Sustained release dosage forms dissolution behavior prediction: A study of matrix tablets using NIR spectroscopy

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets

Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets.

Regulatory Experience with Continuous Manufacturing and Real Time Release Testing for Dissolution in New Drug Applications

Regulatory submissions involving the use of continuous manufacturing (CM)1 and/or real-time release testing for dissolution (RTRT-D) to the United States Food and Drug Administration (FDA) were identified spanning several years. The submissions were for orally administered IR tablets and they were examined from a biopharmaceutics perspective to highlight commonly occurring issues which the FDA's assessment teams identified with the proposed use of CM and/or RTRT-D. The objective of this study is to provide recommendations for best practices that will help advance the field by (i) generating greater opportunities for (drug) Applicants2 to benefit from the implementation of advanced manufacturing approaches, (ii) improving high quality regulatory submissions involving CM and RTRT-D, and thus (iii) lessening the regulatory review burden. This paper has identified several common deficiencies, such as inadequate strategies for stratified sampling of drug product (DP) units, inappropriate design of experiments (DoE), inability of the proposed RTRT-D model to account for dissolution variability and to predict the entire time course of dissolution, insufficient documentation, and unsuitable in vitro dissolution methods.

Imaging techniques for studying solid dosage formulation: Principles and applications

Classic methods for evaluating the disintegration and dissolution kinetics of solid dosage forms are no longer sufficient to meet the growing demands in the pharmaceutical field. Hence, scientists have turned to imaging techniques and computer technology to develop innovative visualization methods. These methods allow for a visual understanding of the disintegration or dissolution process and offer valuable insights into the drug release kinetics. This article aims to provide an overview of the commonly used imaging techniques and their applications in studying the disintegration or dissolution of solid dosage forms. Therefore, imaging presents a novel and alternative approach to understanding the mechanisms of disintegration and dissolution in the formulation study of solid dosages.

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy

Selective laser sintering (SLS) 3D printing is a revolutionary 3D printing technology that has been found capable of creating drug products with varied release profiles by changing the laser scanning speed. Here, SLS 3D printed formulations (printlets) loaded with a narrow therapeutic index drug (theophylline) were produced using SLS 3D printing at varying laser scanning speeds (100-180 mm/s). The use of reflectance Fourier Transform - Near Infrared (FT-NIR) spectroscopy was evaluated as a non-destructive approach to predicting 3D printed tablet density and drug release at 2 h and 4 h. The printed drug products formulated with a higher laser speed exhibited an accelerated drug release and reduced density compared with the slower laser scanning speeds. Univariate calibration models were developed based on a baseline shift in the spectra in the third overtone region upon changing physical properties. For density prediction, the developed univariate model had high linearity (R² value = 0.9335) and accuracy (error < 0.029 mg/mm³). For drug release prediction at 2 h and 4 h, the developed univariate models demonstrated a linear correlation (R² values of 0.9383 and 0.9167, respectively) and accuracy (error < 4.4%). The predicted vs. actual dissolution profiles were found to be statistically similar (f₂ > 50) for all of the test printlets. Overall, this article demonstrates the feasibility of SLS 3D printing to produce drug products containing a narrow therapeutic index drug across a range of drug release profiles, as well as the potential for FT-NIR spectroscopy to predict the physical characteristics of SLS 3D printed drug products (drug release and density) as a non-destructive quality control method at the point-of-care.

Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review

Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Prediction of Dissolution of Sustained Release Coated Ciprofloxacin Beads Using Near-infrared Spectroscopy and Process Parameters: a Data Fusion Approach

The aim of the work is to develop a data fusion model using near-infrared (NIR) and process parameters for the predictions of drug dissolution from controlled release multiparticulate beads. Using a design of experiments, ciprofloxacin-coated beads were manufactured and critical process parameters such as air volume, product temperature, curing temperature, and curing time were measured; environmental humidity was monitored using a Pyrobuttons®. The NIR spectra were decomposed using principal component analysis (PCA). The PCA scores were fused with process measurements and all variables were autoscaled. The autoscaled variables were regressed against measured dissolution data at 1 h and 2 h time points; the PLS regression used quadratic and cross terms. The NIR spectra only model using data collected at the end of bead curing generated a PLS model using 5 latent variables with R² equal to 0.245 and 0.299 and RMSECV 13.23 and 13.12 for the 1 h and 2 h dissolution time points, respectively. The low R² and high root mean square error of cross validation (RMSECV) values indicate that NIR spectra alone were insufficient to model the drug release. Similar results were obtained for NIR model using data collected at the end of spraying phase. Models with fused spectral and process data yielded better prediction with R² above 0.88 and RMSECV less than 5% for the 1 h and 2 h dissolution time points. The data fusion model predicted dissolution profiles with an error less than 10%.

Highly Soluble Drugs Directly Granulated by Water Dispersions of Insoluble Eudragit® Polymers as a Part of Hypromellose K100M Matrix Systems

The aim of the present study was to investigate the suitability of insoluble Eudragit® water dispersions (NE, NM, RL, and RS) for direct high-shear granulation of very soluble levetiracetam in order to decrease its burst effect from HPMC K100M matrices. The process characteristics, ss-NMR analysis, in vitro dissolution behavior, drug release mechanism and kinetics, texture profile analysis of the gel layer, and PCA analysis were explored. An application of water dispersions directly on levetiracetam was feasible only in a multistep process. All prepared formulations exhibited a 12-hour sustained release profile characterized by a reduced burst effect in a concentration-dependent manner. No effect on swelling extent of HPMC K100M was observed in the presence of Eudragit®. Contrary, higher rigidity of formed gel layer was observed using combination of HPMC and Eudragit®. Not only the type and concentration of Eudragit®, but also the presence of the surfactant in water dispersions played a key role in the dissolution characteristics. The dissolution profile close to zero-order kinetic was achieved from the sample containing levetiracetam directly granulated by the water dispersion of Eudragit® NE (5% of solid polymer per tablet) with a relatively high amount of surfactant nonoxynol 100 (1.5%). The initial burst release of drug was reduced to 8.04% in 30 min (a 64.2% decrease) while the total amount of the released drug was retained (97.02%).

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing

This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

Investigation of Dissolution Behavior HPMC/Eudragit®/Magnesium Aluminometasilicate Oral Matrices Based on NMR Solid-State Spectroscopy and Dynamic Characteristics of Gel Layer

Burst drug release is often considered a negative phenomenon resulting in unexpected toxicity or tissue irritation. Optimal release of a highly soluble active pharmaceutical ingredient (API) from hypromellose (HPMC) matrices is technologically impossible; therefore, a combination of polymers is required for burst effect reduction. Promising variant could be seen in combination of HPMC and insoluble Eudragits^® as water dispersions. These can be applied only on API/insoluble filler mixture as over-wetting prevention. The main hurdle is a limited water absorption capacity (WAC) of filler. Therefore, the object of this study was to investigate the dissolution behavior of levetiracetam from HPMC/Eudragit^®NE matrices using magnesium aluminometasilicate (Neusilin^® US2) as filler with excellent WAC. Part of this study was also to assess influence of thermal treatment on quality parameters of matrices. The use of Neusilin^® allowed the application of Eudragit^® dispersion to API/Neusilin^® mixture in one step during high-shear wet granulation. HPMC was added extragranularly. Obtained matrices were investigated for qualitative characteristics, NMR solid-state spectroscopy (ssNMR), gel layer dynamic parameters, SEM, and principal component analysis (PCA). Decrease in burst effect (max. of 33.6%) and dissolution rate, increase in fitting to zero-order kinetics, and paradoxical reduction in gel layer thickness were observed with rising Eudragit^® NE concentration. The explanation was done by ssNMR, which clearly showed a significant reduction of the API particle size (150-500 nm) in granules as effect of surfactant present in dispersion in dependence on Eudragit^®NE amount. This change in API particle size resulted in a significantly larger interface between these two entities. Based on ANOVA and PCA, thermal treatment was not revealed as a useful procedure for this system.

Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries

Near-infrared (874-1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972-1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (R_p²) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

Dissolution assessment of allopurinol immediate release tablets by near infrared spectroscopy

The purpose of this study was to develop a NIR spectroscopic method for assessment of drug dissolution from allopurinol immediate release tablets. Thirty three different batches of allopurinol immediate release tablets containing constant amount of the active ingredient, but varying in excipients content and physical properties were introduced in a PLS calibration model. Correlating allopurinol dissolution reference values measured by the routinely used UV/Vis method, with the data extracted from the NIR spectra, values of correlation coefficient, bias, slope, residual prediction determination and root mean square error of prediction (0.9632, 0.328%, 1.001, 3.58, 3.75%) were evaluated. The obtained values implied that the NIR diffuse reflectance spectroscopy could serve as a faster and simpler alternative to the conventional dissolution procedure, even for the tablets with a very fast dissolution rate (>85% in 15minutes). Apart from the possibility of prediction of the allopurinol dissolution rate, the other multivariate technique, PCA, provided additional data on the non-chemical characteristics of the product, which could not be obtained from the reference dissolution values. Analysis on an independent set of samples confirmed that a difference between the UV/Vis reference method and the proposed NIR method was not significant. According to the presented results, the proposed NIR method may be suitable for practical application in routine analysis and for continuously monitoring the product's chemical and physical properties responsible for expected quality.

Influence of pH Modulation on Dynamic Behavior of Gel Layer and Release of Weakly Basic Drug from HPMC/Wax Matrices, Controlled by Acidic Modifiers Evaluated by Multivariate Data Analysis

The solubility of weakly basic drugs in passage through gastrointestinal tract leads to their pH-dependent release from extended release formulations and to lower drug absorption and bioavailability. The aim of this study was to modulate the micro-environmental pH of hypromellose/montanglycol wax matrices and to observe its influence on the release of weakly basic drug verapamil hydrochloride (VH) with a pH-dependent solubility with respect to gel layer formation and its dynamics. For this study, malic and succinic acids differing in their solubility and pKa were selected as pH modifiers. The dissolution studies were performed by the method of changing pH. Within the same conditions, pH, thickness, and penetration force of the gel layer were measured as well. From the PCA sub-model, it is evident that a higher acid concentration ensured lower gel pH and conditions for higher drug solubility, thus creating larger gel layer with smaller rigidity, resulting in higher VH release during the dissolution test. Incorporation of stronger and more soluble malic acid (100 mg/tablet) created the most acidic and the thickest gel layer through which a total of 74% of VH was released. Despite having lower strength and solubility, matrices containing succinic acid (100 mg/tablet) released a comparable 71% of VH in a manner close to zero-order kinetics. The thinner and less rigid gel layers of the succinic acid matrices allowed an even slightly faster VH release at pH 6.8 than from matrices containing malic acid. Thus acid solubility is more parametrically significant than acid pKa for drug release at pH 6.8.

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R(2)) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

Simultaneous determination of the impurity and radial tensile strength of reduced glutathione tablets by a high selective NIR-PLS method

This paper establishes a high-throughput and high selective method to determine the impurity named oxidized glutathione (GSSG) and radial tensile strength (RTS) of reduced glutathione (GSH) tablets based on near infrared (NIR) spectroscopy and partial least squares (PLS). In order to build and evaluate the calibration models, the NIR diffuse reflectance spectra (DRS) and transmittance spectra (TS) for 330 GSH tablets were accurately measured by using the optimized parameter values. For analyzing GSSG or RTS of GSH tablets, the NIR-DRS or NIR-TS were selected, subdivided reasonably into calibration and prediction sets, and processed appropriately with chemometric techniques. After selecting spectral sub-ranges and neglecting spectrum outliers, the PLS calibration models were built and the factor numbers were optimized. Then, the PLS models were evaluated by the root mean square errors of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP), and by the correlation coefficients of calibration (R(c)) and prediction (R(p)). The results indicate that the proposed models have good performances. It is thus clear that the NIR-PLS can simultaneously, selectively, nondestructively and rapidly analyze the GSSG and RTS of GSH tablets, although the contents of GSSG impurity were quite low while those of GSH active pharmaceutical ingredient (API) quite high. This strategy can be an important complement to the common NIR methods used in the on-line analysis of API in pharmaceutical preparations. And this work expands the NIR applications in the high-throughput and extraordinarily selective analysis.

Comparison of several supervised pattern recognition techniques for detecting additive methamidophos in rotenone preparation by near-infrared spectroscopy

In this paper, different supervised pattern recognition methods have been applied to detect the manually additive methamidophos in rotenone preparation. The aim of this paper was to examine the performances of different supervised pattern recognition techniques: soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), artificial neutral networks (ANN), and support vector machine (SVM). The results obtained show that SVM is the most effective techniques with 100.0% classification accuracy followed by ANN, PLS-DA and with the accuracy of 97.5% and 93.3% respectively while SIMCA yields the poorest result of 85.8%. We hope that the results obtained in this study will help both further chemometric investigations and investigations in the sphere of applied vibrational spectroscopy of sophisticated multicomponent systems. Furthermore, the use of portable instrument and satisfactory classification also indicated the possibility of detecting illicit-addition at scene by near-infrared (NIR) spectroscopy which makes a great sense in pesticide quality control.

Application of near-infrared spectroscopy combined with multivariate analysis in monitoring of crude heparin purification process

Ion-exchange chromatography is a widely used purification technology in the heparin manufacturing process. To improve the efficiency and understand the process directly, a rapid and equally precise method needs to be developed to measure heparin concentration in chromatography process. Here, two robust partial least squares regression (PLS-R) models were established for quantification of heparin based on the near-infrared (NIR) spectroscopy with 80 samples of adsorption process and 76 samples of elution process. Several variables selection algorithms, including correlation coefficient method, successive projection algorithm (SPA) and interval partial least squares (iPLSs), were performed to remove non-informative variables. The results showed that the correlation coefficient of validation (Rp) and the residual predictive deviation (RPD) corresponded to 0.957 and 3.4472 for adsorption process, 0.968 and 3.9849 for elution process, respectively. The approach was found considerable potential for real-time monitoring the heparin concentration of chromatography process.

High-throughput NIR-chemometric methods for determination of drug content and pharmaceutical properties of indapamide tablets

This paper describes the development, validation and application of NIR-chemometric methods for API content and pharmaceutical characterization (disintegration time and crushing strength) of indapamide intact tablets. Development of the method for chemical characterization was performed on samples corresponding to 80, 90, 100, 110 and 120% of indapamide content and for pharmaceutical characterization on samples prepared at nine different compression forces (covering the interval 7-45 kN). NIR spectra of prepared tablets were recorded in transmission mode, and partial least-squares followed by leave-one-out cross-validation were used to develop models for the prediction of the drug content and the pharmaceutical properties of tablets. All developed models were validated in terms of trueness, precision and accuracy. No statistical differences were found between results predicted by NIR-chemometric methods and the ones determined by reference methods. Therefore, the developed NIR-chemometric methods meet the requirements of a high-throughput method for the determination of drug content, pharmaceutical properties of indapamide tablets.

Rapid evaluation for heterogeneities in monoclonal antibodies by liquid chromatography/mass spectrometry with a column-switching system

The development of therapeutic antibodies has grown over the last several years. Most of the recombinant monoclonal antibodies (mAbs) produced by mammalian cells are glycoproteins. Glycosylation of the mAbs can be associated with effector functions, such as antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity, as well as immunogenicity and clearance. Thus, mAb glycan heterogeneity is a significant characteristic associated with the safety and efficacy of the products. Therefore, glycan heterogeneity should be evaluated during research and development (R&D) and during development of mAbs manufacturing processes to identify the process parameters that affect glycan heterogeneity and to enhance understanding of the manufacturing process. There is an increasing need for a rapid, easy, and automated evaluation method for glycan heterogeneity. Liquid chromatography/mass spectrometry (LC/MS) is a method that can be used to analyze glycoforms. LC/MS is marked by the ability to measure the oligosaccharide composition of each glycoform, whereas other general methods, such as capillary electrophoresis, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and ion-exchange chromatography, cannot. However, a laborious off-line purification of mAbs is required to evaluate glycan heterogeneities. In this study, we demonstrate the use of a rapid, easy, and automated evaluation system for mAb glycoforms by LC/MS. This LC/MS system uses a column-switching system equipped with 2 columns, a protein A affinity column and a reversed-phase column (desalting column). We devised 2 column-switching systems: one that targeted intact mAbs (system 1) and one that targeted the light and heavy chains of the mAbs (system 2). Our results show that the proposed systems are applicable as a tool to evaluate the glycoforms in several situations, including the research, development, and production processes of mAbs. Additionally, we hope that our systems are useful as process analytical technology (PAT) for molecular heterogeneities containing glycoforms of mAbs in implementation of quality by design (QbD).

In-line prediction of drug release profiles for pH-sensitive coated pellets

A new method for the prediction of the drug release profiles during a running pellet coating process from in-line near infrared (NIR) measurements has been developed. The NIR spectra were acquired during a manufacturing process through an immersion probe. These spectra reflect the coating thickness that is inherently connected with the drug release. Pellets sampled at nine process time points from thirteen designed laboratory-scale coating batches were subjected to the dissolution testing. In the case of the pH-sensitive Acryl-EZE coating the drug release kinetics for the acidic medium has a sigmoid form with a pronounced induction period that tends to grow along with the coating thickness. In this work the autocatalytic model adopted from the chemical kinetics has been successfully applied to describe the drug release. A generalized interpretation of the kinetic constants in terms of the process and product parameters has been suggested. A combination of the kinetic model with the multivariate Partial Least Squares (PLS) regression enabled prediction of the release profiles from the process NIR data. The method can be used to monitor the final pellet quality in the course of a coating process.