Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor

Deep-Learning-Guided Electrochemical Impedance Spectroscopy for Calibration-Free Pharmaceutical Moisture Content Monitoring

The moisture content of pharmaceutical powders can significantly impact the physical and chemical properties of drug formulations, solubility, flowability, and stability. However, current technologies for measuring moisture content in pharmaceutical materials require extensive calibration processes, leading to poor consistency and a lack of speed. To address this challenge, this study explores the feasibility of using impedance spectroscopy to enable accurate, rapid testing of moisture content of pharmaceutical materials with minimal to zero calibration. By utilizing electrochemical impedance spectroscopy (EIS) signals, we identify a strong correlation between the electrical properties of the materials and varying moisture contents in pharmaceutical samples. Equivalent circuit modeling is employed to unravel the underlying mechanism, providing valuable insights into the sensitivity of impedance spectroscopy to moisture content variations. Furthermore, the study incorporates deep learning techniques utilizing a 1D convolutional neural network (1DCNN) model to effectively process the complex spectroscopy data. The proposed model achieved a notable predictive accuracy with an average error of just 0.69% in moisture content estimation. This method serves as a pioneering study in using deep learning to provide a reliable solution for real-time moisture content monitoring, with potential applications extending from pharmaceuticals to the food, energy, environmental, and healthcare sectors.

Process analytical technology in Downstream-Processing of Drug Substances- A review

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Real-Time Monitoring of a Sol-Gel Reaction for Polysilane Production Using Inline NIR Spectroscopy

The sol-gel process is an effective method for the preparation of homogeneous structured nanomaterials whose physico-chemical properties strongly depend on the experimental conditions applied. The control of a three-component reaction with silanes showing multiple reaction sites revealed the need for an analytical tool that allows a rapid response to ongoing transformations in the reaction mixture. Herein, we describe the implementation of near-infrared (NIR) spectroscopy based on compact, mechanically robust, and cost-efficient micro-optomechanical system technology in the sol-gel process of three silanes with a total of nine reaction sites. The NIR-spectroscopically controlled reaction yields a long-time stable product with reproducible quality, fulfilling the demanding requirements for further use in coating processes. ¹H nuclear magnetic resonance measurements are used as reference values for the calibration of a partial least squares (PLS) regression model. The precise prediction of the desired parameters from collected NIR spectroscopy data acquired during the sol-gel reaction proves the applicability of the calibrated PLS regression model. The determined shelf-life and further processing tests verify the high quality of the sol-gel and the produced highly cross-linked polysilane.

Usefulness of Applying Partial Least Squares Regression to T2 Relaxation Curves for Predicting the Solid form Content in Binary Physical Mixtures

This study applied partial least squares (PLS) regression to nuclear magnetic resonance (NMR) relaxation curves to quantify the free base of an active pharmaceutical ingredient powder. We measured the T₂ relaxation of intact and moisture-absorbed physical mixtures of tetracaine free base (TC) and its hydrochloride salt (TC·HCl). The obtained T₂ relaxation curves were analyzed by two methods, one using a previously reported T₂ relaxation time (T₂), and the other using PLS regression. The accuracy of estimating TC was inadequate when using previous T₂ values because the moisture-absorbed physical mixtures showed biphasic T₂ relaxation curves. By contrast, the entire measured whole of the T₂ relaxation curves was used as input variables and analyzed by PLS regression to quantify the content of TC in the moisture-absorbed TC/TC·HCl. Based on scatterplots of theoretical versus predicted TC, the obtained PLS model exhibited acceptable coefficients of determination and relatively low root mean squared error values for calibration and validation data. The statistical values confirmed that an accurate and reliable PLS model was created to quantify TC in even moisture-absorbed TC/TC·HCl. The bench-top low-field NMR instrument used to apply PLS regression to the T₂ relaxation curve may be a promising tool in process analytical technology.

Heat Transfer Model and Soft Sensing for Segmented Fluidized Bed Dryer

The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequently, a soft sensor is established by combining data sources, a real-time data communication method, and the developed drying model, and it shows the capability of predicting real-time LOD, where the error of end-point LOD is within 0.5 wt.%. The work provides detailed steps and applicable tools for developing a soft sensor, and the online deployment of the soft sensor could support continuous production in the Seg-FBD by enabling visualization of process status and determination of process end point.

A review of emerging technologies enabling improved solid oral dosage form manufacturing and processing

Tablets are the most widely utilized solid oral dosage forms because of the advantages of self-administration, stability, ease of handling, transportation, and good patient compliance. Over time, extensive advances have been made in tableting technology. This review aims to provide an insight about the advances in tablet excipients, manufacturing, analytical techniques and deployment of Quality by Design (QbD). Various excipients offering novel functionalities such as solubility enhancement, super-disintegration, taste masking and drug release modifications have been developed. Furthermore, co-processed multifunctional ready-to-use excipients, particularly for tablet dosage forms, have benefitted manufacturing with shorter processing times. Advances in granulation methods, including moist, thermal adhesion, steam, melt, freeze, foam, reverse wet and pneumatic dry granulation, have been proposed to improve product and process performance. Furthermore, methods for particle engineering including hot melt extrusion, extrusion-spheronization, injection molding, spray drying / congealing, co-precipitation and nanotechnology-based approaches have been employed to produce robust tablet formulations. A wide range of tableting technologies including rapidly disintegrating, matrix, tablet-in-tablet, tablet-in-capsule, multilayer tablets and multiparticulate systems have been developed to achieve customized formulation performance. In addition to conventional invasive characterization methods, novel techniques based on laser, tomography, fluorescence, spectroscopy and acoustic approaches have been developed to assess the physical-mechanical attributes of tablet formulations in a non- or minimally invasive manner. Conventional UV-Visible spectroscopy method has been improved (e.g. fiber-optic probes and UV imaging-based approaches) to efficiently record the dissolution profile of tablet formulations. Numerous modifications in tableting presses have also been made to aid machine product changeover, cleaning, and enhance efficiency and productivity. Various process analytical technologies have been employed to track the formulation properties and critical process parameters. These advances will contribute to a strategy for robust tablet dosage forms with excellent performance attributes.

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.

Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust pre-processing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.

Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy

Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.