AIMC Topic: Chemistry, Pharmaceutical

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Development of machine learning models for estimation of disintegration time on fast-disintegrating tablets.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The disintegration time for solid dosage oral formulations is directly influenced by diverse factors such as molecular properties, physical characteristics, excipient compositions, and formulation-specific attributes. This research addresses the chal...

Application of rheology to hot melt extrusion: Theory and practice.

International journal of pharmaceutics
Hot melt extrusion (HME) has become a key manufacturing method in the pharmaceutical industry for developing novel drug delivery systems, due to its solvent-free nature, ease of operation, and ability to achieve one-step molding and continuous proces...

Advancing Amorphous Solid Dispersions Design: Insights into Dissolution Kinetics via Thermodynamic Descriptor and Machine Learning.

Molecular pharmaceutics
Amorphous solid dispersions (ASD) are an effective strategy for enhancing the solubility and bioavailability of poorly soluble drugs. However, designing and optimizing ASD formulations often rely on extensive dissolution experiments without sufficie...

Recent advancements of Raman spectroscopy application in topical products.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Topical products have gained popularity in the recent years. Diverse formulation types, complex composition, and thermodynamically instable nature present great challenges in the formulation development of topical products. The analytical methods ava...

Harnessing Medicinal Chemical Intuition from Collective Intelligence.

Journal of medicinal chemistry
Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition....

Enhancing YOLOv8n with Mamba-like linear attention for defect detection and coating thickness analysis of irregular film tablet.

International journal of pharmaceutics
This study presents a real-time system that integrates deep learning and machine vision for defect detection and coating thickness measurement of irregularly shaped film-coated tablets. To overcome the accuracy and speed limitations of the traditiona...

Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
The pharmaceutical sector is aware of supercritical CO (SC-CO) as a possible replacement for problematic organic solvents. Using a novel artificial intelligence (AI) strategy to predict drug solubility using the SC-CO system mathematically has been d...

Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation.

Current pharmaceutical design
Drug Delivery Systems (DDS) have been developed to address the challenges associated with traditional drug delivery methods. These DDS aim to improve drug administration, enhance patient compliance, reduce side effects, and optimize target therapy. T...

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network.

The Journal of pharmacy and pharmacology
OBJECTIVES: As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was u...