Development and integration of a continuous horizontal belt filter into drug production procedure.

In the pharmaceutical industry, filtration is traditionally carried out in batch mode. However, with the spread of continuous technologies, there is an increasing demand for robust continuous filtration strategies suitable for processing suspensions produced in continuous crystallizers. Accordingly, this study aimed to investigate a lab-scale horizontal conveyor belt filtration approach for pharmaceutical separation purposes for the first time.

In-line indirect concentration measurement of ultralow dose API during twin-screw wet granulation based on NIR and Raman spectroscopy.

Twin-screw wet granulation (TWSG) is a promising continuous alternative of pharmaceutical wet granulation. One of its benefits is that the components dissolved in the granulation liquid are distributed homogeneously in the granules. This provides an elegant way to manufacture products with ultralow drug doses.

Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0.

Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.

Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision.

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time.

Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet - An industrial case study of attempting to comply with EU regulation 2022/63.

Recently, concerns have been raised about the safety of titanium dioxide (TiO), a commonly used component of pharmaceutical film coatings. The European Union has recently prohibited the application of this material in the food industry, and it is anticipated that the same will happen in the pharmaceutical industry. For this reason, pharmaceutical manufacturers have to consider the possible impact of removing TiO from the film coating of tablets.

Development of a PAT platform for the prediction of granule tableting properties.

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500 kg load range.

Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles.

In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted information is utilized to predict the in vitro dissolution profile of the tablets. For the first time, convolutional neural networks (CNNs) are used for the processing of the chemical images of HPMC distribution and to directly predict the dissolution profile based on the image.

Improvement of drug processability in a connected continuous crystallizer system using formulation additive.

Continuous crystallization in the presence of polymer additives is a promising method to omit some drug formulation steps by improving the technological and also pharmacological properties of crystalline active ingredients. Accordingly, this study focuses on developing an additive-assisted continuous crystallization process using polyvinylpyrrolidone in a connected ultrasonicated plug flow crystallizer and an overflow mixed suspension mixed product removal (MSMPR) crystallizer system. We aimed to improve the flowability characteristics of small, columnar primary plug flow crystallizer-produced acetylsalicylic acid crystals as a model drug by promoting their agglomeration in MSMPR crystallizer with polyvinylpyrrolidone.

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset.

As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review.

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations.

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill.

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.

UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks.

The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets.

Raman-based real-time dissolution prediction using a deterministic permeation model.

The purpose of this study was to develop a deterministic permeation model (DPM) that predicts the in vitro release profile of an active ingredient (API) embedded in hydroxypropyl-methylcellulose (HPMC) matrix tablets based on Raman spectra. So far in the literature, such mechanistic models were utilized only for formulation optimization (off-line dissolution prediction), while the real-time prediction of dissolution profiles based on Process Analytical Technology (PAT) data was performed by empirical methods such as Partial Least Squares (PLS) regression. Our work represents a novel conceptual approach that utilizes a mechanistic model to predict dissolution profiles based on data yielded by PAT tools.

Raman mapping-based non-destructive dissolution prediction of sustained-release tablets.

In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model.

Flux-Based Formulation Development-A Proof of Concept Study.

The work aimed to develop the Absorption Driven Drug Formulation (ADDF) concept, which is a new approach in formulation development to ensure that the drug product meets the expected absorption rate. The concept is built on the solubility-permeability interplay and the rate of supersaturation as the driving force of absorption. This paper presents the first case study using the ADDF concept where not only dissolution and solubility but also permeation of the drug is considered in every step of the formulation development.

Continuous downstream processing of milled electrospun fibers to tablets monitored by near-infrared and Raman spectroscopy.

Electrospinning is a technology for manufacture of nano- and micro-sized fibers, which can enhance the dissolution properties of poorly water-soluble drugs. Tableting of electrospun fibers have been demonstrated in several studies, however, continuous manufacturing of tablets have not been realized yet. This research presents the first integrated continuous processing of milled drug-loaded electrospun materials to tablet form supplemented by process analytical tools for monitoring the active pharmaceutical ingredient (API) content.

Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data.

In this work spectroscopic measurements, process data and Critical Material Attributes (CMAs) are used to predict the in vitro dissolution profile of sustained-release tablets with three machine learning methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble of Regression Trees (ERT). Beside the effect of matrix polymer content and compression force, the influence of active pharmaceutical ingredient (API) and matrix polymer particle size distribution (PSD) on the drug release rate of sustained tablets is studied. The matrix polymer PSD was found to be a significant factor, thus this factor was included in the dissolution prediction experiments.

Continuous blending monitored and feedback controlled by machine vision-based PAT tool.

In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models.

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding.

End-to-end continuous manufacturing of conventional compressed tablets: From flow synthesis to tableting through integrated crystallization and filtration.

An end-to-end continuous pharmaceutical manufacturing process was developed for the production of conventional direct compressed tablets on a proof-of-concept level for the first time. The output reaction mixture of the flow synthesis of acetylsalicylic acid was crystallized continuously in a mixed suspension mixed product removal crystallizer. The crystallizer was directly connected to a continuous filtration carousel device, thus the crystallization, filtration and drying of acetylsalicylic acid (ASA) was carried out in an integrated 2-step process.

Digital UV/VIS imaging: A rapid PAT tool for crushing strength, drug content and particle size distribution determination in tablets.

The Process Analytical Technology (PAT) and the Quality-by-Design (QbD) approaches can efficiently facilitate the shift to the desired continuous manufacturing and real time release testing (RTRT). By this, it is vital to develop new, in-line analytical methods which fulfil the pharmaceutical requirements. The fast-developing digital imaging-based machine vision systems can provide revolutionary solutions not just in the automotive industry but in the pharmaceutical technology, as well.

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets.

Application of artificial neural networks for Process Analytical Technology-based dissolution testing.

This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, where the dissolution was influenced by the composition of the tablets and the tableting compression force. NIR and Raman spectra of the intact tablets were measured, and the dissolution of the tablets was modeled directly from the spectral data.

Continuous alternative to freeze drying: Manufacturing of cyclodextrin-based reconstitution powder from aqueous solution using scaled-up electrospinning.

The aims of this study were to evaluate electrospinning as a continuous alternative to freeze drying in the production of a reconstitution injection dosage form, and to prove that aqueous electrospinning can be realized with a high production rate at room temperature. High-speed electrospinning with a novel continuous cyclone collection was used to manufacture a formulation of the poorly water-soluble antifungal voriconazole (VOR) with sulfobutylether-β-cyclodextrin (SBE-β-CD). The freeze-dried, marketed product of this drug substance, Vfend® also contains SBE-β-CD as excipient.