AIMC Topic: Solubility
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Enhancing Predictions of Drug Solubility Through Multidimensional Structural Characterization Exploitation.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Solubility is not only a significant physical property of molecules but also a vital factor in small-molecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resou...
Exploring a universal model for predicting blueberry soluble solids content based on hyperspectral imaging and transfer learning to address spatial heterogeneity challenge.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Feb 18, 2025
Accurate assessment of soluble solid content (SSC) in blueberries is crucial for quality evaluation. However, in real production lines, blueberries are usually in random placement and the biological heterogeneity of blueberry parts can lead to spectr...
Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO.
Scientific reports
Feb 12, 2025
Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and ...
Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.
Scientific reports
Feb 8, 2025
In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the eff...
Determination of 5-fluorouracil anticancer drug solubility in supercritical COusing semi-empirical and machine learning models.
Scientific reports
Feb 7, 2025
In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO. This important rol...
Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.
Scientific reports
Feb 4, 2025
This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component an...
Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.
AAPS PharmSciTech
Feb 3, 2025
Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs remains challenging. This study introduces Sol_ME, a machine learning-based model designed to predict ...
Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.
Scientific reports
Jan 17, 2025
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded...
Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach.
Journal of controlled release : official journal of the Controlled Release Society
Jan 13, 2025
In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrog...
Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.
Journal of microencapsulation
Jan 5, 2025
AIMS: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN) for minimal mean diameter and maximal entrapment efficiency (EE), enhancing solubility, bioavailability, and the ability to cross the blood-brain bar...
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