AIMC Topic: Drug Compounding
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Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 16, 2024
The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control,...
Particle formation in response to different protein formulations and containers: Insights from machine learning analysis of particle images.
Journal of pharmaceutical sciences
Oct 9, 2024
Subvisible particle count is a biotherapeutics stability indicator widely used by pharmaceutical industries. A variety of stresses that biotherapeutics are exposed to during development can impact particle morphology. By classifying particle morpholo...
Data-driven insights into the properties of liquisolid systems based on machine learning algorithms.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 6, 2024
Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have fo...
Review of machine learning for lipid nanoparticle formulation and process development.
Journal of pharmaceutical sciences
Sep 27, 2024
Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes ...
Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Sep 19, 2024
The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are t...
Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking.
International journal of pharmaceutics
Sep 16, 2024
The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compacti...
The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?
Advanced healthcare materials
Aug 18, 2024
Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack ...
Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0.
International journal of pharmaceutics
Jul 22, 2024
Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this wo...
Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products.
International journal of pharmaceutics
Jul 14, 2024
T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations...
Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine.
International journal of pharmaceutics
Jul 6, 2024
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, a...
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