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The last decade has seen Advanced Medicines Manufacturing (AMM) progress from isolated product developments to the creation of industry-academic centres of excellence, regulatory innovation progressing leading to new standards, and product commercialisation across multiple product formats. This paper examines these developments focusing on successful applications and strategies presented at the 2023 Symposium of the International Consortium for Advanced Medicines Manufacturing (ICAMM). Despite these exemplar applications, there remain significant challenges to the sector-wide adoption of AMM technologies. Drawing on Symposium delegate expert responses to open-ended questions, our coding-based thematic analysis suggest three primary enablers drive successful adoption of AMM technologies at scale, namely: the ability to leverage pre-competitive collaborations to challenge-based problem solving; information and knowledge sharing through centres of excellence; and the development of AMM specific regulatory standards. Further analysis of expert responses identified the emergence of a 'Platform creation' approach to AMM innovation; characterised by: i) New collaboration modes; ii) Exploration of common product-process platforms for new dosage forms and therapy areas; iii) Development of modular equipment assets that enable scale-out, and offer more decentralized or distributed manufacturing models; iv) Standards based on product-process platform archetypes; v) Implementation strategies where platform-thinking and AMM technologies can significantly reduce timelines between discovery, approval and GMP readiness. We provide a definition of the Platform creation concept for AMM and discuss the requirements for its systematic development.

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Crystallization kinetic parameter estimation is important for the classification, design, and scale-up of pharmaceutical manufacturing processes. This study investigates the impact of supersaturation and temperature on the induction time, nucleation rate, and growth rate for the compounds lamivudine (slow kinetics) and aspirin (fast kinetics). Adaptive Bayesian optimization (AdBO) has been used to predict experimental conditions that achieve target crystallization kinetic values for each of these parameters of interest. The use of AdBO to guide the choice of the experimental conditions reduced material usage up to 5-fold when compared to a more traditional statistical design of experiments (DoE) approach. The reduction in material usage demonstrates the potential of AdBO to accelerate process development as well as contribute to Net-Zero and green chemistry strategies. Implementation of AdBO can lead to reduced experimental effort and increase efficiency in pharmaceutical crystallization process development. The integration of AdBO into the experimental development workflows for crystallization development and kinetic experiments offers a promising avenue for advancing the field of autonomous data collection exploiting digital technologies and the development of sustainable chemical processes.

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Modifiers are commonly used in natural, biological, and synthetic crystallization to tailor the growth of diverse materials. Here, we identify tautomers as a new class of modifiers where the dynamic interconversion between solute and its corresponding tautomer(s) produces native crystal growth inhibitors. The macroscopic and microscopic effects imposed by inhibitor-crystal interactions reveal dual mechanisms of inhibition where tautomer occlusion within crystals that leads to natural bending, tunes elastic modulus, and selectively alters the rate of crystal dissolution. Our study focuses on ammonium urate crystallization and shows that the keto-enol form of urate, which exists as a minor tautomer, is a potent inhibitor that nearly suppresses crystal growth at select solution alkalinity and supersaturation. The generalizability of this phenomenon is demonstrated for two additional tautomers with relevance to biological systems and pharmaceuticals. These findings offer potential routes in crystal engineering to strategically control the mechanical or physicochemical properties of tautomeric materials.

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Scaling up and technology transfer of crystallization processes have been and continue to be a challenge. This is often due to the stochastic nature of primary nucleation, various scale dependencies of nucleation mechanisms, and the multitude of scale-up approaches. To better understand these dependencies, a series of isothermal induction time studies were performed across a range of vessel volumes, impeller types, and impeller speeds. From these measurements, the nucleation rate and growth time were estimated as parameters of an induction time distribution model. Then using machine learning techniques, correlations between the vessel hydrodynamic features, calculated from computational flow dynamic simulations, and nucleation kinetic parameters were analyzed. Of the 18 machine learning models trained, two models for the nucleation rate were found to have the best performance (in terms of % of predictions within experimental variance): a nonlinear random Forest model and a nonlinear gradient boosting model. For growth time, a nonlinear gradient boosting model was found to outperform the other models tested. These models were then ensembled to directly predict the probability of nucleation, at a given time, solely from hydrodynamic features with an overall root mean square error of 0.16. This work shows how machine learning approaches can be used to analyze limited datasets of induction times to provide insights into what hydrodynamic parameters should be considered in the scale-up of an unseeded crystallization process.

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OBJECTIVE: Particle shape can have a significant impact on the bulk properties of materials. This study describes the development and application of machine-learning models to predict the crystal shape of mefenamic acid recrystallized from organic solvents. METHODS: Crystals were grown in 30 different solvents to establish a dataset comprising solvent molecular descriptors, process conditions and crystal shape. Random forest classification models were trained on this data and assessed for prediction accuracy. RESULTS: The highest prediction accuracy of crystal shape was 93.5% assessed by fourfold cross-validation. When solvents were sequentially excluded from the training data, 32 out of 84 models predicted the shape of mefenamic acid crystals for the excluded solvent with 100% accuracy and a further 21 models had prediction accuracies from 50-100%. Reducing the feature set to only solvent physical property descriptors and supersaturations resulted in higher overall prediction accuracies than the models trained using all available or another selected subset of molecular descriptors. For the 8 solvents on which the models performed poorly (< 50% accuracy), further characterisation of crystals grown in these solvents resulted in the discovery of a new mefenamic acid solvate whereas all other crystals were the previously known form I. CONCLUSIONS: Random forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for mefenamic acid crystals grown in 20 out of the 28 solvents included in this work. Poor prediction accuracies for the remaining 8 solvents indicate that further factors will be required in the feature set to provide a more generalized predictive morphology model.

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Changes in temperature can significantly affect spectroscopic-based methods for in situ monitoring of processes. As varying temperature is inherent to many processes, associated temperature effects on spectra are unavoidable, which can hinder solute concentration determination. Ultraviolet (UV) and mid-infrared (IR) data were acquired for l-ascorbic acid (LAA) in MeCN/H2O (80:20 w/w) at different concentrations and temperatures. For both techniques, global partial least squares (PLS) models for prediction of LAA concentration constructed without preprocessing of the spectra required a high number of latent variables to account for the effects of temperature on the spectra (root mean square error of cross validation (RMSECV) of 0.18 and 0.16 g/100 g solvent, for UV and IR datasets, respectively). The PLS models constructed on the first derivative spectra required fewer latent variables, yielding variable results in accuracy (RMSECV of 0.23 and 0.06 g/100 g solvent, respectively). Corresponding isothermal local models constructed indicated improved model performance that required fewer latent variables in the absence of temperature effects (RMSECV of 0.01 and 0.04 g/100 g solvent, respectively). Temperature correction of the spectral data via loading space standardization (LSS) enabled the construction of global models using the same number of latent variables as the corresponding local model, which exhibited comparable model performance (RMSECV of 0.06 and 0.04 g/100 g solvent, respectively). The additional chemometric effort required for LSS is justified if prediction of solute concentration is required for in situ monitoring and control of cooling crystallization with an accuracy and precision approaching that attainable using an isothermal local model. However, the model performance with minimal preprocessing may be sufficient, for example, in the early phase development of a cooling crystallization process, where high accuracy is not always required. UV and IR spectrometries were used to determine solubility diagrams for LAA in MeCN/H2O (80:20 w/w), which were found to be accurate compared to those obtained using the traditional techniques of transmittance and gravimetric measurement. For both UV and IR spectrometries, solubility values obtained from models with LSS temperature correction were in better agreement with those determined gravimetrically. In this first example of the application of LSS to UV spectra, significant improvement in the predicted solute concentration is achieved with the additional chemometric effort. There is no extra experimental burden associated with the use of LSS if a structured approach is employed to acquire calibration data that account for both temperature and concentration.

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Amorphous solid dispersions (ASDs) are formulations with enhanced drug solubility and dissolution rate compared to their crystalline counterparts, however, they can be inherently thermodynamically unstable. This can lead to amorphous phase separation and drug re-crystallisation, phenomena that are typically faster and more dominant at the product's surfaces. This study investigates the use of high-resolution time of flight-secondary ion mass spectrometry (ToF-SIMS) imaging as a surface analysis technique combined with image-analysis for the early detection, monitoring and quantification of surface amorphous phase separation in ASDs. Its capabilities are demonstrated for two pharmaceutically relevant ASD systems with distinct re-crystallisation behaviours, prepared using hot melt extrusion (HME) followed by pelletisation or grinding: (1) paracetamol-hydroxypropyl methylcellulose (PCM-HPMC) pellets with drug loadings of 10%-50% w/w and (2) indomethacin-polyvinylpyrrolidone (IND-PVP) ground material with drug loadings of 20%-85% w/w. PCM-HPMC pellets showed intense phase separation, reaching 100% PCM surface coverage within 1-5 months. In direct comparison, IND-PVP HME ground material was more stable with only a moderate formation of isolated IND-rich clusters. Image analysis allowed the reliable detection and quantification of local drug-rich clusters. An Avrami model was applied to determine and compare phase separation kinetics. The combination of chemical sensitivity and high spatial resolution afforded by SIMS was crucial to enable the study of early phase separation and re-crystallisation at the surface. Compared with traditional methods used to detect crystalline material, such as XRPD, we show that ToF-SIMS enabled detection of surface physical instability already at early stages of drug cluster formation in the first days of storage.

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Characterising powder flowability can be challenging when sample quantity is insufficient for a conventional shear cell test, especially in the pharmaceutical industry, where the cost of the active pharmaceutical ingredient (API) used is expensive at an early stage in the drug product development. A previous study demonstrated that powder flowability could be predicted based on powder physical properties and cohesiveness using a small quantity of powder samples (50 mg), but it remained an open question regarding the accuracy of the prediction compared to that measured using industry-standard shear cell testers and its potential to substitute the existing testers. In this study, 16 pharmaceutical powders were selected for a detailed comparative study of the predictive model. The flowability of the powders was predicted using a Bond number and given consolidation stresses, σ1, coupled with the model, where the Bond number represents powder cohesiveness. Compared to the measurements using a Powder Flow Tester (Brookfield) and an FT4 (Freeman Technology) Powder Rheometer shear cell tester, the results showed a good agreement between the predictions and the measurements (<22 % difference) from the two shear cell testers with different consolidation stresses, especially for cohesive materials. The model correctly predicts the class of flowability for 14 and 12 of the 16 powders for the PFT and the FT4, respectively. The study demonstrated that the prediction method of powder flowability using a small sample (50 mg) could substitute a standard shear cell test (>15 g) if the available amount of sample is small.

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Polymorphism and crystal habit play vital roles in dictating the properties of crystalline materials. Here, the structure and properties of oxcarbazepine (OXCBZ) form III are reported along with the occurrence of twisted crystalline aggregates of this metastable polymorph. OXCBZ III can be produced by crystallization from the vapor phase and by recrystallization from solution. The crystallization process used to obtain OXCBZ III is found to affect the pitch, with the most prominent effect observed from the sublimation-grown OXCBZ III material where the pitch increases as the length of aggregates increases. Sublimation-grown OXCBZ III follows an unconventional mechanism of formation with condensed droplet formation and coalescence preceding nucleation and growth of aggregates. A crystal structure determination of OXCBZ III from powder X-ray diffraction methods, assisted by crystal structure prediction (CSP), reveals that OXCBZ III, similar to carbamazepine form II, contains void channels in its structure with the channels, aligned along the c crystallographic axis, oriented parallel to the twist axis of the aggregates. The likely role of structural misalignment at the lattice or nanoscale is explored by considering the role of molecular and closely related structural impurities informed by crystal structure prediction.

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Small-scale crystallization experiments (1-8 mL) are widely used during early-stage crystallization process development to obtain initial information on solubility, metastable zone width, as well as attainable nucleation and/or growth kinetics in a material-efficient manner. Digital imaging is used to monitor these experiments either providing qualitative information or for object detection coupled with size and shape characterization. In this study, a novel approach for the routine characterization of image data from such crystallization experiments is presented employing methodologies for direct image feature extraction. A total of 80 image features were extracted based on simple image statistics, histogram parametrization, and a series of targeted image transformations to assess local grayscale characteristics. These features were utilized for applications of clear/cloud point detection and crystal suspension density prediction. Compared to commonly used transmission-based methods (mean absolute error 8.99 mg/mL), the image-based detection method is significantly more accurate for clear and cloud point detection with a mean absolute error of 0.42 mg/mL against a manually assessed ground truth. Extracted image features were further used as part of a partial least-squares regression (PLSR) model to successfully predict crystal suspension densities up to 40 mg/mL (R 2 > 0.81, Q 2 > 0.83). These quantitative measurements reliably provide crucial information on composition and kinetics for early parameter estimation and process modeling. The image analysis methodologies have a great potential to be translated to other imaging techniques for process monitoring of key physical parameters to accelerate the development and control of particle/crystallization processes.

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Solution crystallization is a part of the synthesis of materials ranging from geological and biological minerals to pharmaceuticals, fine chemicals, and advanced electronic components. Attempts to predict the structure, growth rates and properties of emerging crystals have been frustrated, in part, by the poor understanding of the correlations between the oligomeric state of the solute, the growth unit, and the crystal symmetry. To explore how a solute monomer or oligomer is selected as the unit that incorporates into kinks and how crystal symmetry impacts this selection, we combine scanning probe microscopy, optical spectroscopy, and all-atom molecular simulations using as examples two organic materials, olanzapine (OZPN) and etioporphyrin I (EtpI). The dominance of dimeric structures in OZPN crystals has spurred speculation that the dimers preform in the solution, where they capture the majority of the solute, and then assemble into crystals. By contrast, EtpI in crystals aligns in parallel stacks of flat EtpI monomers unrelated by point symmetry. Raman and absorption spectroscopies show that solute monomers are the majority solute species in solutions of both compounds. Surprisingly, the kinetics of incorporation of OZPN into kinks is bimolecular, indicating that the growth unit is a solute dimer, a minority solution component. The disconnection between the dominant solute species, the growth unit, and the crystal symmetry is even stronger with EtpI, for which the (010) face grows by incorporating monomers, whereas the growth unit of the (001) face is a dimer. Collectively, the crystallization kinetics results with OZPN and EtpI establish that the structures of the dominant solute species and of the incorporating solute complex do not correlate with the symmetry of the crystal lattice. In a broader context, these findings illuminate the immense complexity of crystallization scenarios that need to be explored on the road to the understanding and control of crystallization.

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Heat transfer coefficients in a continuous oscillatory baffled crystallizer (COBC) with a nominal internal diameter of 15 mm have been determined as a function of flow and oscillatory conditions typically used under processing conditions. Residence time distribution measurements show a near-plug flow with high Peclet numbers on the order of 100-1000 s, although there was significant oscillation damping in longer COBC setups. Very rapid heat transfer was found under typical conditions, with overall heat transfer coefficients on the order of 100 s W m-2 K-1. Furthermore, poor mixing in the COBC cooling jacket was observed when lower jacket flow rates were implemented in an attempt to decrease the rate of heat transfer in order to achieve more gradual temperature profile along the crystallizer length. Utilizing the experimentally determined overall heat transfer coefficients, a theoretical case study is presented to investigate the effects of the heat transfer rate on temperature and supersaturation profiles and to highlight potential fouling issues during a continuous plug flow cooling crystallization.

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Water-responsive materials undergo reversible shape changes upon varying humidity levels. These mechanically robust yet flexible structures can exert substantial forces and hold promise as efficient actuators for energy harvesting, adaptive materials and soft robotics. Here we demonstrate that energy transfer during evaporation-induced actuation of nanoporous tripeptide crystals results from the strengthening of water hydrogen bonding that drives the contraction of the pores. The seamless integration of mobile and structurally bound water inside these pores with a supramolecular network that contains readily deformable aromatic domains translates dehydration-induced mechanical stresses through the crystal lattice, suggesting a general mechanism of efficient water-responsive actuation. The observed strengthening of water bonding complements the accepted understanding of capillary-force-induced reversible contraction for this class of materials. These minimalistic peptide crystals are much simpler in composition compared to natural water-responsive materials, and the insights provided here can be applied more generally for the design of high-energy molecular actuators.

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PURPOSE: Spray drying plays an important role in the pharmaceutical industry for product development of sensitive bio-pharmaceutical formulations. Process design, implementation and optimisation require in-depth knowledge of process-product interactions. Here, an integrated approach for the rapid, early-stage spray drying process development of trehalose and glucagon on lab-scale is presented. METHODS: Single droplet drying experiments were used to investigate the particle formation process. Process implementation was supported using in-line process analytical technology within a data acquisition framework recording temperature, humidity, pressure and feed rate. During process implementation, off-line product characterisation provided additional information on key product properties related to residual moisture, solid state structure, particle size/morphology and peptide fibrillation/degradation. RESULTS: A psychrometric process model allowed the identification of feasible operating conditions for spray drying trehalose, achieving high yields of up to 84.67%, and significantly reduced levels of residual moisture and particle agglomeration compared to product obtained during non-optimal drying. The process was further translated to produce powders of glucagon and glucagon-trehalose formulations with yields of >83.24%. Extensive peptide aggregation or degradation was not observed. CONCLUSIONS: The presented data-driven process development concept can be applied to address future isolation problems on lab-scale and facilitate a systematic implementation of spray drying for the manufacturing of sensitive bio-pharmaceutical formulations.

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The symmetries of a crystal are notoriously uncorrelated to those of its constituent molecules. This symmetry breaking is typically thought to occur during crystallization. Here we demonstrate that one of the two symmetry elements of olanzapine crystals, an inversion centre, emerges in solute dimers extant in solution prior to crystallization. We combine time-resolved in situ scanning probe microscopy to monitor the crystal growth processes with all-atom molecular dynamics simulations. We show that crystals grow non-classically, predominantly by incorporation of centrosymmetric dimers. The growth rate of crystal layers exhibits a quadratic dependence on the solute concentration, characteristic of the second-order kinetics of the incorporation of dimers, which exist in equilibrium with a majority of monomers. We show that growth by dimers is preferred due to overwhelming accumulation of adsorbed dimers on the crystal surface, where it is complemented by dimerization and expedites dimer incorporation into growth sites.

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Silk has a long track record of clinical use in the human body, and new formulations, including silk nanoparticles, continue to reveal the promise of this natural biopolymer for healthcare applications. Native silk fibroin can be isolated directly from the silk gland, but generating sufficient material for routine studies is difficult. Consequently, silk fibroin, typically extracted from cocoons, serves as the source for nanoparticle formation. This silk requires extensive processing (e.g., degumming, dissolution, etc.) to yield a hypoallergenic aqueous silk stock, but the impact of processing on nanoparticle production and characteristics is largely unknown. Here, manual and microfluidic-assisted silk nanoparticle manufacturing from 60- and 90-min degummed silk yielded consistent particle sizes (100.9-114.1 nm) with low polydispersity. However, the zeta potential was significantly lower (P < 0.05) for microfluidic-manufactured nanoparticles (-28 to -29 mV) than for manually produced nanoparticles (-39 to -43 mV). Molecular weight analysis showed a nanoparticle composition similar to that of the silk fibroin starting stock. Reducing the molecular weight of silk fibroin reduced the particle size for degumming times ≤30 min, whereas increasing the molecular weight polydispersity improved the nanoparticle homogeneity. Prolonged degumming (>30 min) had no significant effect on particle attributes. Overall, the results showed that silk fibroin processing directly impacts nanoparticle characteristics.

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Optimized physical properties (e.g., bulk, surface/interfacial, and mechanical properties) of active pharmaceutical ingredients (APIs) are key to the successful integration of drug substance and drug product manufacturing, robust drug product manufacturing operations, and ultimately to attaining consistent drug product critical quality attributes. However, an appreciable number of APIs have physical properties that cannot be managed via routes such as form selection, adjustments to the crystallization process parameters, or milling. Approaches to control physical properties in innovative ways offer the possibility of providing additional and unique opportunities to control API physical properties for both batch and continuous drug product manufacturing, ultimately resulting in simplified and more robust pharmaceutical manufacturing processes. Specifically, diverse opportunities to significantly enhance API physical properties are created if allowances are made for generating co-processed APIs by introducing nonactive components (e.g., excipients, additives, carriers) during drug substance manufacturing. The addition of a nonactive coformer during drug substance manufacturing is currently an accepted approach for cocrystals, and it would be beneficial if a similar allowance could be made for other nonactive components with the ability to modify the physical properties of the API. In many cases, co-processed APIs could enable continuous direct compression for small molecules, and longer term, this approach could be leveraged to simplify continuous end-to-end drug substance to drug product manufacturing processes for both small and large molecules. As with any novel technology, the regulatory expectations for co-processed APIs are not yet clearly defined, and this creates challenges for commercial implementation of these technologies by the pharmaceutical industry. The intent of this paper is to highlight the opportunities and growing interest in realizing the benefits of co-processed APIs, exemplified by a body of academic research and industrial examples. This work will highlight reasons why co-processed APIs would best be considered as drug substances from a regulatory perspective and emphasize the areas where regulatory strategies need to be established to allow for commercialization of innovative approaches in this area.

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The application of X-ray microtomography for quantitative structural analysis of pharmaceutical multi-particulate systems was demonstrated for commercial capsules, each containing approximately 300 formulated ibuprofen pellets. The implementation of a marker-supported watershed transformation enabled the reliable segmentation of the pellet population for the 3D analysis of individual pellets. Isolated translation- and rotation-invariant object cross-sections expanded the applicability to additional 2D image analysis techniques. The full structural characterisation gave access to over 200 features quantifying aspects of the pellets' size, shape, porosity, surface and orientation. The extracted features were assessed using a ReliefF feature selection method and a supervised Support Vector Machine learning algorithm to build a model for the detection of broken pellets within each capsule. Data of three features from distinct structure-related categories were used to build classification models with an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets. This approach to extract quantitative information on particle quality attributes combined with advanced data analysis strategies has clear potential to directly inform manufacturing processes, accelerating development and optimisation.

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The Publisher regrets that this article is an accidental duplication of a published article,https://doi.org/10.1016/j.ijpx.2020.100041. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.