Characterization of pharmaceutical powder blends using in situ near-infrared chemical imaging

Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena

A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

Evaluation of the Miscibility of Novel Cocoa Butter Equivalents by Raman Mapping and Multivariate Curve Resolution-Alternating Least Squares

Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution-alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

SWiVIA - Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.

Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy

It is significant to analyze the blend homogeneity of cohesive powders during pharmaceutical manufacturing in order to provide the exact content of the active pharmaceutical ingredient (API) for each individual dose unit. In this paper, an online monitoring platform using an MEMS near infrared (NIR) sensor was designed to control the bin blending process of cohesive powders. The state of blend homogeneity was detected by an adaptive algorithm, which was calibration free. The online control procedures and algorithm’s parameters were fine-tuned through six pilot experiments and were successfully transferred to the industrial production. The reliability of homogeneity detection results was validated by 16 commercial scale experiments using 16 kinds of natural product powders (NPPs), respectively. Furthermore, 19 physical quality attributes of all NPPs and the excipient were fully characterized. The blending end time was used to denote the degree of difficulty of blending. The empirical relationships between variability of NPPs and the blending end time were captured by latent variable modeling. The critical material attributes (CMAs) affecting the blending process were identified as the particle shape and flowability descriptors of cohesive powders. Under the framework of quality by design (QbD) and process analytical technology (PAT), the online NIR spectroscopy together with the powder characterization facilitated a deeper understanding of the mixing process.

Continuous Manufacturing in Pharmaceutical Process Development and Manufacturing

The pharmaceutical industry has found new applications for the use of continuous processing for the manufacture of new therapies currently in development. The transformation has been encouraged by regulatory bodies as well as driven by cost reduction, decreased development cycles, access to new chemistries not practical in batch, improved safety, flexible manufacturing platforms, and improved product quality assurance. The transformation from batch to continuous manufacturing processing is the focus of this review. The review is limited to small, chemically synthesized organic molecules and encompasses the manufacture of both active pharmaceutical ingredients (APIs) and the subsequent drug product. Continuous drug product is currently used in approved processes. A few examples of production of APIs under current good manufacturing practice conditions using continuous processing steps have been published in the past five years, but they are lagging behind continuous drug product with respect to regulatory filings.

Methods to Assess Mixing of Pharmaceutical Powders

The pharmaceutical manufacturing process consists of several steps, each of which must be monitored and controlled to ensure quality standards are met. The level of blending has an impact on the final product quality; therefore, it is important to be able to monitor blending progress and identify an end-point. Currently, the pharmaceutical industry assesses blend content and uniformity through the extraction of samples using thief probes followed by analytical methods, such as spectroscopy, to determine the sample composition. The development of process analytical technologies (PAT) can improve product monitoring with the aim of increasing efficiency, product quality and consistency, and creating a better understanding of the manufacturing process. Ideally, these are inline methods to remove issues related to extractive sampling and allow direct monitoring of the system using various sensors. Many technologies have been investigated, including spectroscopic techniques such as near-infrared spectroscopy, velocimetric techniques that may use tracers, tomographic techniques, and acoustic emissions monitoring. While some techniques have demonstrated potential, many have significant disadvantages including the need for equipment modification, specific requirements of the material, expensive equipment, extensive analysis, the location of the probes may be critical and/or invasive, and lastly, the technique may only be applicable to the development phase. Both the advantages and disadvantages of the technologies should be considered in application to a specific system.

Predicting capsule fill weight from in-situ powder density measurements using terahertz reflection technology

The manufacturing of the majority of solid oral dosage forms is based on the densification of powder. A good understanding of the powder behavior is therefore essential to assure high quality drug products. This is particularly relevant for the capsule filling process, where the powder bulk density plays an important role in controlling the fill weight and weight variability of the final product. In this study we present a novel approach to quantitatively measure bulk density variations in a rotating container by means of terahertz reflection technology. The terahertz reflection probe was used to measure the powder density using an experimental setup that mimics a lab-scale capsule filling machine including a static sampling tool. Three different grades of α-lactose monohydrate excipients specially designed for inhalation application were systematically investigated at five compression stages. Relative densities predicted from terahertz reflection measurements were correlated to off-line weight measurements of the collected filled capsules. The predictions and the measured weights of the powder in the capsules were in excellent agreement, where the relative density measurements of Lactohale 200 showed the strongest correlation with the respective fill weight (R2=0.995). We also studied how the density uniformity of the powder bed was impacted by the dosing process and the subsequent filling of the holes (with excipient powder), which were introduced in the powder bed after the dosing step. Even though the holes seemed to be filled with new powder (by visual inspection), the relative density in these specific segments were found to clearly differ from the undisturbed powder bed state prior to dosing. The results demonstrate that it is feasible to analyze powder density variations in a rotating container by means of terahertz reflection measurements and to predict the fill weight of collected capsules.

Effect of Shear Applied During a Pharmaceutical Process on Near Infrared Spectra

This study describes changes observed in the near-infrared (NIR) diffuse reflectance (DR) spectra of pharmaceutical tablets after these tablets were subjected to different levels of strain (exposure to shear) during the mixing process. Powder shearing is important in the mixing of powders that are cohesive. Shear stress is created in a system by moving one surface over another causing displacements in the direction of the moving surface and is part of the mixing dynamics of particulates in many industries including the pharmaceutical industry. In continuous mixing, shear strain is developed within the process when powder particles are in constant movement and can affect the quality attributes of the final product such as dissolution. These changes in the NIR spectra could affect results obtained from NIR calibration models. The aim of the study was to understand changes in the NIR diffuse reflectance spectra that can be associated with different levels of strain developed during blend shearing of laboratory samples. Shear was applied using a Couette cell and tablets were produced using a tablet press emulator. Tablets with different shear levels were measured using NIR spectroscopy in the diffuse reflectance mode. The NIR spectra were baseline corrected to maintain the scattering effect associated with the physical properties of the tablet surface. Principal component analysis was used to establish the principal sources of variation within the samples. The angular dependence of elastic light scattering shows that the shear treatment reduces the size of particles and produces their uniform and highly isotropic distribution. Tablet compaction further reduces the diffuse component of scattering due to realignment of particles.

Integration of active pharmaceutical ingredient solid form selection and particle engineering into drug product design

This review seeks to offer a broad perspective that encompasses an understanding of the drug product attributes affected by active pharmaceutical ingredient (API) physical properties, their link to solid form selection and the role of particle engineering. While the crucial role of active pharmaceutical ingredient (API) solid form selection is universally acknowledged in the pharmaceutical industry, the value of increasing effort to understanding the link between solid form, API physical properties and drug product formulation and manufacture is now also being recognised.

A truly holistic strategy for drug product development should focus on connecting solid form selection, particle engineering and formulation design to both exploit opportunities to access simpler manufacturing operations and prevent failures. Modelling and predictive tools that assist in establishing these links early in product development are discussed. In addition, the potential for differences between the ingoing API physical properties and those in the final product caused by drug product processing is considered. The focus of this review is on oral solid dosage forms and dry powder inhaler products for lung delivery.

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl(®) tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution-alternating least squares (MCR-ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.