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.

Non-contact Laser Interferometer Method to Characterize Tablet Punches: New Methodology to Assess Surface Roughness.

Sticking to tablet punches is a major issue during drug product manufacturing. Research has shown that sticking involves the interrelationship of powder properties, compression force, length of manufacturing runs and punch quality. Here, we present a novel non-destructive methodology to study the surface metrology of punches to monitor them over their lifetime. This investigation used a non-contact laser interferometer to characterise roughness of commercial standard S7 steel punches coated with chrome that were originally used for commercial scale production that developed a sticking issue. During the development, this phenomenon had not been observed and was not considered a scale-up risk. The profilometer was used to examine the complete surface of these punches to investigate whether they met the acceptability criteria based on BS_ISO_18804 tooling standard. To improve data analysis during changeover, a 3D-printed holder was designed to enable analysis with minimal set-up requirements. Upon investigation, the punches were found to be of an unacceptable roughness and, particularly rough areas of the punch surface profiled, correlated well with areas of visually pronounced sticking. This non-destructive method can be used to produce a more detailed characterisation of punch roughness to ensure surfaces are of an acceptable quality after treatment with coatings.

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.

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.

The Australian National Rabbit Database: 50 yr of population monitoring of an invasive species.

With ongoing introductions into Australia since the 1700s, the European rabbit (Oryctolagus cuniculus) has become one of the most widely distributed and abundant vertebrate pests, adversely impacting Australia's biodiversity and agroeconomy. To understand the population and range dynamics of the species and its impacts better, occurrence and abundance data have been collected by researchers and citizens from sites covering a broad spectrum of climatic and environmental conditions in Australia. The lack of a common and accessible repository for these data has, however, limited their use in determining important spatiotemporal drivers of the structure and dynamics of the geographical range of rabbits in Australia. To meet this need, we created the Australian National Rabbit Database, which combines more than 50 yr of historical and contemporary survey data collected from throughout the range of the species in Australia. The survey data, obtained from a suite of complementary monitoring methods, were combined with high-resolution weather, climate, and environmental information, and an assessment of data quality. The database provides records of rabbit occurrence (689,265 records) and abundance (51,241 records, >120 distinct sites) suitable for identifying the spatiotemporal drivers of the rabbit's distribution and for determining spatial patterns of variation in its key life-history traits, including maximum rates of population growth. Because all data are georeferenced and date stamped, they can be coupled with information from other databases and spatial layers to explore the potential effects of rabbit occurrence and abundance on Australia's native wildlife and agricultural production. The Australian National Rabbit Database is an important tool for understanding and managing the European rabbit in its invasive range and its effects on native biodiversity and agricultural production. It also provides a valuable resource for addressing questions related to the biology, success, and impacts of invasive species more generally. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis.

The impact of pharmaceutical materials properties on drug product quality and manufacturability is well recognised by the industry. An ongoing effort across industry and academia, the Manufacturing Classification System consortium, aims to gather the existing body of knowledge in a common framework to provide guidance on selection of appropriate manufacturing technologies for a given drug and/or guide optimization of the physical properties of the drug to facilitate manufacturing requirements for a given processing route. Simultaneously, material scientists endeavour to develop characterisation methods such as size, shape, surface area, density, flow and compactibility that enable a stronger understanding of materials powder properties. These properties are routinely tested drug product development and advances in instrumentation and computing power have enabled novel characterisation methods which generate larger, more complex data sets leading to a better understanding of the materials. These methods have specific requirements in terms of data management and analysis. An appropriate data management strategy eliminates time-consuming data collation steps and enables access to data collected for multiple methods and materials simultaneously. Methods ideally suited to extract information from large, complex data sets such as multivariate projection methods allow simpler representation of the variability contained within the data and easier interpretation of the key information it contains. In this review, an overview of the current knowledge and challenges introduced by modern pharmaceutical material characterisation methods is provided. Two case studies illustrate how the incorporation of multivariate analysis into the material sciences workflow facilitates a better understanding of materials.

Manufacturing classification system in the real world: factors influencing manufacturing process choices for filed commercial oral solid dosage formulations, case studies from industry and considerations for continuous processing.

Following the first Manufacturing Classification System (MCS) paper, the team conducted surveys to establish which active pharmaceutical ingredient (API) properties were important when selecting or modifying materials to enable an efficient and robust pharmaceutical manufacturing process. The most commonly identified factors were (1) API particle size: small particle sizes are known to increase risk of processing issues; (2) Drug loading in the formulation: high drug loadings allow less opportunity to mitigate poor API properties through the use of excipients. The next step was to establish linkages with process decisions by identifying publicly-available proxies for these important parameters: dose (in place of drug loading) and BCS class (in place of particle size). Poorly-soluble API were seen as more likely to have controlled (smaller) particle size than more highly soluble API. Analysis of 435 regulatory filings revealed that higher doses and more poorly-soluble API was associated with more complex processing routes. Replacing the proxy factors with the original parameters should give the opportunity to demonstrate stronger trends. This assumption was tested by accessing a dataset relating to commercial tablet products. This showed that, for dry processes, a larger particle size was associated with higher achievable drug loading as determined by percolation threshold.

Development of a material sparing bulk density test comparable to a standard USP method for use in early development of API's.

Bulk density can be a key indicator of performance, and may influence choice of formulation route of materials in pharmaceutical development. During early development, the cost of API's can be expensive and the availability of material for powder property analysis is limited. The aim of this work was to investigate a suitable small-scale, low material requirement, bulk density test which would provide comparable data to the recommended large volume USP test. Materials with a range of morphological characteristics typically seen in the pharmaceutical industry were assessed to ensure that methods were suitably robust. It was found that the USP II "low volume" test does not give equivalent results to other tests in the USP, across the range of materials. An alternative test based on the FT4 powder rheometer at a scale of 25 mL gave results equivalent to the large volume USP I standard test. The use of smaller 10-mL methods was also found to give acceptable results for materials that were considered well-behaved but were more variable with difficult to handle materials with low bulk density.

A proposal for a drug product Manufacturing Classification System (MCS) for oral solid dosage forms.

This paper proposes the development of a drug product Manufacturing Classification System (MCS) based on processing route. It summarizes conclusions from a dedicated APS conference and subsequent discussion within APS focus groups and the MCS working party. The MCS is intended as a tool for pharmaceutical scientists to rank the feasibility of different processing routes for the manufacture of oral solid dosage forms, based on selected properties of the API and the needs of the formulation. It has many applications in pharmaceutical development, in particular, it will provide a common understanding of risk by defining what the "right particles" are, enable the selection of the best process, and aid subsequent transfer to manufacturing. The ultimate aim is one of prediction of product developability and processability based upon previous experience. This paper is intended to stimulate contribution from a broad range of stakeholders to develop the MCS concept further and apply it to practice. In particular, opinions are sought on what API properties are important when selecting or modifying materials to enable an efficient and robust pharmaceutical manufacturing process. Feedback can be given by replying to our dedicated e-mail address (mcs@apsgb.org); completing the survey on our LinkedIn site; or by attending one of our planned conference roundtable sessions.

Imaging dehydration kinetics of a channel hydrate form of the HIV-1 attachment inhibitor prodrug BMS-663068.

An analysis of the free acid form of the HIV-1 attachment inhibitor prodrug BMS-663068-01 revealed a reversible moisture sorption event in the 42%-46% relative humidity (RH) range. An existing single-crystal analysis indicated that these observations were due to the formation of a nonstoichiometric channel hydrate. This effect was reproducible on repeated cycles, suggesting that the material's structural integrity was not compromised because of the interconversion process. Small, reversible, and predictable changes in the atomic structure were observed by solid-state nuclear magnetic resonance (ssNMR). Atomic force microscopy (AFM) and environmental scanning electron microscopy (ESEM) could discern changes in surface topography as a function of RH. Surface cracks were visible at 25% RH, most of which disappeared at 60% RH. This change was reversible on reducing the RH, with cracks reappearing in the same locations. A reduction in surface roughness was seen at high humidity, which was consistent with the uptake of moisture causing surface swelling. The observations by AFM/ESEM were consistent with the atomic alterations seen with ssNMR. Changes in unit cell dimensions are not uncommon with channel hydrates as the crystal lattice expands or contracts when the crystal structure absorbs/desorbs water, but concomitant, reversible surface morphology property changes have not been widely reported.

Investigating the applicability of inverse gas chromatography to binary powdered systems: an application of surface heterogeneity profiles to understanding preferential probe-surface interactions.

The aim of this study was to investigate the applicability of surface energy characterization tools such as inverse gas chromatography for the analysis of binary systems. Drug substance was coated with two grades of silicon dioxide and the surface energy characteristics determined using a surface energy analyser. The results demonstrated that the measured dispersive surface energy of such intermediate samples were as a consequence of probe interactions with both constituent components, however, the degree and order of interaction with each species was related to surface energy heterogeneity and surface availability. A method to predict the degree of probe-surface preferentiality within the intermediate samples was applied to the data, demonstrating to closely match the measured data whilst suggesting notable differences in probe-surface preferentiality. Specific probe interactions were also assessed and the results suggested that probe surface preferentiality was not equivalent to that of the dispersive probes, possibly due to differences in ranges of the dispersive/specific forces. An equivalent physically mixed sample was analysed and the results demonstrated that the measured heterogeneity curve mirrored that of the pure drug substance suggesting that the driver for probe interaction is different for the physically mixed and the coated intermediate samples.

Formulation and process design for a solid dosage form containing a spray-dried amorphous dispersion of ibipinabant.

Amorphous forms of poorly soluble drugs are more frequently being incorporated into solid dispersions for administration and extensive research has led to a reasonable understanding of how these dispersions, although still kinetically unstable, improve stability relative to the pure amorphous form. There remains however a paucity of literature describing the effects on such solid dispersions of subsequent processing into solid dosage forms such as tablets. This paper addresses this area by looking at the effects of the addition of common excipients and different manufacturing routes on the stability of a spray-dried dispersion (SDD) of the cannabinoid CB-1 antagonist, ibipinabant. A marked difference in physical stability of tablets was seen with the different fillers with microcrystalline cellulose (MCC) giving the best stability profile. It was found that minimising the number of compression steps led to improved formulation stability with a direct compression process giving the best results. Increased levels of crystallinity were seen in coated tablets most likely due to the exposure of the amorphous matrix to moisture and heat during the coating process. DSIMS analysis of the SDD particles indicated increased levels of polymer on the surface.

Surface energy analysis as a tool to probe the surface energy characteristics of micronized materials--a comparison with inverse gas chromatography.

This study investigates the impact of micronization on the measured surface energy characteristics of an active pharmaceutical ingredient (API), ibipinabant, by inverse gas chromatography (IGC) using both a fixed probe concentration, commonly used in standard IGC methods, and a fixed probe surface coverage approach applied by the surface energy analyzer (SEA), a next generation IGC system. The IGC measurements indicate an initial increase in surface energy, going from un-micronized to micronized, followed by a reduction in surface energy with increasing micronization extent. This was attributable to the change in the retention behaviour of the dispersive probes as a consequence of the change in the probe surface coverage rather than a change in the actual surface energy of the materials being analysed. It was observed in the SEA data that micronization leads to an increase in the measured dispersive surface energy of the drug substance with increasing micronization extent. The increase in surface energy is primarily due to the generation of new, higher energy interaction sites, although a small additional increase is also observed which is related to the increase in the number and distribution of high energy sites. The results demonstrate that in order to obtain comparable surface energetic data between batches with varied surface area, and presumably between different materials, results should be obtained at a specific, and constant, probe surface coverage.

Physical stability and recrystallization kinetics of amorphous ibipinabant drug product by fourier transform raman spectroscopy.

The solid-state physical stability and recrystallization kinetics during storage stability are described for an amorphous solid dispersed drug substance, ibipinabant, at a low concentration (1.0%, w/w) in a solid oral dosage form (tablet). The recrystallization behavior of the amorphous ibipinabant-polyvinylpyrrolidone solid dispersion in the tablet product was characterized by Fourier transform (FT) Raman spectroscopy. A partial least-square analysis used for multivariate calibration based on Raman spectra was developed and validated to detect less than 5% (w/w) of the crystalline form (equivalent to less than 0.05% of the total mass of the tablet). The method provided reliable and highly accurate predictive crystallinity assessments after exposure to a variety of stability storage conditions. It was determined that exposure to moisture had a significant impact on the crystallinity of amorphous ibipinabant. The information provided by the method has potential utility for predictive physical stability assessments. Dissolution testing demonstrated that the predicted crystallinity had a direct correlation with this physical property of the drug product. Recrystallization kinetics was measured using FT Raman spectroscopy for the solid dispersion from the tablet product stored at controlled temperature and relative humidity. The measurements were evaluated by application of the Johnson-Mehl-Avrami (JMA) kinetic model to determine recrystallization rate constants and Avrami exponent (n = 2). The analysis showed that the JMA equation could describe the process very well, and indicated that the recrystallization kinetics observed was a two-step process with an induction period (nucleation) followed by rod-like crystal growth.

The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets.

The objective of this work was to apply artificial neural networks (ANNs) to examine the relative importance of various factors, both formulation and process, governing the in-vitro dissolution from enteric-coated sustained release (SR) minitablets. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of the various formulation and processing variables in determining minitablet dissolution rate. Both forward and backward stepwise algorithms were used as well as genetic algorithms. Networks were subsequently trained using the back propagation algorithm in order to check whether or not the IFS process had correctly located any unimportant inputs. IFS gave consistent rankings for the importance of the various formulation and processing variables in determining the release of drug from minitablets. Consistent ranking was achieved for both indices of the release process; ie, the time taken for release to commence through the enteric coat (T(lag)) and that for the drug to diffuse through the SR matrix of the minitablet into the dissolution medium (T9(0-10)). In the case of the T(lag) phase, the main coating parameters, along with the original batch blend size and the blend time with lubricant, were found to have most influence. By contrast, with the T(90-10 phase), the amounts of matrix forming polymer and direct compression filler were most important. In the subsequent training of the ANNs, removal of inputs regarded as less important led to improved network performance. ANNs were capable of ranking the relative importance of the various formulations and processing variables that influenced the release rate of the drug from minitablets. This could be done for all main stages of the release process. Subsequent training of the ANN verified that removal of less relevant inputs from the training process led to an improved performance from the ANN.