A Proposal for an Alternative Approach to Particle Size Method Development During Early-Stage Small Molecule Pharmaceutical Development

Past, Current, and Future: Application of Image Analysis in Small Molecule Pharmaceutical Development

The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis.

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

The aim of this work was to develop approaches to utilize whole particle distributions for both particle size and particle shape parameters to map the full range of particle properties in a curated dataset. It is hoped that such an approach may enable a more complete understanding of the particle landscape as a step towards improving the link between particle properties and processing behaviour. A 1-dimensional principal component analysis (PCA) approach was applied to create a 'morphological distribution landscape'. A dataset of imaged APIs, intermediates and excipients encompassing particle size, particle shape (elongation, length and width) and distribution shape was curated between 2008 and 2022. The curated dataset encompassed over 200 different materials, which included over 150 different APIs, and approximately 3500 unique samples. For the purposes of the current work, only API samples were included. The morphological landscape enables differentiation of materials of equivalent size but varying shape and vice versa. It is hoped that this type of approach can be utilised to better understand the influence of particle properties on pharmaceutical processing behaviour and thereby enable scientists to leverage historical knowledge to highlight and mitigate risks associated to materials of similar morphological nature.

The Role of Titanium Dioxide (E171) and the Requirements for Replacement Materials in Oral Solid Dosage Forms: An IQ Consortium Working Group Review

Titanium dioxide (in the form of E171) is a ubiquitous excipient in tablets and capsules for oral use. In the coating of a tablet or in the shell of a capsule the material disperses visible and UV light so that the contents are protected from the effects of light, and the patient or caregiver cannot see the contents within. It facilitates elegant methods of identification for oral solid dosage forms, thus aiding in the battle against counterfeit products. Titanium dioxide ensures homogeneity of appearance from batch to batch fostering patient confidence. The ability of commercial titanium dioxide to disperse light is a function of the natural properties of the anatase polymorph of titanium dioxide, and the manufacturing processes used to produce the material utilized in pharmaceuticals. In some jurisdictions E171 is being considered for removal from pharmaceutical products, as a consequence of it being delisted as an approved colorant for foods. At the time of writing, in the view of the authors, no system or material which could address both current and future toxicological concerns of Regulators and the functional needs of the pharmaceutical industry and patients has been identified. This takes into account the assessment of materials such as calcium carbonate, talc, isomalt, starch and calcium phosphates. In this paper an IQ Consortium team outlines the properties of titanium dioxide and criteria to which new replacement materials should be held.

Particle Property Characterization and Data Curation for Effective Powder Property Modeling in the Pharmaceutical Industry

Computational modeling, machine learning, and statistical data analysis are increasingly utilized to mitigate chemistry, manufacturing, and control failures related to particle properties in solid dosage form manufacture. Advances in particle characterization techniques and computational approaches provide unprecedented opportunities to explore relationships between particle morphology and drug product manufacturability. Achieving this, however, has numerous challenges such as producing and appropriately curating robust particle size and shape data. Addressing these challenges requires a harmonized strategy from material sampling practices, characterization technique selection, and data curation to provide data sets which are informative on material properties. Herein, common sources of error in particle characterization and data compression are reviewed, and a proposal for providing robust particle morphology (size and shape) data to support modeling efforts, approaches for data curation, and the outlook for modeling particle properties are discussed.

Quantitative Microscopy: Particle Size/Shape Characterization, Addressing Common Errors Using 'Analytics Continuum' Approach

Particle size/shape characterization of active pharmaceutical ingredient (API) is integral to successful product development. It is more of a correlative property than a decision-making measure. Though microscopy is the only technique that provides a direct measure of particle properties, it is neglected for reasons like non-repeatability and non-reproducibility which is often attributed to a) fundamental error, b) segregation error, c) human error, d) sample randomness, e) sample representativeness etc. Using the "Sucrose" as model sample, we propose "analytics continuum" approach that integrates optical microscope PSD measurements complimented by NIR spectroscopy-based trending analysis as a prescreening tool to demonstrate sample randomness and representativeness. Furthermore, plethora of statistical tests are utilized to infer population statistics. Subsequently, an attribute-based control chart and bootstrap-based confidence interval was developed to monitor product performance. A flowchart to serve as an elementary guideline is developed, which is then extended to handle more complex situations involving API crystallized from two different solvent systems. The results show that the developed methodology can be utilized as a quantitative procedure to assess the suitability of API/excipients from different batches or from alternate vendors and can significantly help in understanding the differences between material even on a minor scale.

Characterization of Controlled Release Microspheres Using FIB-SEM and Image-Based Release Prediction

For polymer-based controlled release drug products (e.g. microspheres and implants), active pharmaceutical ingredient distribution and microporosity inside the polymer matrix are critical for product performance, particularly drug release kinetics. Due to the decreasing domain size and increasing complexity of such products, conventional characterization and release test techniques are limited by their resolution and speed. In this study, samples of controlled release poly(lactic-co-glycolic acid) microspheres in the diameter range of 30-80 μm are investigated with focused ion beam scanning electron microscope imaging at 20 nm or higher resolution. Image data is quantified with artificial intelligence-based image analytics to provide size distributions of drug particles and pores within the microsphere sample. With an innovative image-based numerical simulation method, release profiles are predicted in a matter of days regardless of the designed release time. A mechanistic understanding on the impact of porosity to the interplays of drug, formulation, process, and dissolution was gained.