A new PAT/QbD approach for the determination of blend homogeneity: Combination of on-line NIRS analysis with PC Scores Distance Analysis (PC-SDA)

Towards a real-time release of blends and tablets using NIR and Raman spectroscopy at commercial scales

Near Infrared and Raman spectroscopy-based Process Analytical Technology tools were used for monitoring blend uniformity (BU) and content uniformity (CU) for solid oral formulations. A quantitative Partial Least Square model was developed to monitor BU as real-time release testing at a commercial scale. The model having the R2, and root mean square error of 0.9724 and 2.2047, respectively can predict the target concentration of 100% with a 95% confidence interval of 101.85-102.68% even after one year. The tablets from the same blends were investigated for CU using NIR and Raman techniques both in reflection and transmission mode. Raman reflection technique was found to be the best and the PLS model was developed using tablets compressed at different concentrations, hardness, and speed. The model with R2 and RMSE of 0.9766 and 1.9259, respectively was used for the quantification of CU. Both the BU and CU models were validated for accuracy, precision, specificity, linearity, and robustness. The accuracy was proved against the HPLC method with a relative standard deviation of less than 3%. The equivalency for BU by NIR and CU by Raman was evaluated using Schuirmann's Two One-sided tests and found equivalent to HPLC within a 2% acceptable limit.

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.

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

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.

Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data

Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments.

Environmentally-friendly technology for rapid identification and quantification of emerging pollutants from wastewater using infrared spectroscopy

The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm^-1 to 14,000 cm^-1. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, β-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.

Optimization of PLS modeling parameters via quality by design concept for Gardenia jasminoides Ellis using online NIR sensor

This paper discussed the process parameters optimization of partial least-square (PLS) modeling according to quality by design (QbD) concept. D-optimal design and online near-infrared (NIR) sensor were proposed to analysis the Geniposide in Gardenia jasminoides Ellis using above process parameters to achieve robustness PLS model. Four critical model parameters (CMPs) were identified to construct a D-optimal design, which included the selection of sample set, spectra pre-processing, latent variables and variable selection methods. NIR sensor dataset was obtained under a pilot scale system. The D-optimal design optimization strategy resulted in a robust PLS model with the optimal parameters, 1/2 samples for calibration sets through Baseline spectra pre-processing with SiPLS-selecting variables under 8 factors. The critical evaluation attributes (CEAs) of PLS model were recommended as follows: the RMSEC and R_cal² of the calibration set were 0.005901 and 0.9983. The RMSEP and R_pre² of the validation set were 0.02002 and 0.9845. The multivariate detection limit (MDL) was 1.143 × 10^-3. Therefore, design space of CMPs which affected CEAs of PLS model was established. The result demonstrated that the proposed method was beneficial for the robustness of PLS model, which also showed a significant guideline for the design and development of PLS model.

Process Analytical Quality Control of Tailored Drug Release Formulation Prepared via Hot-Melt Extrusion Technology

The objective of the present study was to compare the influence of Eudragit^® RS PO and RL PO blends on the release of water-soluble and insoluble drugs from hot-melt extruded formulations. In addition, we aimed to evaluate drug content uniformity and distribution by Fourier transform-infrared (FT-IR) chemical imaging. Theophylline (TP) and carbamazepine (CBZ) were selected as the water-soluble and insoluble model drugs, respectively. Eudragit^® RS PO and RL PO were selected as the polymeric matrices. FT-IR chemical imaging clearly demonstrated the content uniformity and distribution for both drugs in the extrudates, which was confirmed by HPLC. Increasing the ratio of Eudragit^® RL PO led to an increase in the in vitro drug release, whereas an increase in the ratio of Eudragit^® RS PO sustained the drug release for up to 12 h. The hot-melt extrusion of TP and CBZ with varying ratios of Eudragit^® RS PO and RL PO can be employed to tailor the drug release profiles. In this study, we demonstrated, for the first time, the use of FT-IR chemical imaging as a process analytical technique to determine the drug content uniformity and distribution. Our data correlated well with the results of HPLC analysis in the study of tailored drug release from the prepared hot-melt extruded formulation.

Blend uniformity evaluation during continuous mixing in a twin screw granulator by in-line NIR using a moving F-test

This study focuses on the twin screw granulator of a continuous from-powder-to-tablet production line. Whereas powder dosing into the granulation unit is possible from a container of preblended material, a truly continuous process uses several feeders (each one dosing an individual ingredient) and relies on a continuous blending step prior to granulation. The aim of the current study was to investigate the in-line blending capacity of this twin screw granulator, equipped with conveying elements only. The feasibility of in-line NIR (SentroPAT, Sentronic GmbH, Dresden, Germany) spectroscopy for evaluating the blend uniformity of powders after the granulator was tested. Anhydrous theophylline was used as a tracer molecule and was blended with lactose monohydrate. Theophylline and lactose were both fed from a different feeder into the twin screw granulator barrel. Both homogeneous mixtures and mixing experiments with induced errors were investigated. The in-line spectroscopic analyses showed that the twin screw granulator is a useful tool for in-line blending in different conditions. The blend homogeneity was evaluated by means of a novel statistical method being the moving F-test method in which the variance between two blocks of collected NIR spectra is evaluated. The α- and β-error of the moving F-test are controlled by using the appropriate block size of spectra. The moving F-test method showed to be an appropriate calibration and maintenance free method for blend homogeneity evaluation during continuous mixing.

Detecting Blending End-Point Using Mean Squares Successive Difference Test and Near-Infrared Spectroscopy

An algorithm based on mean squares successive difference test applied to near-infrared and principal component analysis scores was developed to monitor and determine the blending profile and to assess the end-point in the statistical stabile phase. Model formulations consisting of an active compound (acetylsalicylic acid), together with microcrystalline cellulose and two grades of calcium carbonate with dramatically different particle shapes, were prepared. The formulation comprising angular-shaped calcium carbonate reached blending end-point slower when compared with the formulation comprising equant-shaped calcium carbonate. Utilizing the ring shear test, this distinction in end-point could be related to the difference in flowability of the formulations. On the basis of the two model formulations, a design of experiments was conducted to characterize the blending process by studying the effect of CaCO3 grades and fill level of the bin on blending end-point. Calcium carbonate grades, fill level, and their interaction were shown to have a significant impact on the blending process.

Quality by design for herbal drugs: a feedforward control strategy and an approach to define the acceptable ranges of critical quality attributes

INTRODUCTION

The concept of quality by design (QbD) has been widely accepted and applied in the pharmaceutical manufacturing industry. There are still two key issues to be addressed in the implementation of QbD for herbal drugs. The first issue is the quality variation of herbal raw materials and the second issue is the difficulty in defining the acceptable ranges of critical quality attributes (CQAs).

OBJECTIVE

To propose a feedforward control strategy and a method for defining the acceptable ranges of CQAs for the two issues.

METHODS

In the case study of the ethanol precipitation process of Danshen (Radix Salvia miltiorrhiza) injection, regression models linking input material attributes and process parameters to CQAs were built first and an optimisation model for calculating the best process parameters according to the input materials was established. Then, the feasible material space was defined and the acceptable ranges of CQAs for the previous process were determined.

RESULTS

In the case study, satisfactory regression models were built with cross-validated regression coefficients (Q(2) ) all above 91 %. The feedforward control strategy was applied successfully to compensate the quality variation of the input materials, which was able to control the CQAs in the 90-110 % ranges of the desired values. In addition, the feasible material space for the ethanol precipitation process was built successfully, which showed the acceptable ranges of the CQAs for the concentration process.

CONCLUSION

The proposed methodology can help to promote the implementation of QbD for herbal drugs.

In-line quantification of two active ingredients in a batch blending process by near-infrared spectroscopy: influence of physical presentation of the sample

The aim of this study was to apply near-infrared (NIR) spectroscopy to the simultaneous in-line monitoring of two active pharmaceutical ingredients (APIs) in a pharmaceutical batch blending process. The formulation under study consisted of a high load API (A1), one polymer, a second API (A2) and one lubricant. Additionally, the effects of the presentation of A1 on the spectral data were evaluated. For this purpose, the high load active was blended either as a cohesive powder or as a free flowing material. For improving the flow behavior of the high load active a melt-granulation (MG) step was performed. The NIR spectra of the high load API (A1) before and after MG showed that the polymer wavelength absorption band was the most affected, this wavelength range was also associated with the water band region. Thus, these frequencies carried information from the melt-granulation process and could be influenced by the water content. For the APIs quantification, independent partial least squares (PLS-1) models for each API were generated. Furthermore, a PLS-2 model was also developed for the simultaneous quantification of each API. The PLS models were used for the in-line blend uniformity monitoring of both APIs.

Towards better process understanding: chemometrics and multivariate measurements in manufacturing of solid dosage forms

The manufacturing of tablets involves many unit operations that possess multivariate and complex characteristics. The interactions between the material characteristics and process related variation are presently not comprehensively analyzed due to univariate detection methods. As a consequence, current best practice to control a typical process is to not allow process-related factors to vary i.e. lock the production parameters. The problem related to the lack of sufficient process understanding is still there: the variation within process and material properties is an intrinsic feature and cannot be compensated for with constant process parameters. Instead, a more comprehensive approach based on the use of multivariate tools for investigating processes should be applied. In the pharmaceutical field these methods are referred to as Process Analytical Technology (PAT) tools that aim to achieve a thorough understanding and control over the production process. PAT includes the frames for measurement as well as data analyzes and controlling for in-depth understanding, leading to more consistent and safer drug products with less batch rejections. In the optimal situation, by applying these techniques, destructive end-product testing could be avoided. In this paper the most prominent multivariate data analysis measuring tools within tablet manufacturing and basic research on operations are reviewed.

Use of near-infrared spectroscopy to quantify drug content on a continuous blending process: influence of mass flow and rotation speed variations

The aim of this study was to develop a quantitative Near-Infrared (NIR) method which monitors the homogeneity of a pharmaceutical formulation coming out of a continuous blender. For this purpose, a NIR diode array spectrometer with fast data acquisition was selected. Additionally, the dynamic aspects of a continuous blending process were studied; the results showed a well-defined cluster for the steady state, and the paths for the start-up and emptying stages were clearly identified. The end point of the start-up phase was detected by moving block of standard deviation, relative standard deviation, and principal component analysis. A partial least square (PLS) model was generated for the quantification of the drug, with a standard error of prediction of 0.2% m/m. The PLS model was successfully applied for monitoring the drug level at the outlet of the continuous blender. Furthermore, the PLS model was tested under different flow and stirring rates. Flow and stirring rate variations caused different powder flow dynamics, which were reflected on the NIR measurements. Therefore, the PLS model was sensitive to changes in mass flow and rotation speeds.