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

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Comparative study on the real-time monitoring of a fluid bed drying process of extruded granules using near-infrared spectroscopy and audible acoustic emission

The process of fluidized bed drying of granules was comparatively evaluated by on-line real-time measurements of granule moisture content (MC) using near-infrared spectroscopy (NIR) and audible acoustic emission (AAE). The extruded granules were prepared by kneading a powder blend containing lactose, starch, crystalline cellulose, and riboflavin, with water. The MC of the granules (while they were dried at 35 °C in a fluidized bed dryer) was monitored simultaneously with NIR and AAE. The prediction accuracy of the NIR and AAE using partial least squares (PLS) was verified by measuring MC of the granules. The best calibration models following NIR and AAE evaluations consisted of five latent variables with correlation coefficients of 1.000 and 0.998 and root mean square error of 0.259 and 0.615, respectively. As a result of external verification, the accuracy of MC analysis by AAE was slightly lower than that of NIR; however, it was still applicable in practice. Furthermore, the end point of fluidized bed drying process was automatically determined using the PLS discriminant analysis. From the above results, it can be concluded that the AAE-mediated granule drying process can be monitored with sufficient accuracy (compared with NIR).

Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis

Most drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer.

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.

Multivariate analysis for optimization and validation of the industrial tablet-manufacturing process

OBJECTIVE

This study aimed initially to optimize the industrial tablet-manufacturing process using multivariate analysis, and then to validate the model obtained. The study also provides a comprehensive review of the influence of different factors on relevant biopharmaceutical parameters.

SIGNIFICANCE

This is the first time multivariate analysis has been applied to such a broad set of industrial data to investigate the influence of starting materials and the tablet-manufacturing processes on drug dissolution.

METHODS

Partial least squares regression was retrospectively applied to the data obtained from 2 years production, to study the influence of 90 factors on dissolution of tablets that contained two active pharmaceutical ingredients. The model established was verified using the worst-case approach and process validation.

RESULTS

Croscarmellose sodium had the most significant influence on drug dissolution, with the next significant factors as sodium chloride and sodium glycolate content, settling volume, particle size, suspension pH, loss on drying, and maximum temperature during drying. Loss on drying of microcrystalline cellulose and specific surface area of magnesium stearate were also essential factors. Among the process parameters, auger speed during roller compaction, compression speed, and force feeder speed during tablet compression had significant impacts on the tablet dissolution rate. The multivariate model created satisfied the process validation.

CONCLUSIONS

This multivariate analysis is a useful tool to predict and optimize critical material attributes and process parameters. The variability of the materials can be successfully compensated for using various process parameters, to ensure consistent approved drug quality, to thus provide better patient care.

From Academia to Reality Check: A Theoretical Framework on the Use of Chemometric in Food Sciences

There is no doubt that the current knowledge in chemistry, biochemistry, biology, and mathematics have led to advances in our understanding about food and food systems. However, the so-called reductionist approach has dominated food research, hindering new developments and innovation in the field. In the last three decades, food science has moved into the digital and technological era, inducing several challenges resulting from the use of modern instrumental techniques, computing and algorithms incorporated to the exploration, mining, and description of data derived from this complexity. In this environment, food scientists need to be mindful of the issues (advantages and disadvantages) involved in the routine applications of chemometrics. The objective of this opinion paper is to give an overview of the key issues associated with the implementation of chemometrics in food research and development. Please note that specifics about the different methodologies and techniques are beyond the scope of this review.

Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.

Chemometrics-assisted solid-state characterization of pharmaceutically relevant materials. Polymorphic substances

Current regulations command to properly characterize pharmaceutically relevant solid systems. Chemometrics comprise a range of valuable tools, suitable to process large amounts of data and extract valuable information hidden in their structure. This review aims to detail the results of the fruitful association between analytical techniques and chemometrics methods, focusing on those which help to gain insight into the characteristics of drug polymorphism as an important aspect of the solid state of bulk drugs and drug products. Hence, the combination of Raman, terahertz, mid- and near- infrared spectroscopies, as well as instrumental signals resulting from X-ray powder diffraction, ¹³C solid state nuclear magnetic resonance spectroscopy and thermal methods with quali-and quantitative chemometrics methodologies are examined. The main issues reviewed, concerning pharmaceutical drug polymorphism, include the use of chemometrics-based approaches to perform polymorph classification and assignment of polymorphic identity, as well as the determination of given polymorphs in simple mixtures and complex systems. Aspects such as the solvation/desolvation of solids, phase transformation, crystallinity and the recrystallization from the amorphous state are also discussed. A brief perspective of the field for the next future is provided, based on the developments of the last decade and the current state of the art of analytical instrumentation and chemometrics methodologies.

The Future of Pharmaceutical Manufacturing Sciences

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.

Batch Statistical Process Monitoring Approach to a Cocrystallization Process

Cocrystals are defined as crystalline structures composed of two or more compounds that are solid at room temperature held together by noncovalent bonds. Their main advantages are the increase of solubility, bioavailability, permeability, stability, and at the same time retaining active pharmaceutical ingredient bioactivity. The cocrystallization between furosemide and nicotinamide by solvent evaporation was monitored on-line using near-infrared spectroscopy (NIRS) as a process analytical technology tool. The near-infrared spectra were analyzed using principal component analysis. Batch statistical process monitoring was used to create control charts to perceive the process trajectory and define control limits. Normal and non-normal operating condition batches were performed and monitored with NIRS. The use of NIRS associated with batch statistical process models allowed the detection of abnormal variations in critical process parameters, like the amount of solvent or amount of initial components present in the cocrystallization.

Vibrational spectroscopy and microspectroscopy analyzing qualitatively and quantitatively pharmaceutical hot melt extrudates

Since the last decade, more and more Active Pharmaceutical Ingredient (API) candidates have poor water solubility inducing low bioavailability. These molecules belong to the Biopharmaceutical Classification System (BCS) classes II and IV. Thanks to Hot-Melt Extrusion (HME), it is possible to incorporate these candidates in pharmaceutical solid forms. Indeed, HME increases the solubility and the bioavailability of these drugs by encompassing them in a polymeric carrier and by forming solid dispersions. Moreover, in 2004, the FDA's guidance initiative promoted the usefulness of Process Analytical Technology (PAT) tools when developing a manufacturing process. Indeed, the main objective when developing a new pharmaceutical process is the product quality throughout the production chain. The trend is to follow this parameter in real-time in order to react immediately when there is a bias. Vibrational spectroscopic techniques, NIR and Raman, are useful to analyze processes in-line. Moreover, off-line Raman microspectroscopy is more and more used when developing new pharmaceutical processes or when analyzing optimized ones by combining the advantages of Raman spectroscopy and imaging. It is an interesting tool for homogeneity and spatial distribution studies. This review treats about spectroscopic techniques analyzing a HME process, as well off-line as in-line, presenting their advantages and their complementarities.

Real-time monitoring of the drying of extruded granules in a fluid-bed dryer using audible acoustic emission chemometrics

The purpose of this study was an attempt to adapt the audible acoustic emission (AAE) sound measurement method for the on-line monitoring of the fluid-bed drying process of pharmaceutical granules.