Rapid at-line early cell death quantification using capacitance spectroscopy.

Cell death is one of the failure modes of mammalian cell culture. Apoptosis is a regulated cell death process mainly observed in cell culture. Timely detection of apoptosis onset allows opportunities for preventive controls that ensure high productivity and consistent product quality. Capacitance spectroscopy captures the apoptosis-related cellular properties changes and thus quantifies the percentage of dying cells. This study demonstrated a quantification model that measures the percentage of apoptotic cells using a capacitance spectrometer in an at-line setup. When predicting the independent test set collected from bench-scale bioreactors, the root-mean-squared error of prediction was 8.8% (equivalent to 9.9% of the prediction range). The predicted culture evolution trajectory aligned with measured values from the flow cytometer. Furthermore, this method alarms cell death onset earlier than the traditional viability test, that is, the trypan blue exclusion test. Compared to flow cytometry (the traditional early cell death detection method), this method is rapid, simple, and less labor-intensive. In addition, this at-line setup can be easily transferred between scales (e.g., lab-scale for development to manufacturing scale), which benefits process transfers between facilities, scale-up, and other process transitions.

Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models.

Near infrared spectroscopy (NIRS) is often used during the tablet coating process to assess coating thickness. As the coating process proceeds, the increase and decrease in NIRS signal from both the coating formulation and tablet core has been related to coating thickness. Partial least-squares models are often generated relating NIRS spectra to reference coating thickness measurements for in-line and/or at-line monitoring of the coating process. This study investigated the effect of the reference coating thickness measurements on the accuracy of the model. The two primary reference techniques used were weight gain-based coating thickness and terahertz-based coating thickness. Most NIRS coating thickness models currently use weight gain-based reference values; however, terahertz-time-of-flight spectroscopy (THz-TOF) offers a more direct reference coating thickness measurement. Results showed that the accuracy of the NIRS coating thickness model significantly improved when terahertz-based coating thickness measurements were used as reference when compared to weight gain-based coating thickness measurements. Therefore, the application of THz-TOF as a reference method is further demonstrated.

Quality by Design Approach Using Multiple Linear and Logistic Regression Modeling Enables Microemulsion Scale Up.

The development of pharmaceutical nanoformulations has accelerated over the past decade. However, the nano-sized drug carriers continue to meet substantial regulatory and clinical translation challenges. In order to address some of these key challenges in early development, we adopted a quality by design approach to develop robust predictive mathematical models for microemulsion formulation, manufacturing, and scale-up. The presented approach combined risk management, design of experiments, multiple linear regression (MLR), and logistic regression to identify a design space in which microemulsion colloidal properties were dependent solely upon microemulsion composition, thus facilitating scale-up operations. Developed MLR models predicted microemulsion diameter, polydispersity index (PDI), and diameter change over 30 days storage, while logistic regression models predicted the probability of a microemulsion passing quality control testing. A stable microemulsion formulation was identified and successfully scaled up tenfold to 1L without impacting droplet diameter, PDI, or stability.

Feedforward and Feedback Control of a Pharmaceutical Coating Process.

This work demonstrates the use of a combination of feedforward and feedback loops to control the controlled release coating of theophylline granules. Feedforward models are based on the size distribution of incoming granules and are used to set values for the airflow in the fluid bed processor and the target coat weight to be applied to the granules. The target coat weight of the granules is controlled by a feedback loop using NIR spectroscopy to monitor the progress of the process. By combining feedforward and feedback loops, significant variation in the size distributions and ambient conditions were accommodated in the fluid bed coating of the granules and a desired dissolution profile was achieved. The feedforward component of the control system was specifically tested by comparing the performance of the control system with and without this element by Monte Carlo simulation.

Method Development and Validation of an Inline Process Analytical Technology Method for Blend Monitoring in the Tablet Feed Frame Using Raman Spectroscopy.

Inline process analytical technology sensors are the key elements to enable continuous manufacturing. They facilitate real-time monitoring of critical quality attributes of both intermediate materials and finished products. The aim of this study was to demonstrate method development and validation for inline and offline calibration strategies to determine the blend content during tablet compression via Raman spectroscopy. An inline principal component regression model was developed from Raman spectra collected in the feed frame. At the same time, an offline study was conducted over a small amount of the calibration blends using an in-house moving powder setup to simulate the environment of the feed frame. The model developed offline was able to predict the active ingredient content after a bias correction and used only a fraction of the material. The offline method can serve as a simple method to facilitate calibration development when the time and access to the press is limited. The study takes into consideration, the necessary components of method development and offers perspectives on the validation of an inline process analytics method. Method testing and validation was performed for the inline process analytical technology method. The established Raman method was demonstrated as suitable for the determination of bulk assay of the active ingredient in powders inside the feed frame for use during batch and continuous manufacturing processes.

Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams.

Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation ("gold standard" model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.

Adaptive modeling optimized by the data fusion strategy: Real-time dying cell percentage prediction using capacitance spectroscopy.

The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in-line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole-Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X-matrix). Firstly, the Cole-Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X-matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root-mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.

Development of iterative optimization technology: Selecting pure component spectra using a small-scale feed frame simulator.

The application of spectroscopic process analytical technology (PAT) for in-line data collection offers advantages to modern pharmaceutical manufacturing. Partial least squares (PLS) models are the preferred approach for predicting API potency from PAT data, particularly near-infrared (NIR) spectra. However, the calibration burden of PLS models is sometimes considered prohibitive. Pure component approaches, such as iterative optimization technology (IOT), have a reduced calibration burden for PAT applications. The IOT algorithm is dependent on several assumptions, including the harmonization of spectral collection conditions for pure component and mixture spectra. Collecting pure components under identical conditions to mixture spectra does not guarantee accurate predictions, and not all pure components are suitable for individual processing. This IOT assumption must be addressed to facilitate IOT application in PAT systems. In this work, IOT predicted API potency from in-line NIR spectra using combinations of stagnant and dynamic pure component spectra. A small number of mixture samples called a development set guided the selection of representative pure component spectral sets. Several model performance metrics from the development set predictions identified optimal pure component spectral sets for prediction of test sets. The combination of IOT and a development set generated accurate API potency predictions and potentiates the application of IOT in challenging pharmaceutical manufacturing settings. The IOT assumption of similar collection conditions should not be regarded as an assumption, but rather a consideration that the pure component spectral collection conditions should be representative of the mixture spectra to ensure appropriate predictions.

Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process.

Near-infrared (NIR) spectroscopy is a powerful process analytical tool for monitoring chemical constituents in continuous pharmaceutical processes. However, the density variation introduced when quantitative NIR measurements are performed on powder streams at different flow rates is a potential source of a lack of model robustness. Since different flow rates are often required to meet the production requirements (e.g., during scale-up) of a continuous process, the development of efficient strategies to characterize, understand, and mitigate the impact of powder density on NIR measurements is highly desirable. This study focused on assessing the effect of powder physical variation on NIR by enabling the in-line characterization of powder stream density in a simulated continuous system. The in-line measurements of powder stream density were facilitated through a unique analytical interface to a flowing process. Powder streams delivered at various design levels of flow rate and tube angle were monitored simultaneously by NIR diffuse reflectance spectroscopy, live imaging, and dynamic mass characterization. Statistical analysis and multivariate modeling confirmed powder density as a significant source of spectral variability due to flow rate. Besides providing broader process understanding, results elucidated potential mitigation strategies to facilitate effective continuous process scale-up while ensuring NIR model robustness against density.

Capacitance spectroscopy enables real-time monitoring of early cell death in mammalian cell culture.

BACKGROUND/AIMS: Previous work developed a quantitative model using capacitance spectroscopy in an at-line setup to predict the dying cell percentage measured from a flow cytometer. This work aimed to transfer the at-line model to monitor lab-scale bioreactors in real-time, waiving the need for frequent sampling and enabling precise controls. METHODS AND RESULTS: Due to the difference between the at-line and in-line capacitance probes, direct application of the at-line model resulted in poor accuracy and high prediction bias. A new model with a variable range and offering similar spectral shape across all probes was first constructed, improving prediction accuracy. Moreover, the global calibration method included the variance of different probes and scales in the model, reducing prediction bias. External parameter orthogonalization, a preprocessing method, also mitigated the interference from feeding, which further improved model performance. The root-mean-square error of prediction of the final model was 6.56% (8.42% of the prediction range) with an R2 of 92.4%. CONCLUSION: The culture evolution trajectory predicted by the in-line model captured the cell death and alarmed cell death onset earlier than the trypan blue exclusion test. Additionally, the incorporation of at-line spectra following orthogonal design into the calibration set was shown to generate calibration models that are more robust than the calibration models constructed using the in-line spectra only. This is advantageous, as at-line spectral collection is easier, faster, and more material-sparing than in-line spectra collection.

Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures.

Process analytical technology (PAT) is an essential tool within pharmaceutical manufacturing to ensure consistent quality and maintain process control. Near-infrared (NIR) spectroscopy is one of the most popular PAT techniques, particularly for monitoring active pharmaceutical ingredient (API) concentrations. To interpret the spectral outputs of NIR spectroscopy, advanced multivariate models are required. Calibration-free models such as iterative optimization technology (IOT) algorithms are increasingly of interest, due primarily to their reduced material and time burdens. Variable/wavelength selection is a common method to improve prediction performance and robustness for IOT by focusing on spectral regions with the most relevant information. However, currently proposed wavelength selection approaches rely on training sets for optimization, therefore reducing or removing the advantages of IOT over empirical calibration-dependent models. In this work, a true calibration-free wavelength selection method is proposed based on measuring the difference between individual wavelengths of a mixture spectra and the net analyte signals via a wavelength angle mapper (WAM). An extension of the WAM utilizing a spectral window of wavelength instead of individual wavelengths, called SWAM, was also developed. However, the SWAM method does require a small training set to optimize wavelength selection parameters. The WAM and SWAM methods showed similar prediction performance for API in pharmaceutical powder blends when compared against other calibration-dependent models and the base IOT algorithm.

Challenges, opportunities and recent advances in near infrared spectroscopy applications for monitoring blend uniformity in the continuous manufacturing of solid oral dosage forms.

Near infrared (NIR) spectroscopy has been widely recognized as a powerful PAT tool for monitoring blend uniformity in continuous manufacturing (CM) processes. However, the dynamic nature of the powder stream and the fast rate at which it moves, compared to batch processes, introduces challenges to NIR quantitative methods for monitoring blend uniformity. For instance, defining the effective sample size interrogated by NIR, selecting the best sampling location for blend monitoring, and ensuring NIR model robustness against influential sources of variability are challenges commonly reported for NIR applications in CM. This article reviews the NIR applications for powder blend monitoring in the continuous manufacturing of solid oral dosage forms, with a particular focus on the challenges, opportunities for method optimization and recent advances with respect three main aspects: effective sample size measured by NIR, probe location and method robustness.

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed-batch conditions.

Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.

Development of calibration-free/minimal calibration wavelength selection for iterative optimization technology algorithms toward process analytical technology application.

As continuous manufacturing (CM) processes are developed, process analytical technology (PAT) via NIR spectroscopy has become an integral tool in process monitoring. NIR spectroscopy requires the deployment of complex multivariate models to extract the relevant information. The model of choice for the pharmaceutical industry is Partial Least Squares (PLS). However, the development of PLS can be burdensome due to the time and resource intensive requirements of calibration. To overcome this challenge, calibration-free/minimal calibration approaches have become of increasing interest. Iterative optimization technology (IOT) algorithms are a favorable calibration-free/minimal calibration approach with only the requirement of pure component spectra for successful active pharmaceutical ingredient (API) quantification. IOT algorithms were utilized to monitor potency trends (qualitative) and API content (quantitative) in a CM system and compared to a traditional PLS model. To overcome the reduced prediction performance of IOT during non-steady state conditions, a novel wavelength method based on variable importance in projection scores was employed. Overall, the success and value of IOT algorithms for application in CM settings was demonstrated.

NIR spectroscopic methods for monitoring blend potency in a feed frame - calibration transfer between offline and inline using a continuum regression filter.

A material sparing method for near-infrared (NIR) calibration was developed using an offline apparatus coupled with a calibration transfer method to enable a partial least squares (PLS) model to monitor the concentration of active pharmaceutical ingredients (API) in the feed frame of a rotary tablet press. The offline apparatus was designed to simulate the powder flow dynamic and NIRS measurement environment of a tablet-press feed frame. A comprehensive experimental design, including calibration and testing, was employed to determine blend inhomogeneity. NIR spectra were collected at both the feed frame (inline) conditions and the simulator (offline) conditions. The simulator conditions were designed to mimic the density and powder flow in the feed frame during the actual tableting process. The offline data were pretreated by an orthogonalization-based calibration transfer algorithm, a continuum regression filter (CR filter), before being subjected to PLS modeling. This study demonstrated: (1) calibration for inline application can be generated using an offline apparatus, and (2) the CR filter, as an innovative calibration transfer method, can generalize the offline method for multiple feed frame conditions.

Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes.

Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination.

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Rapid serum-free/suspension adaptation: Medium development using a definitive screening design for Chinese hamster ovary cells.

The biopharmaceutical industry prefers to culture the mammalian cells in suspension with a serum-free media (SFM) due to improved productivity and process consistency. However, mammalian cells preferentially grow as adherent cells in a complete medium (CM) containing serum. Therefore, cells require adaptation from adherence in CM to suspension culture in SFM. This work proposes an adaptation method that includes media supplementation during the adaption of Chinese hamster ovary cells. As a result, the adaptation was accelerated compared to the traditional repetitive subculturing. Ca2+ /Mg2+ supplementation significantly reduced the doubling time compared to the adaptation without supplementation during the adaptation of adherent cells from 100% CM to 75% CM (p < 0.05). Furthermore, a definitive screening design (DSD) was applied to select essential nutrients during the adaptation from 10% CM to 0% CM. The main effects of Ca2+ and Dulbecco's modified essential medium (DMEM) were found significant to both viable cell density and viability at harvest. Additionally, the interaction term between Ca2+ and DMEM was found significant, which highlights the ability of DSD to capture interaction terms. Eventually, the media supplementation method resulted in adaptation SFM in 27 days, compared to the previously reported 66 days. Additionally, the membrane surface integrin expression was found significantly decreased when adherent cells were adapted to suspension. Moreover, the Ca2+ /Mg2+ supplementation correlated with faster integrin recovery after trypsinization. However, faster integrin recovery did not contribute to the accelerated cell growth when subculturing from 100% CM to 75% CM.

Quality by Design Methodology Applied to Process Optimization and Scale up of Curcumin Nanoemulsions Produced by Catastrophic Phase Inversion.

In the presented study, we report development of a stable, scalable, and high-quality curcumin-loaded oil/water (o/w) nanoemulsion manufactured by concentration-mediated catastrophic phase inversion as a low energy nanoemulsification strategy. A design of experiments (DoE) was constructed to determine the effects of process parameters on the mechanical input required to facilitate the transition from the gel phase to the final o/w nanoemulsion and the long-term effects of the process parameters on product quality. A multiple linear regression (MLR) model was constructed to predict nanoemulsion diameter as a function of nanoemulsion processing parameters. The DoE and subsequent MLR model results showed that the manufacturing process with the lowest temperature (25 °C), highest titration rate (9 g/minute), and lowest stir rate (100 rpm) produced the highest quality nanoemulsion. Both scales of CUR-loaded nanoemulsions (100 g and 500 g) were comparable to the drug-free optimal formulation with 148.7 nm and 155.1 nm diameter, 0.22 and 0.25 PDI, and 96.29 ± 0.76% and 95.60 ± 0.88% drug loading for the 100 g and 500 g scales, respectively. Photostability assessments indicated modest loss of drug (<10%) upon UV exposure of 24 h, which is appropriate for intended transdermal applications, with expected reapplication of every 6-8 h.

Refractive Index Measurement of Pharmaceutical Solids: A Review of Measurement Methods and Pharmaceutical Applications.

Refractive index is an important optical parameter that can be used to characterize the physicochemical properties of pharmaceutical solids. The complexity of most drugs and solid oral dosage systems introduces challenges for refractive index measurement methods. These challenges are highlighted, and different types of measurement methods are discussed in this review article. These measurements provide pharmaceutical scientists the opportunity to improve the drug-development process and enhance product quality. Pharmaceutical applications range from identification and quantification of drug crystallinity and polymorphism to mechanical strength assessment of tablets. This review article surveys the literature and evaluates the current and potential future characterization of pharmaceutical solids using refractive index measurements.