Process monitoring and fault detection on a hot-melt extrusion process using in-line Raman spectroscopy and a hybrid soft sensor

Thermal degradation of Affinisol HPMC: Optimum Processing Temperatures for Hot Melt Extrusion and 3D Printing

PURPOSE

Affinisol HPMC HME is a new popular form of hypromellose specifically designed for the hot melt extrusion and 3D printing of pharmaceutical products. However, reports of its thermal stability include only data obtained under inert N2 atmosphere, which is not consistent with the common pharmaceutical practice. Therefore, detailed investigation of its real-life thermal stability in air is paramount for identification of potential risks and limitations during its high-temperature processing.

METHODS

In this work, the Affinisol HPMC HME 15LV powder as well as extruded filaments will be investigated by means of thermogravimetry, differential scanning calorimetry and infrared spectroscopy with respect to its thermal stability.

RESULTS

The decomposition in N2 was proceeded in accordance with the literature data and manufacturer's specifications: onset at ~260°C at 0.5°C·min-1, single-step mass loss of 90-95%. However, in laboratory or industrial practice, high-temperature processing is performed in the air, where oxidation-induced degradation drastically changes. The thermogravimetric mass loss in air proceeded in three stages: ~ 5% mass loss with onset at 150°C, ~ 70% mass loss at 200°C, and ~ 15% mass loss at 380°C. Diffusion of O2 into the Affinisol material was identified as the rate-determining step.

CONCLUSION

For extrusion temperatures ≥170°C, Affinisol exhibits a significant degree of degradation within the 5 min extruder retention time. Hot melt extrusion of pure Affinisol can be comfortably performed below this temperature. Utilization of plasticizers may be necessary for safe 3D printing.

Exploring the Solid-State Landscape of Carbamazepine during Dehydration: A Low Frequency Raman Spectroscopy Perspective

The solid-state landscape of carbamazepine during its dehydration was explored using Raman spectroscopy in the low- (-300 to -15, 15 to 300) and mid- (300 to 1800 cm^-1) frequency spectral regions. Carbamazepine dihydrate and forms I, III, and IV were also characterized using density functional theory with periodic boundary conditions and showed good agreement with experimental Raman spectra with mean average deviations less than 10 cm^-1. The dehydration of carbamazepine dihydrate was examined under different temperatures (40, 45, 50, 55, and 60 °C). Principal component analysis and multivariate curve resolution were used to explore the transformation pathways of different solid-state forms during the dehydration of carbamazepine dihydrate. The low-frequency Raman domain was able to detect the rapid growth and subsequent decline of carbamazepine form IV, which was not as effectively observed by mid-frequency Raman spectroscopy. These results showcased the potential benefits of low-frequency Raman spectroscopy for pharmaceutical process monitoring and control.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Recent Progress in Hot Melt Extrusion Technology in Pharmaceutical Dosage Form Design

BACKGROUND

The Hot Melt Extrusion (HME) technique has shown tremendous potential in transforming highly hydrophobic crystalline drug substances into amorphous solids without using solvents. This review explores in detail the general considerations involved in the process of HME, its applications and advances.

OBJECTIVE

The present review examines the physicochemical properties of polymers pertinent to the HME process. Theoretical approaches for the screening of polymers are highlighted as a part of successful HME processed drug products. The critical quality attributes associated with the process of HME are also discussed in this review. HME plays a significant role in the dosage form design, and the same has been mentioned with suitable examples. The role of HME in developing several sustained release formulations, films, and implants is described along with the research carried out in a similar domain.

METHODS

The method includes the collection of data from different search engines like PubMed, ScienceDirect, and SciFinder to get coverage of relevant literature for accumulating appropriate information regarding HME, its importance in pharmaceutical product development, and advanced applications.

RESULTS

HME is known to have advanced pharmaceutical applications in the domains related to 3D printing, nanotechnology, and PAT technology. HME-based technologies explored using Design-of- Experiments also lead to the systematic development of pharmaceutical formulations.

CONCLUSION

HME remains an adaptable and differentiated technique for overall formulation development.

Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions

In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

Innovations in Thermal Processing: Hot-Melt Extrusion and KinetiSol® Dispersing

Thermal processing has gained much interest in the pharmaceutical industry, particularly for the enhancement of solubility, bioavailability, and dissolution of active pharmaceutical ingredients (APIs) with poor aqueous solubility. Formulation scientists have developed various techniques which may include physical and chemical modifications to achieve solubility enhancement. One of the most commonly used methods for solubility enhancement is through the use of amorphous solid dispersions (ASDs). Examples of commercialized ASDs include Kaletra®, Kalydeco®, and Onmel®. Various technologies produce ASDs; some of the approaches, such as spray-drying, solvent evaporation, and lyophilization, involve the use of solvents, whereas thermal approaches often do not require solvents. Processes that do not require solvents are usually preferred, as some solvents may induce toxicity due to residual solvents and are often considered to be damaging to the environment. The purpose of this review is to provide an update on recent innovations reported for using hot-melt extrusion and KinetiSol® Dispersing technologies to formulate poorly water-soluble APIs in amorphous solid dispersions. We will address development challenges for poorly water-soluble APIs and how these two processes meet these challenges.

Development and Validation of an In-Line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

A key principle of developing a new medicine is that quality should be built in, with a thorough understanding of the product and the manufacturing process supported by appropriate process controls. Quality by design principles that have been established for the development of drug products/substances can equally be applied to the development of analytical procedures. This paper presents the development and validation of a quantitative method to predict the concentration of piroxicam in Kollidon^® VA 64 during hot melt extrusion using analytical quality by design principles. An analytical target profile was established for the piroxicam content and a novel in-line analytical procedure was developed using predictive models based on UV-Vis absorbance spectra collected during hot melt extrusion. Risks that impact the ability of the analytical procedure to measure piroxicam consistently were assessed using failure mode and effect analysis. The critical analytical attributes measured were colour (L* lightness, b* yellow to blue colour parameters—in-process critical quality attributes) that are linked to the ability to measure the API content and transmittance. The method validation was based on the accuracy profile strategy and ICH Q2(R1) validation criteria. The accuracy profile obtained with two validation sets showed that the 95% β-expectation tolerance limits for all piroxicam concentration levels analysed were within the combined trueness and precision acceptance limits set at ±5%. The method robustness was tested by evaluating the effects of screw speed (150–250 rpm) and feed rate (5–9 g/min) on piroxicam content around 15% w/w. In-line UV-Vis spectroscopy was shown to be a robust and practical PAT tool for monitoring the piroxicam content, a critical quality attribute in a pharmaceutical HME process.