AIMC Topic: Pharmaceutical Preparations

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A review of the state-of-the-art: progress in ultrasonic and acoustic techniques for quality assessment in the development and manufacturing of oral solid dosage forms - Part I: theoretical foundations and principles.

International journal of pharmaceutics
Over the past two decades, a diverse array of ultrasonic and acoustic elastic-wave techniques has been developed to non-destructively assess macro- and micro-scale properties of compressed Oral Solid Dosage (OSD) forms. These methods are increasingly...

A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.

The AAPS journal
Understanding a drug's plasma half-life is essential in guiding dosage regimens and optimizing therapeutic outcomes, particularly in the early stages of drug development. By using published pharmacokinetic data from Food Animal Residue Avoidance Data...

Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties.

Scientific reports
Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determina...

AGDNGDA: Unraveling Drug-Associated Genes with Adaptive Graph Diffusion Networks.

Journal of chemical information and modeling
Understanding the intricate relationships between genes and drugs is crucial for advancing drug discovery. However, biological experiments aimed at identifying gene-drug associations are typically time-consuming and inefficient, leading to significan...

Machine learning analysis and simulation of pharmaceutical drying process based on prediction of concentration distribution and mass transfer.

Scientific reports
Modeling and analysis of the lyophilization process for low-temperature drying of pharmaceutical compounds was evaluated via a hybrid model that combines mass transfer and machine learning. We investigated the predictive accuracy of three machine lea...

From Liquid SNEDDS to Solid SNEDDS: A Comprehensive Review of Their Development and Pharmaceutical Applications.

The AAPS journal
The liquid and solid formulations of self-nano-emulsifying drug delivery systems (SNEDDS) have garnered significant attention in the pharmaceutical field for their ability to enhance the solubility and absorption of hydrophobic drugs. While both liqu...

Toxigraphnet: a graph neural network framework for precise toxicity prediction of drug molecules.

Journal of computer-aided molecular design
Accurate prediction of a drug molecule's toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often r...

Predicting the Ionization Behavior of Drugs in Tissue in MALDI and MALDI-2 Mass Spectrometry Imaging Using Machine Learning.

Analytical chemistry
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and its most common application, MALD-MS imaging (MSI), are widely used techniques in the analysis of intact biomolecules. In the context of pharmaceutical research, MALDI-MSI i...

Molecular enrichment integrated with soft voting for efficient SERS detection of multiple drugs.

Analytica chimica acta
Surface-enhanced Raman spectroscopy (SERS) is regarded as a powerful tool for rapid drug identification in the liquid phase. However, the implementation of SERS for drug detection in the liquid phase remains constrained by two critical limitations: i...

Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.

Molecular pharmaceutics
In drug discovery, metabolite identification data are used to identify metabolic soft spots in research molecules to facilitate reduced metabolism in subsequently designed compounds. In addition, knowledge about exact metabolite structures enables th...