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Machine Learning
Additive Manufacturing in Pharmaceuticals – Part 1
Additive Manufacturing in Pharmaceuticals
See the new book, edited by Dr. Subham Banerjee, Ph.D., Associate Professor in the Department of Pharmaceutics, National Institute of Pharmaceutical Education & Research (NIPER), Guwahati, Assam, India.
Description: This book presents the different…
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Optimising excipient properties to prevent aggregation in biopharmaceutical formulations
Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability, but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship…
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In-line particle size measurement during granule fluidization using convolutional neural…
This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the…
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Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their…
This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18–40) can serve as suitable substitutes due to the similarity…
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Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery,…
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A Machine Learning Approach to Qualitatively Evaluate Different Granulation Phases by Acoustic…
Wet granulation is a frequent process in the pharmaceutical industry. As a starting point for numerous dosage forms, the quality of the granulation not only affects subsequent production steps but also impacts the quality of the final product. It is thus crucial and economical to monitor this…
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Deep Learning-Based Machine Vision System for Use in High-Shear Granulation
Aim
Develop a prototype machine vision system and evaluate feasibility of a deep learning-based image analysis approach for estimation of the state of granulation on the acquired granule bulk surface images.
Introduction
Despite many decades of research in granulation technology, the…
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Deep Learning-Based Machine Vision System for In-Line Monitoring of High-Shear Wet Granulation…
Aim
Develop a machine vision system for in-line monitoring of high-shear granulation processes to provide a more reliable and continuous solution for assessing the state of granulation.
Introduction
Granulation is critical for pharmaceutical production, but inherently complex and poorly…
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A multi-step machine learning approach for accelerating QbD-based process development of protein…
This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving…
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Application of unsupervised and supervised learning to a material attribute database of tablets…
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of…
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