Global approach for the validation of an in-line Raman spectroscopic method to determine the API content in real-time during a hot-melt extrusion process

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R 2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R 2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42252-024-00063-5.

Evaluation of a compact composite sensor array for concentration monitoring of solutions and suspensions via multivariate analysis

Compact composite probes were identified as a priority to alleviate space constraints in miniaturized unit operations and pharmaceutical manufacturing platforms. Therefore, in this proof of principle study, a compact composite sensor array (CCSA) combining ultraviolet and near infrared features at four different wavelengths (280, 340, 600, 860 nm) in a 380 × 30 mm housing (length x diameter, 7 mm diameter at the probe head), was evaluated for its capabilities to monitor in situ concentration of solutions and suspensions via multivariate analysis using partial least squares (PLS) regression models. Four model active pharmaceutical ingredients (APIs): warfarin sodium isopropanol solvate (WS), lidocaine hydrochloride monohydrate (LID), 6-mercaptopurine monohydrate (6-MP), and acetaminophen (ACM) in their aqueous solution and suspension formulation were used for the assessment. The results demonstrate that PLS models can be applied for the CCSA prototype to measure the API concentrations with similar accuracy (validation samples within the United States Pharmacopeia (USP) limits), compared to univariate CCSA models and multivariate models for an established Raman spectrometer. Specifically, the multivariate CCSA models applied to the suspensions of 6-MP and ACM demonstrate improved accuracy of 63% and 31%, respectively, compared to the univariate CCSA models [1]. On the other hand, the PLS models for the solutions WS and LID showed a reduced accuracy compared to the univariate models [1].

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.

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.

The physical stability of drugs linked to quality-by-design (QbD) and in-process technology (PAT) perspectives

The physical stability of solid-state forms in which drugs may exist is in some sense an overlooked aspect. In an era where strategies such as amorphous solid dispersions or co-amorphous preparations might provide answers to stumbling blocks such as poor drug solubility and bioavailability, the physical stability of such solid-state preparations should be a priority. Furthermore, the pharmaceutical industry is moving towards adapting a real time release of pharmaceutical products strategy, through the utilization of process analytical technology. It is thus becoming imperative to investigate the various types of phase transformations a specific solid-state form of a drug may undergo. Also, to critically assess the applicability of process analytical tools that may be sensitive enough to monitor not only chemical but also physical drug stability. These combined efforts allow quality to be built into the product, rather than dealing with costly post batch release recalls. Given that drug stability is an essential quality attribute for a drug product and the quality-by-design approach (QbD) is a best solution to build quality in all pharmaceutical products we focussed on the critical material attributes (CMAs), specifically relating to the physical stability of any given drug. This review highlights physical drug stability in relation to CMAs and how this ultimately link to the finished pharmaceutical product. Investigated challenges associated current PAT strategies is also discussed.

In-Line Monitoring the Degradation of Polypropylene under Multiple Extrusions Based on Raman Spectroscopy

Polymer degradation is a common problem in the extrusion process. In this work, Raman spectroscopy, a robust, rapid, and non-destructive tool for in-line monitoring, was utilized to in-line monitor the degradation of polypropylene (PP) under multiple extrusions. Raw spectra were pretreated by chemometrics methods to extract variations of spectra and eliminate noise. The variation of Raman intensity with the increasing number of extrusions was caused by the scission of PP chains and oxidative degradation, and the variation trend of Raman intensity indicated that long chains were more likely to be damaged by the extrusion. For the quantitative analysis of degradation, the partial least square was used to build a model to predict the degree of PP degradation measured by gel permeation chromatography (GPC). For the calibration set, the coefficient of determination (R2) and the root mean square error of cross-validation (RMSECV) were 0.9859 and 1.2676%, and for the prediction set, R2 and the root mean square error of prediction (RMSEP) were 0.9752 and 1.7228%, which demonstrated the accuracy of the proposed model. The in-line Raman spectroscopy combined with the chemometrics methods was proved to be an accurate and highly effective tool, which can monitor the degradation of polymer in real time.

Inline UV/Vis spectroscopy as PAT tool for hot-melt extrusion

Hot-melt extrusion on co-rotating twin screw extruders is a focused technology for the production of pharmaceuticals in the context of Quality by Design. Since it is a continuous process, the potential for minimizing product quality fluctuation is enhanced. A typical application of hot-melt extrusion is the production of solid dispersions, where an active pharmaceutical ingredient (API) is distributed within a polymer matrix carrier. For this dosage form, the product quality is related amongst others to the drug content. This can be monitored on- or inline as critical quality attribute by a process analytical technology (PAT) in order to meet the specific requirements of Quality by Design. In this study, an inline UV/Vis spectrometer from ColVisTec was implemented in an early development twin screw extruder and the performance tested in accordance to the ICH Q2 guideline. Therefore, two API (carbamazepine and theophylline) and one polymer matrix (copovidone) were considered with the main focus on the quantification of the drug load. The obtained results revealed the suitability of the implemented PAT tool to quantify the drug load in a typical range for pharmaceutical applications. The effort for data evaluation was minimal due to univariate data analysis, and in combination with a measurement frequency of 1 Hz, the system is sufficient for real-time data acquisition.

An update on the contribution of hot-melt extrusion technology to novel drug delivery in the twenty-first century: part I

INTRODUCTION

Currently, hot melt extrusion (HME) is a promising technology in the pharmaceutical industry, as evidenced by its application to manufacture various FDA-approved commercial products in the market. HME is extensively researched for enhancing the solubility and bioavailability of poor water-soluble drugs, taste masking, and modifying release in drug delivery systems. Additionally, its other novel opportunities or pharmaceutical applications, and capability for continuous manufacturing are being investigated. This efficient, industrially scalable, solvent-free, continuous process can be easily automated and coupled with other novel platforms for continuous manufacturing of pharmaceutical products.

AREAS COVERED

This review focuses on updates on solubility enhancement of poorly water-soluble drugs and process analytical tools such as UV/visible spectrophotometry; near-infrared spectroscopy; Raman spectroscopy; and rheometry for continuous manufacturing, with a special emphasis on fused deposition modeling 3D printing.

EXPERT OPINION

The strengths, weakness, opportunities, threats (SWOT) and availability of commercial products confirmed wide HME applicability in pharmaceutical research. Increased interest in continuous manufacturing processes makes HME a promising strategy for this application. However, there is a need for extensive research using process analytical tools to establish HME as a dependable continuous manufacturing process.

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.

In-Line UV-Vis Spectroscopy as a Fast-Working Process Analytical Technology (PAT) during Early Phase Product Development Using Hot Melt Extrusion (HME)

This paper displays the potential of an in-line PAT system for early phase product development during pharmaceutical continuous manufacturing following a Quality by Design (QbD) framework. Hot melt extrusion (HME) is used as continuous manufacturing process and UV⁻Vis spectroscopy as an in-line monitoring system. A sequential design of experiments (DoE) (screening, optimisation and verification) was used to gain process understanding for the manufacture of piroxicam (PRX)/Kollidon® VA64 amorphous solid dispersions. The influence of die temperature, screw speed, solid feed rate and PRX concentration on the critical quality attributes (CQAs) absorbance and lightness of color (L*) of the extrudates was investigated using multivariate tools. Statistical analysis results show interaction effects between concentration and temperature on absorbance and L* values. Solid feed rate has a significant effect on absorbance only and screw speed showed least impact on both responses for the screening design. The optimum HME process conditions were confirmed by 4 independent studies to be 20% w/w of PRX, temperature 140 °C, screw speed 200 rpm and feed rate 6 g/min. The in-line UV-Vis system was used to assess the solubility of PRX in Kollidon® VA64 by measuring absorbance and L* values from 230 to 700 nm. Oversaturation was observed for PRX concentrations higher than 20% w/w. Oversaturation can be readily identified as it causes scattering in the visible range. This is observed by a shift of the baseline in the visible part of the spectrum. Extrudate samples were analyzed for degradation using off-line High-Performance Liquid Chromatography (HPLC) standard methods. Results from off-line experiments using differential scanning calorimetry (DSC), and X-ray diffraction (XRD) are also presented.