AIMC Topic: Tablets

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Mechanistic, data-driven, and hybrid models: A critical comparison in surrogate drug dissolution modeling.

International journal of pharmaceutics
Mathematical modeling is becoming increasingly important in the pharmaceutical industry. It supports the Quality by Design framework by aiding process understanding and examining the impact of critical material and process parameters on the critical ...

A multitask modelling framework for tablet manufacturability and quality attributes in direct compression using knowledge-guided neural networks.

International journal of pharmaceutics
Assessing the feasibility of a manufacturing route for a given formulation and process is a key initial step in drug product development. Additionally, the final product must meet a series of critical quality attributes to be considered suitable to m...

A mechanistic framework for predicting tablet disintegration: Integrating the Representative Capillary Evolution Model (RCEM) and the Dynamic Void Fraction Evolution Model (DVFEM).

International journal of pharmaceutics
The disintegration behaviour of pharmaceutical tablets is a critical quality attribute influencing drug release, yet predicting it from formulation and processing parameters remains challenging due to complex underlying mechanisms. This work presents...

Enhanced ribbon quality in roller compaction process by mitigating splitting through a machine-learning framework.

International journal of pharmaceutics
Ribbon splitting, a phenomenon that can occur during the roller compaction operation used in dry granulation processes, can lead to compromised granule uniformity, poor tabletability, and ultimately, off-specification tablet production. Despite its i...

Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.

The AAPS journal
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...

Advancing Direct Tablet Compression with AI: A multi-task framework for quality control, batch acceptance, and causal analysis.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Pharmaceutical manufacturing has surged in drug development with the rise of Pharma 4.0, leveraging artificial intelligence (AI) to improve efficiency, optimize resource use, and reduce production times. Direct Tablet Compression (DTC), a key manufac...

Rapid detection and quantification of falsified Viagra using cloud-based portable NIR technology and machine learning.

Journal of pharmaceutical and biomedical analysis
The prevalence of falsified medications remains a global health challenge, intensified by globalization, internet accessibility, and the high profitability associated with low risks for this type of trafficking. This study demonstrates the innovative...

Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation.

Scientific reports
This research assesses multiple predictive models aimed at estimating disintegration time for pharmaceutical oral formulations, based on a dataset comprising nearly 2,000 data points that include molecular, physical, compositional, and formulation at...

An oral robotic pill reliably and safely delivers teriparatide with high bioavailability in healthy volunteers: A phase 1 study.

British journal of clinical pharmacology
AIMS: The incidence of osteoporosis is projected to exceed 70 million people over the age of 65 years by 2030. Osteoanabolic agents, such as teriparatide and abaloparatide, are not only effective in reducing fracture incidence but also improve skelet...

RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.

Scientific reports
The study is based on applying Artificial Neural Network (ANN) based machine learning and Response Surface Methodology (RSM) as simultaneous bivariate approaches in developing controlled-release rivaroxaban (RVX) osmotic tablets. The influence of dif...