Measurement, optimisation and control of particle properties in pharmaceutical manufacturing processes

Previously held under moratorium from 2 June 2020 until 6 June 2022.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material

XRT : Extraction of quantitative structural descriptors from solid pharmaceutical products

In this project we demonstrate the use of x-ray tomography for the quantification of structural descriptors from two selected solid pharmaceutical products: single formulated particles and a commercial Ibuprofen capsule. In particular, we demonstrate the application of image processing strategies for noise reduction, image segmentation and the extraction of quantitative structural descriptors. Information on the sample’s solid state properties can be used to evaluate the manufacturing process and allows a prediction of the solid performance for subsequent processing steps or after administration to the patient

Direct image feature extraction and multivariate analysis for crystallisation process characterisation

Small-scale crystallization experiments (1-8 mL) are widely used during early-stage crystallization process development to obtain initial information on solubility, metastable zone width, as well as attainable nucleation and/or growth kinetics in a material-efficient manner. Digital imaging is used to monitor these experiments either providing qualitative information or for object detection coupled with size and shape characterization. In this study, a novel approach for the routine characterization of image data from such crystallization experiments is presented employing methodologies for direct image feature extraction. A total of 80 image features were extracted based on simple image statistics, histogram parametrization, and a series of targeted image transformations to assess local grayscale characteristics. These features were utilized for applications of clear/cloud point detection and crystal suspension density prediction. Compared to commonly used transmission-based methods (mean absolute error 8.99 mg/mL), the image-based detection method is significantly more accurate for clear and cloud point detection with a mean absolute error of 0.42 mg/mL against a manually assessed ground truth. Extracted image features were further used as part of a partial least-squares regression (PLSR) model to successfully predict crystal suspension densities up to 40 mg/mL (R2 > 0.81, Q2 > 0.83). These quantitative measurements reliably provide crucial information on composition and kinetics for early parameter estimation and process modeling. The image analysis methodologies have a great potential to be translated to other imaging techniques for process monitoring of key physical parameters to accelerate the development and control of particle/crystallization processes

Peptide isolation via spray drying : particle formation, process design and implementation for the production of spray dried glucagon

Purpose Spray drying plays an important role in the pharmaceutical industry for product development of sensitive bio-pharmaceutical formulations. Process design, implementation and optimisation require in-depth knowledge of process-product interactions. Here, an integrated approach for the rapid, early-stage spray drying process development of trehalose and glucagon on lab-scale is presented. Methods Single droplet drying experiments were used to investigate the particle formation process. Process implementation was supported using in-line process analytical technology within a data acquisition framework recording temperature, humidity, pressure and feed rate. During process implementation, off-line product characterisation provided additional information on key product properties related to residual moisture, solid state structure, particle size/morphology and peptide fibrillation/degradation. Results A psychrometric process model allowed the identification of feasible operating conditions for spray drying trehalose, achieving high yields of up to 84.67%, and significantly reduced levels of residual moisture and particle agglomeration compared to product obtained during non-optimal drying. The process was further translated to produce powders of glucagon and glucagon-trehalose formulations with yields of >83.24%. Extensive peptide aggregation or degradation was not observed. Conclusions The presented data-driven process development concept can be applied to address future isolation problems on lab-scale and facilitate a systematic implementation of spray drying for the manufacturing of sensitive bio-pharmaceutical formulations

High spatial resolution ToF-SIMS imaging and image analysis strategies to monitor and quantify early phase separation in amorphous solid dispersions

Amorphous solid dispersions (ASDs) are formulations with enhanced drug solubility and dissolution rate compared to their crystalline counterparts, however, they can be inherently thermodynamically unstable. This can lead to amorphous phase separation and drug re-crystallisation, phenomena that are typically faster and more dominant at the product’s surfaces. This study investigates the use of high-resolution time of flight-secondary ion mass spectrometry (ToF-SIMS) imaging as a surface analysis technique combined with image-analysis for the early detection, monitoring and quantification of surface amorphous phase separation in ASDs. Its capabilities are demonstrated for two pharmaceutically relevant ASD systems with distinct re-crystallisation behaviours, prepared using hot melt extrusion (HME) followed by pelletisation or grinding: (1) paracetamol-hydroxypropyl methylcellulose (PCM-HPMC) pellets with drug loadings of 10–50% w/w and (2) indomethacin-polyvinylpyrrolidone (IND-PVP) ground material with drug loadings of 20–85% w/w. PCM-HPMC pellets showed intense phase separation, reaching 100% surface coverage within 1-5 months. In direct comparison, IND-PVP HME ground material was more stable with only a moderate formation of isolated IND-rich clusters. Image analysis allowed the reliable detection and quantification of local drug-rich clusters. An Avrami model was applied to determine and compare phase separation kinetics. The combination of chemical sensitivity and high spatial resolution afforded by SIMS was crucial to enable the study of early phase separation and re-crystallisation at the surface. Compared with traditional methods used to detect crystalline material, such as XRPD, we show that ToF-SIMS enabled detection of surface physical instability already at early stages of drug cluster formation in the first days of storage

Digital design of end-to-end manufacturing process for mefenamic acid using mechanistic modelling

Conference abstract