AIMC Topic: Chemistry, Pharmaceutical
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In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method.
Nature communications
Nov 7, 2024
Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which ...
Optimising the production of PLGA nanoparticles by combining design of experiment and machine learning.
International journal of pharmaceutics
Nov 2, 2024
Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanopreci...
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...
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...
Evaluation of floatability characteristics of gastroretentive tablets using VIS imaging with artificial neural networks.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Sep 11, 2024
Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier system into the stomach over an extended period. Among gastroretentive dosage forms, floating tablet...
A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches.
International journal of pharmaceutics
Aug 11, 2024
The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO (scCO)-based techniques. Measuring the solubility of drugs in scCO at varying conditions is a crucial parameter ...
Optimization and evaluation of modified release solid dosage forms using artificial neural network.
Scientific reports
Jul 16, 2024
This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapin...
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...
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