the methods used for qualitative and quantitative chemical analysis of ergot alkaloids in tablets and pharmaceutic forms, liquid cultural media and mycelia from submerged cultures of ergot and other organisms producing ergoalkaloid, sclerotias of industrial Claviceps spp. parasitic strains. We reviewed analytical approaches for the determination of ergopeptines (including their dihydro- and bromine derivatives) and semisynthetic ergot-derived medicines such as cabergoline, necergoline and pergolide, including precursors for their synthesis. Over the last few decades, strategies and approaches for the analysis of ergoalkaloids for medical use have changed, but the general principles and objectives have remained the same as before. These changes are related to the development of new genetically improved strains producing ergoalkaloids and the development of technologies for the online control of biotechnological processes and pharmaceutical manufacturing ("process analytical technologies," PAT). Overall, the industry is moving toward "smart manufacturing." The development of approaches to production cost estimation and product quality management, manufacturing management, increasing profitability and reducing the negative impact on personnel and the environment are integral components of sustainable development. Analytical approaches for the analysis of ergot alkaloids in pharmaceutical raw materials should have high enough specificity for the separation of dihydro derivatives, enantiomers and R-S epimers of alkaloids, but low values of the quantitative detection limit are less frequently needed. In terms of methodology, detection methods based on mass spectrometry have become more developed and widespread, but NMR analysis remains in demand because of its high accuracy and specificity. Both rapid methods and liquid chromatography remain in demand in routine practice, with rapid analysis evolving toward higher accuracy owing to improved analytical performance and new equipment. New composite electrochemical sensors (including disposable sensors) have demonstrated potential for real-time process control.

Aerogel-Lined Capillaries as Liquid-Core Waveguides for Raman Signal Gain of Aqueous Samples: Advanced Manufacturing and Performance Characterization

An advanced process for the manufacturing of aerogel-lined capillaries is presented; these are applicable as liquid-core waveguides for gaining the Raman signal of aqueous samples. With respect to the spin-coating process we have used so far for the manufacturing of aerogel-lined capillaries, the here-presented manufacturing process is advanced as it enables (i) the lining of longer capillaries, (ii) the adjustment of the lining-thickness via the lining velocity, and (iii) the reproducible generation of crack-free linings. The key parameters of the advanced process and their effect on the fabrication of aerogel-lined capillaries with optimal Raman signal gain are reported and related to the thickness and topography of the aerogel linings by the support of scanning electron microscopy.

Rational formulation and industrial manufacturing of lipid-based complex injectables: Landmarks and trends

Lipid-based complex injectables are renowned for their effectiveness in delivering drugs, with many approved products. While significant strides have been made in formulating nanosystems for small molecular weight drugs, a pivotal breakthrough emerged with the recognition of lipid nanoparticles as a promising platform for delivering nucleic acids. This finding has paved the way for tackling long-standing challenges in molecular and delivery aspects (e.g., mRNA stability, intracellular delivery) that have impeded the clinical translation of gene therapy, especially in the realm of immunotherapy. Nonetheless, developing and implementing new lipid-based delivery systems pose significant challenges, as industrial manufacturing of these formulations often involves complex, multi-batch processes, giving rise to issues related to scalability, stability, sterility, and regulatory compliance. To overcome these obstacles, embracing the principles of quality-by-design (QbD) is imperative. Furthermore, adopting cutting-edge manufacturing and process analytical tools (PAT) that facilitate the transition from batch to continuous production is essential. Herein, the key milestones and insights derived from the development of currently approved lipid- nanosystems will be explored. Additionally, a comprehensive and critical overview of the latest technologies and regulatory guidelines that underpin the creation of more efficient, scalable, and flexible manufacturing processes for complex lipid-based nanoformulations will be provided.

Application of in-line Raman spectroscopy to monitor crystallization and melting processes in milk fat

Anhydrous milk fat (AMF) and its fractions are used as ingredients in a wide range of food applications. Obtaining the appropriate solid fat content (SFC) is essential to achieve the desired product texture. At present, in-line monitoring techniques to control milk fat crystallization and melting are largely unavailable. The thermal behaviour of milk fat (AMF and four of its fractions) was monitored in a temperature-controlled vessel using an in-line Raman analyser and compared with thermograms generated using differential scanning calorimetry (DSC). The major stages of milk fat crystallization and melting were identified using the in-line Raman analyser. Thermal data from DSC showed excellent linear correlations with Raman spectral data (R2 value of 0.97 for the onset of milk fat crystallisation). Partial least squares regression (PLSR) models were developed using Raman spectra to predict SFC with coefficient of determination (R2Cs) from 0.929 to 0.992 and root mean standard error of calibration (RMSECs) ranging from 3.20 to 10.36%. Results demonstrated Raman spectroscopy has significant potential as a way of monitoring milk fat crystallization and melting processes.

Advances and opportunities in process analytical technologies for viral vector manufacturing

Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.

Emerging technologies for quality control of cell-based, advanced therapy medicinal products

Advanced therapy medicinal products (ATMP) are complex medicines based on gene therapy, somatic cell therapy, and tissue engineering. These products are rapidly arising as novel and promising therapies for a wide range of different clinical applications. The process for the development of well-established ATMPs is challenging. Many issues must be considered from raw material, manufacturing, safety, and pricing to assure the quality of ATMPs and their implementation as innovative therapeutic tools. Among ATMPs, cell-based ATMPs are drugs altogether. As for standard drugs, technologies for quality control, and non-invasive isolation and production of cell-based ATMPs are then needed to ensure their rapidly expanding applications and ameliorate safety and standardization of cell production. In this review, emerging approaches and technologies for quality control of innovative cell-based ATMPs are described. Among new techniques, microfluid-based systems show advantages related to their miniaturization, easy implementation in analytical process and automation which allow for the standardization of the final product.

Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Robust platform for inline Raman monitoring and control of perfusion cell culture

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Optics miniaturization strategy for demanding Raman spectroscopy applications

Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA's Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm-1 resolution within the 400-4000 cm-1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.

Giving the cells what they need when they need it: Biosensor-based feeding control

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

In Operando Analysis of Milk-Based Oral Formulations during Digestion Using Synchrotron Small-Angle X-ray Scattering Coupled to Low-Frequency Raman Spectroscopy

A low-frequency Raman (LFR) probe was coupled to an in-line small-angle X-ray scattering (SAXS) beamline to test the capabilities of a combinatory approach for the determination of lipid and drug behavior during the enzymatic lipolysis of milk-based oral formulations. Cinnarizine was used as the model drug, and its solubilization dynamics as well as its potential impact on the supramolecular structures formed by the digestion products of bovine milk were evaluated from the perspective of both techniques. The SAXS data were superior in distinguishing various liquid crystalline assemblies formed during the digestion process, with LFR providing complementary information regarding the formation of calcium soaps. On the other hand, studying changes in the LFR domain allowed the differentiation of drug solubilization and precipitation; processes that were less clear from the X-ray scattering data. Given the relative simplicity of the combined experimental setup, these results highlight the advantages that the combination of the two techniques can provide for understanding and developing new lipid-based formulations and will help to translate the results obtained at synchrotron facilities to routine analysis procedures in laboratory/industry-based environments.

Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step

The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.

Validation of the cell culture monitoring using a Raman spectroscopy calibration model developed with artificially mixed samples and investigation of model learning methods using initial batch data

The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.

Robust method for chromatogram shape analysis to improve early detection of performance drifts and adverse changes in process parameters during purification column operations

The biopharmaceutical industry is under increased pressure to maximize efficiency, enhance quality compliance, and reduce the cost of drug substance manufacturing. Ways to reduce costs associated with manufacturing of complex biological molecules include maximizing efficiency of chromatography purification steps. For example, process analytical technology (PAT) tools can be employed to improve column resin life, prevent column operating failures, and decrease the time it takes to solve investigations of process deviations. We developed a robust method to probe the shape of the chromatogram for indications of column failure or detrimental changes in the process. The approach herein utilizes raw data obtained from manufacturing followed by a pre-processing routine to align chromatograms and patch together the different chromatogram phases in preparation for multivariate analysis. A principal component analysis (PCA) was performed on the standardized chromatograms to compare different batches, and resulted in the identification specific process change that affected the profile. In addition, changes in the chromatogram peaks were used to create predictive models for impurity clearance. This approach has the potential for early detection of column processing issues, improving timely resolution in large-scale chromatographic operations.

The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry

It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Food Safety Issues in the Oltrepò Pavese Area: A SERS Sensing Perspective

Handly and easy-to-use optical instrumentation is very important for food safety monitoring, as it provides the possibility to assess law and health compliances at every stage of the food chain. In particular, the Surface-enhanced Raman Scattering (SERS) method appears highly promising because the intrinsic drawback of Raman spectroscopy, i.e., the natural weakness of the effect and, in turn, of the signal, is overcome thanks to the peculiar interaction between laser light and plasmonic excitations at the SERS substrate. This fact paved the way for the widespread use of SERS sensing not only for food safety but also for biomedicine, pharmaceutical process analysis, forensic science, cultural heritage and more. However, the current technological maturity of the SERS technique does not find a counterpart in the recognition of SERS as a routine method in compliance protocols. This is mainly due to the very scattered landscape of SERS substrates designed and tailored specifically for the targeted analyte. In fact, a very large variety of SERS substrates were proposed for molecular sensing in different environments and matrices. This review presents the advantages and perspectives of SERS sensing in food safety. The focus of the survey is limited to specific analytes of interest for producers, consumers and stakeholders in Oltrepò Pavese, a definite regional area that is located within the district of Pavia in the northern part of Italy. Our attention has been addressed to (i) glyphosate in rice fields, (ii) histamine in a world-famous local product (wine), (iii) tetracycline, an antibiotic often detected in waste sludges that can be dangerous, for instance in maize crops and (iv) Sudan dyes-used as adulterants-in the production of saffron and other spices, which represent niche crops for Oltrepò. The review aims to highlight the SERS performance for each analyte, with a discussion of the different methods used to prepare SERS substrates and the different reported limits of detection.

A targeted liquid chromatography mass spectrometry method for routine monitoring of cell culture media components for bioprocess development

The analysis of cell culture media (CCM) components is critical for understanding cell growth kinetics and overall product quality during biomanufacturing. Given the diverse physical and chemical nature of CCM compounds present at a wide range of concentrations, there is an increasing demand for single-platform analytical assays with exceptional specificity and sensitivity. This study presents a targeted LC-MS/MS method for the identification and quantitation of 110 CCM analytes is presented, where target metabolites are monitored over an 20-min gradient. The analyte panel constitutes amino acids, vitamins, organic acids, nucleic acids, carbohydrates, and lipids. The method employs isotopically labeled standards to enable specific and accurate relative quantitation of CCM compounds based on physicochemical properties and retention time. Quantitation is performed on a triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. The method demonstrates strong linearity with an R2 of ≥0.99 with three orders of linear dynamic range and inter-day and intra-day precision with a%CV of <10% for spiked-in quality control samples. We also present three case studies to demonstrate method applicability in the bioprocessing space for developing vaccines and biologics.

Evaluation of a compact composite sensor array for concentration monitoring of solutions and suspensions via multivariate analysis

Compact composite probes were identified as a priority to alleviate space constraints in miniaturized unit operations and pharmaceutical manufacturing platforms. Therefore, in this proof of principle study, a compact composite sensor array (CCSA) combining ultraviolet and near infrared features at four different wavelengths (280, 340, 600, 860 nm) in a 380 × 30 mm housing (length x diameter, 7 mm diameter at the probe head), was evaluated for its capabilities to monitor in situ concentration of solutions and suspensions via multivariate analysis using partial least squares (PLS) regression models. Four model active pharmaceutical ingredients (APIs): warfarin sodium isopropanol solvate (WS), lidocaine hydrochloride monohydrate (LID), 6-mercaptopurine monohydrate (6-MP), and acetaminophen (ACM) in their aqueous solution and suspension formulation were used for the assessment. The results demonstrate that PLS models can be applied for the CCSA prototype to measure the API concentrations with similar accuracy (validation samples within the United States Pharmacopeia (USP) limits), compared to univariate CCSA models and multivariate models for an established Raman spectrometer. Specifically, the multivariate CCSA models applied to the suspensions of 6-MP and ACM demonstrate improved accuracy of 63% and 31%, respectively, compared to the univariate CCSA models [1]. On the other hand, the PLS models for the solutions WS and LID showed a reduced accuracy compared to the univariate models [1].

An adaptive modeling approach using spiking-augmentation method to improve chemometric model performance in bioprocess monitoring

Intensified and continuous processes require fast and robust methods and technologies to monitor product titer for faster analytical turnaround time, process monitoring, and process control. The current titer measurements are mostly offline chromatography-based methods which may take hours or even days to get the results back from the analytical labs. Thus, offline methods will not meet the requirement of real time titer measurements for continuous production and capture processes. FTIR and chemometric based multivariate modeling are promising tools for real time titer monitoring in clarified bulk (CB) harvests and perfusate lines. However, empirical models are known to be vulnerable to unseen variability, specifically a FTIR chemometric titer model trained on a given biological molecule and process conditions often fails to provide accurate predictions of titer in another molecule under different process conditions. In this study, we developed an adaptive modeling strategy: the model was initially built using a calibration set of available perfusate and CB samples and then updated by augmenting spiking samples of the new molecules to the calibration set to make the model robust against perfusate or CB harvest of the new molecule. This strategy substantially improved the model performance and significantly reduced the modeling effort for new molecules.

Continuous multi-membrane chromatography of large viral particles

Continuous multi-column chromatography (CMCC) has been successfully implemented to address biopharmaceutical biomolecule instability, to improve process efficiency, and to reduce facility footprint and capital cost. This paper explores the implementation of a continuous multi-membrane chromatography (CMMC) process, using four membrane units, for a large viral particle in just few weeks. CMMC improves the efficiency of the chromatography step by enabling higher loads with smaller membranes for multiple cycles of column use and enables steady-state continuous bioprocessing. The separation performance of CMMC was compared to a conventional batch chromatographic capture step used at full manufacturing scale. The product step yield was 80% using CMMC versus 65% in batch mode while increasing slightly the relative purity. Furthermore, the total amount of membrane area required for the CMMC approach was approximately 10% of the area needed for batch operation, while realizing similar processing times. Since CMMC uses smaller membrane sizes, it can take advantage of the high flow rates achievable for membrane chromatography that are not typically possible at larger membrane scales due to skid flow rate limitations. As such, CMMC offers the potential for more efficient and cost-effective purification trains.

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.

Performance assessment of probe-based Raman spectroscopy systems for biomedical analysis

We present a methodology for evaluating the performance of probe-based Raman spectroscopy systems for biomedical analysis. This procedure uses a biological standard sample and data analysis approach to circumvent many of the issues related to accurately measuring and comparing the signal quality of Raman spectra between systems. Dairy milk is selected as the biological standard due to its similarity to tissue spectral properties and because its homogeneity eliminates the dependence of probe orientation on the measured spectrum. A spectral dataset is first collected from milk for each system configuration, followed by a model-based correction step to remove photobleaching artifacts and accurately calculate SNR. Results demonstrate that the proposed strategy, unlike current methods, produces an experimental SNR that agrees with the theoretical value. Four preconfigured imaging spectrographs that share similar manufacturer specifications were compared, showing that their capabilities to detect biological Raman spectra widely differ in terms of throughput and stray light rejection. While the methodology is used to compare spectrographs in this case, it can be adapted for other purposes, such as optimizing the design of a custom-built Raman spectrometer, evaluating inter-probe variability, or examining how altering system subcomponents affects signal quality.

Co-administration of Intravenous Drugs: Rapidly Troubleshooting the Solid Form Composition of a Precipitate in a Multi-drug Mixture Using On-Site Raman Spectroscopy

Intravenous drugs are often co-administrated in the same intravenous catheter line due to which compatibility issues, such as complex precipitation processes in the catheter line, may occur. A well-known example that led to several neonatal deaths is the precipitation due to co-administration of ceftriaxone- and calcium-containing solutions. The current study is exploring the applicability of Raman spectroscopy for testing intravenous drug compatibility in hospital settings. The precipitation of ceftriaxone calcium was used as a model system and explored in several multi-drug mixtures containing both structurally similar and clinically relevant drugs for co-infusion. Equal molar concentrations of solutions containing ceftriaxone and calcium chloride dihydrate were mixed with solutions of cefotaxime, ampicillin, paracetamol, and metoclopramide. The precipitate formed was collected as an "unknown" material, dried, and analyzed. Several solid-state analytical methods, including X-ray powder diffraction, Raman spectroscopy, and thermogravimetric analysis, were used to characterize the precipitate. Raman microscopy was used to investigate the identity of single sub-visual particles precipitated from a mixture of ceftriaxone, cefotaxime, and calcium chloride. X-ray powder diffraction suggested that the precipitate was partially crystalline; however, the identity of the solid form of the precipitate could not be confirmed with this standard method. Raman spectroscopy combined with multi-variate analyses (principal component analysis and soft independent modelling class analogy) enabled the correct detection and identification of the precipitate as ceftriaxone calcium. Raman microscopy enabled the identification of ceftriaxone calcium single particles of sub-visual size (around 25 μm), which is in the size range that may occlude capillaries. This study indicates that Raman spectroscopy is a promising approach for supporting clinical decisions and especially for compatibility assessments of drug infusions in hospital settings.

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

Optogenetic closed-loop feedback control of the unfolded protein response optimizes protein production

In biotechnological protein production processes, the onset of protein unfolding at high gene expression levels leads to diminishing production yields and reduced efficiency. Here we show that in silico closed-loop optogenetic feedback control of the unfolded protein response (UPR) in S. cerevisiae clamps gene expression rates at intermediate near-optimal values, leading to significantly improved product titers. Specifically, in a fully-automated custom-built 1L-photobioreactor, we used a cybergenetic control system to steer the level of UPR in yeast to a desired set-point by optogenetically modulating the expression of α-amylase, a hard-to-fold protein, based on real-time feedback measurements of the UPR, resulting in 60% higher product titers. This proof-of-concept study paves the way for advanced optimal biotechnology production strategies that diverge from and complement current strategies employing constitutive overexpression or genetically hardwired circuits.