Correlative Image-Based Release Prediction and 3D Microstructure Characterization for a Long Acting Parenteral Implant

Microstructure Formation and Characterization of Long-Acting Injectable Microspheres: The Gateway to Fully Controlled Drug Release Pattern

Long-acting injectable microspheres have been on the market for more than three decades, but if calculated on the brand name, only 12 products have been approved by the FDA due to numerous challenges in achieving a fully controllable drug release pattern. Recently, more and more researches on the critical factors that determine the release kinetics of microspheres shifted from evaluating the typical physicochemical properties to exploring the microstructure. The microstructure of microspheres mainly includes the spatial distribution and the dispersed state of drug, PLGA and pores, which has been considered as one of the most important characteristics of microspheres, especially when comparative characterization of the microstructure (Q3) has been recommended by the FDA for the bioequivalence assessment. This review extracted the main variables affecting the microstructure formation from microsphere formulation compositions and preparation processes and highlighted the latest advances in microstructure characterization techniques. The further understanding of the microsphere microstructure has significant reference value for the development of long-acting injectable microspheres, particularly for the development of the generic microspheres.

Focused ion beam-scanning electron microscopy provides novel insights of drug delivery phenomena

Scanning electron microscopy (SEM) has long been a standard tool for morphological analyses, providing sub micrometer resolution of pharmaceutical formulations. However, analysis of internal morphologies of such formulations can often be biased due to the introduction of artifacts that originate from sample preparation. A recent advancement in SEM, is the focused ion beam scanning electron microscopy (FIB-SEM). This technique uses a focused ion beam (FIB) to remove material with nanometer precision, to provide virtually sample-independent access to sub-surface structures. The FIB can be combined with SEM imaging capabilities within the same instrumentation. As a powerful analytical tool, electron microscopy and FIB-milling are performed sequentially to produce high-resolution 3D models of structural peculiarities of diverse drug delivery systems or their behavior in a biological environment, i.e. intracellular or -tissue distribution. This review paper briefly describes the technical background of the method, outlines a wide array of potential uses within the drug delivery field, and focuses on intracellular transport where high-resolution images are an essential tool for mechanistical insights.

Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus

Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.