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

Enhancing Process Control and Quality in Amorphous Solid Dispersions Using In-Line UV-Vis Monitoring of L* as a Real-Time Response

Background: This study demonstrates the application of the sequential design of experiments (DoE) approach within the quality by design (QbD) framework to optimize extrusion processes through screening, optimization, and robustness testing. Methods: An in-line UV-Vis process analytical technology (PAT) system was successfully employed to monitor critical quality attributes (CQAs) of piroxicam amorphous solid dispersion (ASD) extrusion products, specifically lightness (L*). Results: L* measurement proved highly effective for ensuring the quality and uniformity of ASDs, offering real-time insights into their physical appearance and process stability. Small variations in L* acted as early indicators of processing issues, such as phase separation or bubble formation, enabling timely intervention. This straightforward and rapid technique supports real-time process monitoring and control, allowing automated adjustments to maintain product consistency and quality. By adopting this strategy, manufacturers can minimize variability, reduce waste, and ensure adherence to quality target product profiles (QTPPs). Conclusions: Overall, this study highlights the value of in-line UV-Vis spectroscopy as a PAT tool in hot melt extrusion, enhancing CQA assessment and advancing the efficiency and reliability of ASD manufacturing.

Optimizing extrusion processes and understanding conformational changes in itraconazole amorphous solid dispersions using in-line UV-Vis spectroscopy and QbD principles

This paper presents a comprehensive investigation of the manufacturing of itraconazole (ITZ) amorphous solid dispersions (ASDs) with Kolllidon® VA64 (KVA64) using hot-melt extrusion (HME) and in-line process monitoring, employing a Quality by Design (QbD) approach. A sequential Design of Experiments (DoE) strategy was utilized to optimize the manufacturing process, with in-line UV-Vis spectroscopy providing real-time monitoring. The first DoE used a fractional factorial screening design to evaluate critical process parameters (CPPs), revealing that ITZ concentration had the most significant impact on the product quality attributes. The second DoE, employing a central composite design, explored the interactions between feed rate and screw speed, using torque and absorbance at 370 nm as responses to develop a design space. Validation studies confirmed process robustness across multiple days, with stable in-line UV-Vis spectra and consistent product quality using 30 % ITZ, 300 rpm, 150 °C and 7 g/min as the optimized process conditions. Theoretical and experimental analyses indicated that shifts in UV-Vis spectra at different ITZ concentrations were due to conformational changes in ITZ, which were confirmed through density functional theory (DFT) calculations and infrared spectroscopy. This work offers novel insights into the production and monitoring of ITZ-KVA64-ASDs, demonstrating that in-line UV-Vis spectroscopy is a powerful tool for real-time process monitoring and/or control.

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.

Question-based review for pharmaceutical development: An enhanced quality approach

Over the last years, the pharmaceutical industry has faced real challenges regarding quality assurance. In this context, the establishment of more holistic approaches to the pharmaceutical development has been encouraged. The emergence of the Quality by Design (QbD) paradigm as systematic, scientific and risk-based methodology introduced a new concept of pharmaceutical quality. In essence, QbD can be interpreted as a strategy to maximize time and cost savings. An in-depth understanding of the formulation and manufacturing process is demanded to optimize the safety, efficacy and quality of a drug product at all stages of development. This innovative approach streamlines the pharmaceutical Research and Development (R&D) process, provides greater manufacturing flexibility and reduces regulatory burden. To assist in QbD implementation, International Conference on Harmonisation (ICH), U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) organized and launched QbD principles in their guidance for industry, identifying key concepts and tools to design and develop a high-quality drug product. Despite the undeniable advantages of the QbD approach, and the widespread information on QbD regulatory expectations, its full implementation in the pharmaceutical field is still limited. The present review aims to establish a crosswise overview on the current application status of QbD within the framework of the ICH guidelines (ICH Q8(R2) - Q14 and ICH Q2(R2)). Moreover, it outlines the way information gathered from the QbD methodology is being harmonized in Marketing Authorization Applications (MAAs) for European market approval. This work also highlights the challenges that hinder the deployment of the QbD strategy as a standard practice.

Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk

Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.

In-line monitoring of solid dispersion preparation in small scale extrusion based on UV-vis spectroscopy

The poor solubility of a large number of active pharmaceutical ingredients (APIs) is a major challenge in pharmaceutical research. Therefore, the extrusion of amorphous solid dispersions (ASDs) is one promising approach to enhance the dissolution rate by molecularly dissolving the API in an amorphous carrier polymer. During ASD extrusion, crucial parameters as the dissolution of the API in the carrier polymer need to be monitored. Within this study, a small scale twin screw extruder was coupled with special ColVisTec UV-vis probes that are characterized by their small dimensions. This setup enables a systematic formulation design and optimization based on in-line monitoring of drug dissolution using small material quantities. In fact, sample quantities of about 5 mg were evaluated for each measurement, representing 50% of the material inside the die. The amount of undissolved drug particles was determined based on the lightness of the extrudates. It was shown that the temperature has a significant effect on the drug dissolution in the polymer. Furthermore, complete drug dissolution was shifted to lower temperatures if higher residence times were applied. Based on the courses of lightness, regime maps were modeled that specify the process conditions where ASDs are successfully manufactured.

Analytical Quality by Design (AQbD) Approach to the Development of Analytical Procedures for Medicinal Plants

Scientific regulatory systems with suitable analytical methods for monitoring quality, safety, and efficacy are essential in medicinal plant drug discovery. There have been only few attempts to adopt the analytical quality by design (AQbD) strategy in medicinal plants analysis over the last few years. AQbD is a holistic method and development approach that understands analytical procedure, from risk assessment to lifecycle management. The enhanced AQbD approach reduces the time and effort necessary to develop reliable analytical methods, leads to flexible change control through the method operable design region (MODR), and lowers the out-of-specification (OOS) results. However, it is difficult to follow all the AQbD workflow steps in the field of medicinal plants analysis, such as defining the analytical target profiles (ATPs), identifying critical analytical procedure parameters (CAPPs), among others, because the complexity of chemical and biological properties in medicinal plants acts as a barrier. In this review, various applications of AQbD to medicinal plant analytical procedures are discussed. Unlike the analysis of a single compound, medicinal plant analysis is characterized by analyzing multiple components contained in biological materials, so it will be summarized by focusing on the following points: Analytical methods showing correlations within analysis parameters for the specific medicinal plant analysis, plant raw material diversity, one or more analysis targets defined for multiple phytochemicals, key analysis attributes, and analysis control strategies. In addition, the opportunities available through the use of design-based quality management techniques and the challenges that coexist are also discussed.

Designing prototype rapid test device at qualitative performance to detect residue of tetracycline in chicken carcass

Background and Aim:

Human health problems due as a microbial resistance or tumors and cancers because consumption of the carcasses containing residues of tetracycline are main global problems in the context of fight against antimicrobial resistance phenomena. Explanation of the sustainable development goals, particularly point 3, is well recognized that all animal products for human consumption must be safe to live a healthy life. This study aimed to design a prototype of rapid test devices (RTD) based on principles of precipitate to obtain a specific color change after the process of reactions as an indicator to determine tetracycline residues in the carcass.

Materials and Methods:

Five samples of tetracycline-containing poultry carcasses using artificial add the tetracycline at pharmaceutics grade were examined using a prototype of a strong reaction solution for tetracycline fixation based on the concept bonded by ion Fe(III) at atom O in position atom C-1 at the ring of tetracycline and ion N⁺ as the functional branch of tetracycline. RTD detection was evaluated using a yellow color presentation and an absorbance spectrometric technique at a wavelength of 273 nm.

Results:

The following chemicals were used to create the best-fixed tetracycline residue: HCl and H₂SO₄ dissolved in H₂O, chromatographic grade of 0.1 N and 0.5 N of HNO₃, and 1% Fe (III) Cl. The RTD had a higher limit of detection (LOD) than the ultraviolet-visible spectrophotometer.

Conclusion:

The results of this study revealed that RTD, as constructed in this study, can be used to detect residue at LOD 44.764 mg/mL during 120 min of exposure through a light-emitting diode at 980 nm wavelength (p<0.05). The necessity for using RTD was because of the apparent limitations of conventional devices.

NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing

PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established 'soft sensing' method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing.

Analytical Quality by Design (AQbD) Approach to the Development of In Vitro Release Test for Topical Hydrogel

The aim of our study was to adapt the analytical quality by design (AQbD) approach to design an effective in vitro release test method using USP apparatus IV with a semi-solid adapter (SSA) for diclofenac sodium hydrogel. The analytical target profile (ATP) of the in vitro release test and ultra-high-performance liquid chromatography were defined; the critical method attributes (CMAs) (min. 70% of the drug should be released during the test, six time points should be obtained in the linear portion of the drug release profile, and the relative standard deviation of the released drug should not be over 10%) were selected. An initial risk assessment was carried out, in which the CMAs (ionic strength, the pH of the media, membrane type, the rate of flow, the volume of the SSA (sample amount), the individual flow rate of cells, drug concentration %, and the composition of the product) were identified. With the results, it was possible to determine the high-risk parameters of the in vitro drug release studies performed with the USP apparatus IV with SSA, which were the pH of the medium and the sample weight of the product. Focusing on these parameters, we developed a test protocol for our hydrogel system.

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.

Validation of the Developed Zero-Order Infrared Spectrophotometry Method for Qualitative and Quantitative Analyses of Tranexamic Acid in Marketed Tablets

(1) Background: The functional groups present in tranexamic acid allow direct infrared detection analysis. This study aimed to develop, apply, and validate an infrared spectrophotometry method used for qualitative and quantitative analyses of tranexamic acid in marketed tablets. (2) Methods: This was a descriptive observational study that consisted of several stages: determining the specific wavenumber for analysis, obtaining a simple linear regression equation, analyzing tranexamic acid both qualitatively and quantitatively, and validating the developed method for routine analysis. (3) Results: The peak analysis obtained a range of baseline wavenumbers from 1679.17 to 1295.25 cm⁻¹. The regression equation obtained was Y = 310.8527 × X + 0.9718, and the coefficient of determination (R²) obtained was 0.9994. The tranexamic acids in marketed tablets overall have a similarity index value of more than 0.90 and overall have levels ranging between 97.0% and 103.0%. The infrared spectrophotometry method that was successfully developed, applied, and validated for qualitative and quantitative analyses of tranexamic acid in marketed tablets meets the requirements both qualitatively and quantitatively of the tablet monograph. (4) Conclusions: The infrared spectrophotometry method has been validated and meets the requirements for accuracy, precision, detection limit, quantitation limit, linearity, range, and specificity.

Formulation and development of Serratiopeptidase enteric coated tablets and analytical method validation by UV Spectroscopy

Serratiopeptidase (SRP) is a proteolytic enzyme that emerged as one of the most potent anti-inflammatory and analgesic drugs. The purpose of the present study was to formulate and evaluate enteric-coated tablets for SRP and investigate their stability using a simple and validated analytical method by ultraviolet (UV) spectroscopy. The colloidal silicon dioxide (2.50%), sodium starch glycolate (3.44%), and crospovidone (2.50%) were used as appropriate excipients for the development of core part of tablets. To protect the prepared tablets from acidic environment in the stomach, white shellac, castor oil, HPMC phthalate 40, and ethyl cellulose were used. The seal coating and enteric coating attained were 2.75% and 6.74%, respectively. SRP was found to be linear at 265 nm in the concentration range of 25–150 µg/mL. The results revealed that our developed method was linear (R² = 0.999), precise (RSD % = 0.133), and accurate (% recovery = 99.96–103.34). The formulated SRP tablets were found to be stable under accelerated conditions as well as under room temperature for 6 months (assay %: >97.5%). The in vitro drug release study demonstrated that enteric-coated tablets were able to restrict SRP release in both acidic environments: 0.1 N HCl and simulated gastric fluid (pH 1.2). Moreover, at 60 minutes, the formulated SRP tablets revealed 13.0% and 8.98% higher drug release in phosphate buffer (pH 6.8) and simulated intestinal fluid (pH 6.8), respectively, compared to the marketed tablet formulation. This study concludes that enteric-coated tablets of SRP with higher drug release in the intestine can be prepared and examined for their stability using validated analytical technique of UV spectroscopy.

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.

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.

Novel Cleaning-in-Place Strategies for Pharmaceutical Hot Melt Extrusion

To avoid any type of cross-contamination, residue-free production equipment is of utmost importance in the pharmaceutical industry. The equipment cleaning for continuous processes such as hot melt extrusion (HME), which has recently gained popularity in pharmaceutical applications, necessitates extensive manual labour and costs. The present work tackles the HME cleaning issue by investigating two cleaning strategies following the extrusion of polymeric formulations of a hormonal drug and for a sustained release formulation of a poorly soluble drug. First, an in-line quantification by means of UV–Vis spectroscopy was successfully implemented to assess very low active pharmaceutical ingredient (API) concentrations in the extrudates during a cleaning procedure for the first time. Secondly, a novel in-situ solvent-based cleaning approach was developed and its usability was evaluated and compared to a polymer-based cleaning sequence. Comparing the in-line data to typical swab and rinse tests of the process equipment indicated that inaccessible parts of the equipment were still contaminated after the polymer-based cleaning procedure, although no API was detected in the extrudate. Nevertheless, the novel solvent-based cleaning approach proved to be suitable for removing API residue from the majority of problematic equipment parts and can potentially enable a full API cleaning-in-place of a pharmaceutical extruder for the first time.