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Machine Learning
Application of unsupervised and supervised learning to a material attribute database of tablets…
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of…
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Predicting pharmaceutical inkjet printing outcomes using machine learning
Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process…
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Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials…
Understanding powder flow in the pharmaceutical industry facilitates the development of robust production routes and effective manufacturing processes. In pharmaceutical manufacturing, machine learning (ML) models have the potential to enable rapid decision-making and minimise the time and material…
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Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery…
A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been…
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Formulation and Characterization of Buccal Films Containing Valsartan with Additional Support from…
The present study was aimed to the development and characterization of valsartan-containing buccal films with an introduction to a novel technique of image analysis. Visual inspection of the film provided a wealth of information that was difficult to quantify objectively. The obtained images of the…
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Technological advances and challenges for exploring attribute transmission in tablet development by…
High shear wet granulation and tableting (HSWG-T) are complex pharmaceutical processes, which makes tablet quality be influenced by many variables including raw material attributes, process variables, and intermediate granule properties. To achieve the desired final quality of tablets and improve…
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Predicting pharmaceutical powder flow from microscopy images using deep learning
The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show…
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Machine learning using multi-modal data predicts the production of selective laser sintered 3D…
Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide…
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Advancing oral delivery of biologics: Machine learning predicts peptide stability in the…
The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide…
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The applications of machine learning to predict the forming of chemically stable amorphous solid…
Amorphous solid dispersion (ASD) is one of the most important strategies to improve the solubility and dissolution rate of poorly water-soluble drugs. As a widely used technique to prepare ASDs, hot-melt extrusion (HME) provides various benefits, including a solvent-free process, continuous…
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