Morphological distribution mapping: Utilisation of modelling to integrate particle size and shape distributions

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

Targeting pentamidine towards CD44-overexpressing cells using hyaluronated lipid-polymer hybrid nanoparticles

Biodegradable nanocarriers possess enormous potential for use as drug delivery systems that can accomplish controlled and targeted drug release, and a wide range of nanosystems have been reported for the treatment and/or diagnosis of various diseases and disorders. Of the various nanocarriers currently available, liposomes and polymer nanoparticles have been extensively studied and some formulations have already reached the market. However, a combination of properties to create a single hybrid system can give these carriers significant advantages, such as improvement in encapsulation efficacy, higher stability, and active targeting towards specific cells or tissues, over lipid or polymer-based platforms. To this aim, this work presents the formulation of poly(lactic-co-glycolic) acid (PLGA) nanoparticles in the presence of a hyaluronic acid (HA)-phospholipid conjugate (HA-DPPE), which was used to anchor HA onto the nanoparticle surface and therefore create an actively targeted hybrid nanosystem. Furthermore, ionic interactions have been proposed for drug encapsulation, leading us to select the free base form of pentamidine (PTM-B) as the model drug. We herein report the preparation of hybrid nanocarriers that were loaded via ion-pairing between the negatively charged PLGA and HA and the positively charged PTM-B, demonstrating an improved loading capacity compared to PLGA-based nanoparticles. The nanocarriers displayed a size of below 150 nm, a negative zeta potential of -35 mV, a core-shell internal arrangement and high encapsulation efficiency (90%). Finally, the ability to be taken up and exert preferential and receptor-mediated cytotoxicity on cancer cells that overexpress the HA specific receptor (CD44) has been evaluated. Competition assays supported the hypothesis that PLGA/HA-DPPE nanoparticles deliver their cargo within cells in a CD44-dependent manner.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.

Determining the Impact of Roller Compaction Processing Conditions on Granulate and API Properties: Impact of Formulation API Load

Previous work demonstrated that roller compaction of a 40%w/w theophylline-loaded formulation resulted in granulate consisting of un-compacted fractions which were shown to constitute between 34 and 48%v/v of the granulate dependent on processing conditions. The active pharmaceutical ingredient (API) primary particle size within the un-compacted fraction was also shown to have undergone notable size reduction. The aim of the current work was to test the hypothesis that the observations may be more indicative of the relative compactability of the API due to the formulation being above the percolation threshold. This was done by assessing the impact of varied API loads in the formulation on the non-granulated fraction of the final granulate and the extent of attrition of API particles within the non-granulated fraction. The influence of processing conditions for all formulations was also investigated. The results verify that the observations, both of this study and the previous work, are not a consequence of exceeding the percolation threshold. The volume of un-compacted material within the granulate samples was observed to range between 34.7 and 65.5% depending on the API load and roll pressure, whilst the API attrition was equivalent across all conditions.

Solid-state analysis for pharmaceuticals: Pathways to feasible and meaningful analysis

The solid state of matter is the preferred starting point for designing a pharmaceutical product. This is driven by both patient preferences and the relative ease of supplying a solid pharmaceutical product with desired quality and performance. Solid form diversity is increasingly prevalent as a crucial element in designing these products, which underpins the importance of solid-state analytical methods. This paper provides a critical analysis of challenges related to solid-state analytics, as well as considerations and suggestions for feasible and meaningful pharmaceutical analysis.

In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.