Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Aqueous suspensions containing crystalline drug in the sub-micron range is a favorable platform for long-acting injectables where particle size can be used to obtain a desired plasma-concentration profile. Stabilizers are added to the suspensions and screened extensively to define the optimal formulation composition. In the initial formulation screening the amount of drug compound can be limited, necessitating milling methods for small-volume screening predictable for scale-up. Hence, adaptive focused ultrasound was investigated as a potential milling method for rapid small-volume suspensions by identifying the critical process parameters during preparation. Suspensions containing drug compounds with different mechanical properties and thereby grindability, i.e., cinnarizine, haloperidol, and indomethacin with brittle, elastic, and plastic properties, respectively, were investigated to gain an understanding of the manufacturing with adaptive focused acoustics as well as comparison to already established milling techniques. Using a DoE-design, peak incident power was identified as the most crucial process parameter impacting the milling process for all three compounds. It was possible to decrease the sizes of drug particles to micron range after one minute of focused ultrasound exposure which was superior compared to other milling techniques (e.g., non-focused ultrasound exposure). The addition of milling beads decreased the drug particle sizes even further, thus to a lower degree than other already established milling techniques such as milling by dual centrifugation. This study thereby demonstrated that adaptive focused ultrasonication was a promising method for rapid homogenization and particle size reduction to micron range for different compounds varying in grindability without altering the crystalline structure.

OBJECTIVE: Twin-screw wet granulation (TSWG) is a manufacturing process that offers several advantages for the processing of water-insoluble active pharmaceutical ingredients (APIs) and has been used for increasing the solubility and dissolution rates. Here we introduce a novel TSWG approach with reduced downstream processing steps by using non-volatile solvents as granulating binders.
METHODS: Herein, TSWG was carried out using Transcutol a non-volatile protic solvent as a granulating binder and dissolution enhancer of ibuprofen (IBU) blends with cellulose polymer grades (Pharmacoat® 603, Affinisol™, and AQOAT®).
RESULTS: The physicochemical characterisation of the produced granules showed excellent powder flow and the complete transformation of IBU into the amorphous state. Dissolution studies presented immediate release rates for all IBU formulations due to the high drug-polymer miscibility and the Transcutol solubilising capacity.
CONCLUSIONS: Overall, the study demonstrated an innovative approach for the development of extruded granules by processing water-insoluble APIs with non-volatile solvents for enhanced dissolution rates at high drug loadings.

Continuous manufacturing has the potential to offer several benefits for the production of oral solid dosage forms, including reduced costs, low-scale equipment, and the application of process analytical technology (PAT) for real-time process control. This study focuses on the implementation of a stream sampler to develop a near infrared (NIR) calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5-7.5 % w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler for flowing powders. A continuous mixer shaft speed of 250 RPM was selected to operate the mixing process based on a variability analysis developed with in-line spectral data acquired using the stream sampler at 6 RPM. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39 % w/w and a bias of 0.05 % w/w. An independent experimental run conducted two days later revealed that the continuous mixing process and the NIR calibration model presented low day-to-day variation. The minimum practical error (MPE) and sill values through variographic analysis showed low variance associated with the sampling process using the stream sampler. Results demonstrated the promising capacity of the stream sampler coupled to an NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration.

The aim of this study was to rapidly develop a sufficiently robust andrographolide nanosuspension (AG-NS) system using hummer acoustic resonance (HAR) technology. The system can effectively improve the dissolution properties of AG, while having high stability and scale-up adaptability. The formulation of AG-NS was optimized in a high-throughput manner using HAR technology and the preparation process was optimized stepwise. Optimal AG-NS with Z-Ave = 223.99 ± 3.16 nm, PDI=0.095 ± 0.007 and zeta potential = -33.20 ± 0.58 mV was successfully prepared with Polyvinylpyrrolidone K30 and Sodium dodecyl sulfate. The optimal prescription was successfully scaled up 100 and 150 times using HAR technology, which was the initial exploration of its commercial scale production. AG-NS was solidified using freeze drying and fluid bed technology, respectively. The optimal AG-NS and its solidified products were exhaustively characterized using various analytical techniques. The high energy input of HAR technology and drying process converted part of the drug into the amorphous state. The in-vitro drug dissolution studies demonstrated relatively higher drug dissolution for AG-NS and its solidified products compared to controls at both the dissolution media (pH 1.2 buffer and pH 6.8 buffer). AG-NS and its solidified products successfully maintained their physical stability in short-term stability and accelerated stability experiments, respectively.

Three-dimensional printing (3D printing) or \"additive manufacturing\" first came to prominence in the field of engineering, in particular in the transport sector where the value of its fast and accurate prototyping and manufacture of spare parts was quickly recognised. However, over the last ten years, this revolutionary technology has disrupted established manufacture in an increasingly diverse range of technical areas. Perhaps the most unexpected of these is pharmaceuticals - not merely the manufacture of products such as surgically inserted implants, but also of dosage formulations themselves - now available in all manner of printed delivery forms and vehicles and showing promising control of release properties though 3D printing process choices. This review will provide an overview of how 3D printing technology has developed and expanded across technological boundaries during the past decade, with a closer look at the current opportunities and barriers to its widespread adoption, particularly in the medical and pharmaceutical sectors. Special attention has been paid to patents as a boost and barrier to the expansion of 3D printing in the medical and pharmaceutical sector, with a focus on the patent literature.

Powder flow is one of the crucial factors affecting several pharmaceutical manufacturing processes. Problems due to insufficient powder flow reduce production process efficiency and cause suboptimum product quality. The U.S. Pharmacopoeia has specified four methods to evaluate the flowability of pharmaceutical powders, including angle of repose (AoR), compressibility index (CI) and Hausner ratio (HR), Flow through an orifice, and shear cell. Comparison within and between those methods with 21 powders (covering a wide range of flowability) was performed in this study. Strong correlation was observed between fixed base cone AoR, and fixed height cone AoR (R2 = 0.939). CI and HR values calculated from a tapped density tester (meeting USP standards), manual tapping, and Geopyc® correlated strongly (R2 > 0.9). AoR, CI/HR, minimum diameter for flowing through an orifice (dmin), and shear cell results generally correlate strongly for materials with flowability worse than Avicel® PH102. Both shear cell and CI/HR methods can reliably distinguish powders exhibiting poor flow. For materials with good flow, the ability to distinguish powders follows the order of AoR ≈ CI/HR > shear cell > dmin. The systematic comparison of the four common methods provides useful information to guide the selection of methods for future powder flow characterization. Given the limitations observed in all four methods, we recommend that multiple techniques should be used, when possible, to more holistically characterize the flowability of a wide range of powders.

Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, allowing the manufacturing of small, personalized batches of medication. Despite its simplicity and suitability for upscaling to large-scale production, SLS was not designed for pharmaceutical manufacturing and necessitates a time-consuming, trial-and-error adaptation process. In response, this study introduces a deep learning model trained on a variety of features to identify the best feature set to represent drugs and polymeric materials for the prediction of the printability of drug-loaded formulations using SLS. The proposed model demonstrates success by achieving 90% accuracy in predicting printability. Furthermore, explainability analysis unveils materials that facilitate SLS printability, offering invaluable insights for scientists to optimize SLS formulations, which can be expanded to other disciplines. This represents the first study in the field to develop an interpretable, uncertainty-optimized deep learning model for predicting the printability of drug-loaded formulations. This paves the way for accelerating formulation development, propelling us into a future of personalized medicine with unprecedented manufacturing precision.

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

UNASSIGNED: Most therapeutics delivered using short-acting formulations need repeated administration, which can harm patient compliance and raise failure risks related to inconsistent treatment. Injectable long-acting formulations (ILAFs) are controlled/sustained-release formulations fabricated to deliver active pharmaceutical ingredients (APIs) and extend their half-life over days to months. Longer half-lives of ILAFs minimize the necessity for frequent doses, increase patient compliance, and reduce the risk of side effects from intravenous (IV) infusions. Using ILAF technologies, the immediate drug release can also be controlled, thereby minimizing potential adverse effects due to high initial drug blood concentrations.
UNASSIGNED: In this review, we have discussed various ILAFs, their physiochemical properties, fabrication technologies, advantages, and practical issues, as well as address some major challenges in their application. Especially, the approved ILAFs are highlighted.
UNASSIGNED: ILAFs are sustained-release formulations with extended activity, which can improve patient compliance. ILAFs are designed to deliver APIs like proteins and peptides and extend their half-life over days to months. The specific properties of each ILAF preparation, such as extended-release and improved drug targeting capabilities, make them an effective approach for precise and focused therapy. Furthermore, this is especially helpful for biopharmaceuticals with short biological half-lives and low stability since most environmental conditions can protect them from sustained-release delivery methods.