A Proposed Complete Methodology to Predict Gravity Flow Obstruction of Pharmaceutical Powders in Drug Product Manufacturing.

We present herein a comprehensive methodology to evaluate the risks involved in gravity-driven flow of pharmaceutical powders, including mass flow/funnel flow pattern, arch formation under active stress state (initial discharging) and passive stress state (following initial discharging), and rathole formation. Built on original theories underpinning the hopper design procedure, the methodology was modified to accommodate practices of pharmaceutical powder handling. All data required are generated from conventional ring shear tester. We applied the methodology to evaluate the powder flow risks during drug product manufacturing campaigns, where two powder blends with distinct flow behavior were discharged from a 200-L bin. The predicted results are in agreement with experiments where visual observations were possible, including the flow pattern, arch formation under active stress state, and rathole formation. One notable discovery is that pharmaceutical powders exhibit high risk of arch formation under active stress state, because of the exceeding major principal stress than the passive state. This phenomenon has been so far overlooked and the existing flow function-based classification cannot capture this risk. We propose, through this methodology, that reliable powder flow assessment should consider factors preventing flow (i.e., flow function), as well as factors facilitating flow (i.e., external stress).

In-line near infrared spectroscopy during freeze-drying as a tool to measure efficiency of hydrogen bond formation between protein and sugar, predictive of protein storage stability.

Sugars are often used as stabilizers of protein formulations during freeze-drying. However, not all sugars are equally suitable for this purpose. Using in-line near-infrared spectroscopy during freeze-drying, it is shown here that hydrogen bond formation during freeze-drying, under secondary drying conditions in particular, can be related to the preservation of the functionality and structure of proteins during storage. The disaccharide trehalose was best capable of forming hydrogen bonds with the model protein, lactate dehydrogenase, thereby stabilizing it, followed by the molecularly flexible oligosaccharide inulin 4kDa. The molecularly rigid oligo- and polysaccharides dextran 5kDa and 70kDa, respectively, formed the least amount of hydrogen bonds and provided least stabilization of the protein. It is concluded that smaller and molecularly more flexible sugars are less affected by steric hindrance, allowing them to form more hydrogen bonds with the protein, thereby stabilizing it better.

In situ protein secondary structure determination in ice: Raman spectroscopy-based process analytical tool for frozen storage of biopharmaceuticals.

A Raman spectroscopy-based method for in situ monitoring of secondary structural composition of proteins during frozen and thawed storage was developed. A set of reference proteins with different α-helix and ß-sheet compositions was used for calibration and validation in a chemometric approach. Reference secondary structures were quantified with circular dichroism spectroscopy in the liquid state. Partial least squares regression models were established that enable estimation of secondary structure content from Raman spectra. Quantitative secondary structure determination in ice was accomplished for the first time and correlation with existing (qualitative) protein structural data from the frozen state was achieved. The method can be used in the presence of common stabilizing agents and is applicable in an industrial freezer setup. Raman spectroscopy represents a powerful, noninvasive, and flexibly applicable tool for protein stability monitoring during frozen storage.

Raman model development for the protein conformational state classification in different freeze-dried formulations.

The aim of this work is to build a multivariate calibration (MVC) model from Raman spectra for the prediction of the protein conformational state class (i.e. native-like or non-native) in different freeze-dried pharmaceutical formulations of a model protein lactate dehydrogenase (LDH). As this model would be intended to facilitate and better understand formulation and process development, it should allow acceptable classification performance despite variations in formulation type and batch. Therefore, it was attempted to (1) find which factors interfere the Raman spectra, (2) understand them, and (3) make the MVC model robust for them. A variance analysis within the Raman spectral data space identified significant spectral background variations among certain formulation types and batches in the studied samples. Raw material (i.e. LDH) batch variability and the presence of a Maillard reaction in formulations were the main reasons for this. We demonstrate the successful use of both exhaustive calibration and external parameter orthogonalization (EPO) pre-processing for making the Raman classification model more robust for the expected spectral interferences.

Exploration and classification of chromatographic fingerprints as additional tool for identification and quality control of several Artemisia species.

The World Health Organization accepts chromatographic fingerprints as a tool for identification and quality control of herbal medicines. This is the first study in which the distinction, identification and quality control of four different Artemisia species, i.e. Artemisia vulgaris, A. absinthium, A. annua and A. capillaris samples, is performed based on the evaluation of entire chromatographic fingerprint profiles developed with identical experimental conditions. High-Performance Liquid Chromatography (HPLC) with Diode Array Detection (DAD) was used to develop the fingerprints. Application of factorial designs leads to methanol/water (80:20 (v/v)) as the best extraction solvent for the pulverised plant material and to a shaking bath for 30 min as extraction method. Further, so-called screening, optimisation and fine-tuning phases were performed during fingerprint development. Most information about the different Artemisia species, i.e. the highest number of separated peaks in the fingerprint, was acquired on four coupled Chromolith columns (100 mm × 4.6 mm I.D.). Trifluoroacetic acid 0.05% (v/v) was used as mobile-phase additive in a stepwise linear methanol/water gradient, i.e. 5, 34, 41, 72 and 95% (v/v) methanol at 0, 9, 30, 44 and 51 min, where the last mobile phase composition was kept isocratic till 60 min. One detection wavelength was selected to perform data analysis. The lowest similarity between the fingerprints of the four species was present at 214 nm. The HPLC/DAD method was applied on 199 herbal samples of the four Artemisia species, resulting in 357 fingerprints. The within- and between-day variation of the entire method, as well as the quality control fingerprints obtained during routine analysis, were found acceptable. The distinction of these Artemisia species was evaluated based on the entire chromatographic profiles, developed by a shared method, and visualised in score plots by means of the Principal Component Analysis (PCA) exploratory data-analysis technique. Samples of different quality could be indicated on the score plots. No multi-component analysis was required to reach the goal. Furthermore, differences related to the origin of some of the not-certified samples were shown. The importance of the specific herbal part used for its identification was also presented. In addition, no differences were observed among fingerprints of lyophilised or conditioned-air dried samples. Finally, a classification technique, Soft Independent Modelling by Class Analogy (SIMCA), was successfully evaluated as identification technique for unknown samples. Six additional Artemisia species (29 herbal samples) were identified as not belonging to any of the four modelled classes. The developed chromatographic fingerprints and the evaluation of the entire profiles provide an added value to the distinction, identification and quality control of the simultaneously investigated Artemisia species.

Near-infrared spectroscopic evaluation of lyophilized viral vaccine formulations.

This article examines the applicability of near-infrared spectroscopy (NIRS) to evaluate the virus state in a freeze-dried live, attenuated vaccine formulation. Therefore, this formulation was freeze-dried using different virus volumes and after applying different pre-freeze-drying virus treatments (resulting in different virus states): (i) as used in the commercial formulation; (ii) without antigen (placebo); (iii) concentrated via a centrifugal filter device; and (iv) stressed by 96 h exposure to room temperature. Each freeze-dried product was measured directly after freeze-drying with NIR spectroscopy and the spectra were analyzed using principal component analysis (PCA). Herewith, two NIR spectral regions were evaluated: (i) the 7300-4000 cm(-1) region containing the amide A/II band which might reflect information on the coated proteins of freeze-dried live, attenuated viruses; and (ii) the C-H vibration overtone regions (10,000-7500 and 6340-5500 cm(-1) ) which might supply information on the lipid layer surrounding the freeze-dried live, attenuated viruses. The different pre-freeze-drying treated live, attenuated virus formulations (different virus states and virus volumes) resulted in different clusters in the scores plots resulting from the PCA of the collected NIR spectra. Secondly, partial least squares discriminant analysis models (PLS-DA) were developed and evaluated, allowing classification of the freeze-dried formulations according to virus pretreatment. The results of this study suggest the applicability of NIR spectroscopy for evaluating live, attenuated vaccine formulations with respect to their virus pretreatment and virus volume.

Particle swarm optimization and genetic algorithm as feature selection techniques for the QSAR modeling of imidazo[1,5-a]pyrido[3,2-e]pyrazines, inhibitors of phosphodiesterase 10A.

Quantitative structure-activity relationship (QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization (PSO) and genetic algorithm (GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression (MLR)] and non-linear [locally weighted regression (LWR) based on both Euclidean (E) and Mahalanobis (M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50 . The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction (RMSEP) for the test sets obtained by PSO/MLR, GA/MLR, PSO/LWRE, PSO/LWRM, GA/LWRE, and GA/LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear.

Raman spectroscopy and multivariate analysis for the rapid discrimination between native-like and non-native states in freeze-dried protein formulations.

This study investigates whether Raman spectroscopy combined with multivariate analysis (MVA) enables a rapid and direct differentiation between two classes of conformational states, i.e., native-like and non-native proteins, in freeze-dried formulations. A data set comprising of 99 spectra, both from native-like and various types of non-native freeze-dried protein formulations, was obtained by freeze-drying lactate dehydrogenase (LDH) as model protein under various conditions. Changes in the secondary structure in the solid freeze-dried proteins were determined through visual interpretation of the blank corrected second derivative amide I band in the ATR-FTIR spectra (further called FTIR spectra) and served as an independent reference to assign class labels. Exploratory analysis and supervised classification, using Principal Components Analysis (PCA) and Partial Least Squares - Linear Discriminant Analysis (PLS-LDA), respectively, revealed that Raman spectroscopy is with 95% accuracy able to correctly discriminate between native-like and non-native states in the tested freeze-dried LDH formulations. Backbone (i.e., amide III) and side chain sensitive spectral regions proved important for making the discrimination between both classes. As discrimination was not influenced by the spectral signals from the tested excipients, there was no need for blank corrections. The Raman model may allow direct and automated analysis of the investigated quality attribute, opening possibilities for a real time and in-line quality indication as a future step. However, the sensitivity of the method should be further investigated and where possible improved.

Robust calibrations on reduced sample sets for API content prediction in tablets: definition of a cost-effective NIR model development strategy.

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.

Poliovirus separation from cell extracts using capillary electrophoresis: potential use in vaccine production and control?

Rapid assessment of the concentration of virus particles in a given sample remains a challenge. Modern separation methods, such as capillary electrophoresis, were proposed recently to study viruses and viral infection or to separate and characterize viral vaccines in a time-efficient manner. Even though capillary electrophoresis is much more rapid than traditional virological methods and has the advantages of automation, increased precision and reliability, it has the drawback of reduced sensitivity for low concentrations. A sensitivity improvement is then necessary in many cases for a successful application. However, to date, only highly purified viral samples were examined using capillary electrophoresis. The injection of larger sample volumes, followed by intra-capillary concentration, was used in this study for cell extracts. Poliovirus was successfully detected rapidly, without any laborious staining procedures and incubation times. The method is simple, fast, automatic, requires only minute amounts of samples and reagents, and no expensive dyes or biological reagents. Additionally, the method showed a potential for monitoring the viral load during growth and purification, with obvious prospects for the optimization of the variable and time-consuming virus propagation procedures. The results of this study provide a potential basis for the development of routine methods for viral particles analysis, irrespective of their infective properties. In the future, the capillary electrophoresis test could help study the relationship between the intact poliovirus particles and the D-antigenic properties of a viral suspension, or could represent a supplementary or alternative test for virus concentration and D-antigen assays during vaccine production.

Near-infrared spectroscopy for in-line monitoring of protein unfolding and its interactions with lyoprotectants during freeze-drying.

This work presents near-infrared spectroscopy (NIRS) as an in-line process analyzer for monitoring protein unfolding and protein-lyoprotectant hydrogen bond interactions during freeze-drying. By implementing a noncontact NIR probe in the freeze-drying chamber, spectra of formulations containing a model protein immunoglobulin G (IgG) were collected each process minute. When sublimation was completed in the cake region illuminated by the NIR probe, the frequency of the amide A/II band (near 4850 cm(-1)) was monitored as a function of water elimination. These two features were well correlated during protein dehydration in the absence of protein unfolding (desired process course), whereas consistent deviations from this trend to higher amide A/II frequencies were shown to be related to protein unfolding. In formulations with increased sucrose concentrations, the markedly decreased amide A/II frequencies seen immediately after sublimation indicated an increased extent of hydrogen bond interaction between the protein's backbone and surrounding molecules. At the end of drying, there was evidence of nearly complete water substitution for formulations with 1%, 5%, and 10% sucrose. The presented approach shows promising perspectives for early fault detection of protein unfolding and for obtaining mechanistic process information on actions of lyoprotectants.

Rational use of stacking principles for signal enhancement in capillary electrophoretic separations of poliovirus samples.

The use of an earlier developed capillary electrophoresis (CE) method, either to investigate poliovirus (PV) samples with a low viral-purity level or to study the less abundant sub-viral particles, revealed the necessity for an intra-column signal enhancement strategy. Although intra-column signal enhancement is a very popular approach to assay small molecules, it is less straightforward for the analysis of biological macromolecules or particles. A reason could be that, for a proper signal enhancement approach, these samples have to be thoroughly studied to understand the factors affecting the separation process. For the investigated PV samples, a screening design revealed that injecting larger sample plugs significantly enhanced the analytical signal, but also significantly decreased the separation efficiency. A subsequently executed central composite design determined the largest sample plug that can be injected without compromising the separation. Finally, the sample dilution and the length of the injected plug were used for tuning the intensity of the analytical response. Two combinations of sample dilution and injected plug size, at extreme values, were investigated in detail to define the best procedure for PV analysis using CE. In both situations, PV was effectively separated and quantified in rather complex samples, showing a good repeatability, an acceptable linearity for the PV particles and a decreased limit of detection in comparison with the existing method. In conclusion, intra-column signal enhancement can be successfully applied for viral suspensions, extending the applicability of CE methods to samples with lower virus concentrations, and/or allowing a significant reduction in the minimum required volume of sample. For PV samples, 5µl of sample is necessary instead of the previous 20µl, while the analytical signal was enhanced up to 14 times. The results of this study can provide a basis for the development of routine CE methods for viral particle analysis, especially when rational and reproducible signal enhancement is required.

Emerging analytical separation techniques with high-throughput potential for pharmaceutical analysis, part II: Novel chromatographic modes.

In this review paper, the high-throughput potential of some novel chromatographic modes is surveyed. The modes are Hydrophilic Interaction Liquid Chromatography (HILIC), Supercritical Fluid Chromatography (SFC), and Polar Organic Solvent Chromatography (POSC). Their high throughput potential will be discussed in three domains, i.e. drug discovery, bio-analysis in clinical drug development, and quality control (QC) testing, and is illustrated with some examples.

Emerging analytical separation techniques with high throughput potential for pharmaceutical analysis, part I: Stationary phase and instrumental developments in LC.

In recent years, a trend of change has been observed within pharmaceutical industry. As modern drug discovery has reached a remarkable level of complexity and drugs need to be discovered, developed and produced against strict timelines and within cost- and regulatory constraints, industry seeks "lean" solutions to increase productivity. Among them, increasing the sample throughput of the ever-growing number of necessary (routine) analyses has become a popular target to cut precious time. For the last thirty years, High-Performance Liquid Chromatography (HPLC) has been the leading technology when it comes to various analyses in pharmaceutical industry; however, its necessity of serial analyses taking typically 10-45 min has been a sample throughput-limiting barrier. Lately, the fundamentals of HPLC have been exploited to raise new technologies that can speed up analyses to ground breaking limits, without compromising separation efficiency. This paper reviews some promising technologies, i.e. totally porous sub-2microm particles accompanied by pressures up to 1000 bar (Ultra-Performance Liquid Chromatography or UPLC), fused-core particle technology, monolithic supports and High Temperature Liquid Chromatography (HTLC), having the potential to take LC to the next level in pharmaceutical industry. As each analytical method has its own demands, the advances of the above technologies are discussed for different applications in pharmaceutical analysis where high-throughput analysis can be meaningful, i.e. in a drug discovery and development setting, and in quality operations. Both chemical and biological pharmaceuticals are considered. We discuss the perspectives of these technologies and their realizations up to now in high-throughput pharmaceutical analysis.

Improving the capillary electrophoretic analysis of poliovirus using a Plackett-Burman design.

Separation techniques may offer interesting alternatives to classical virological techniques both for fundamental research purposes and for vaccine manufacturing. A capillary electrophoretic method for the analysis of the poliovirus was developed based on conditions for the human rhinovirus taken from literature. The method was optimized using a 12-experiment Plackett-Burman design, applied in order to examine simultaneously the effects of eight factors on responses such as, mobility of the electroosmotic flow, effective mobility of the poliovirus, analysis time and resolution between the virus peak and a system peak. The proposed method manages to perform an acceptable separation of poliovirus particles using a 50 mM borate buffer with 25 mM SDS, in an uncoated fused-silica capillary upon application of 10 kV at 30 degrees C. The linearity of the proposed method was investigated for a range of poliovirus dilutions up to 140 microg/mL.