AIMC Topic: Technology, Pharmaceutical
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Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.
AAPS PharmSciTech
Mar 31, 2020
Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from sol...
Flow Chemistry in Contemporary Chemical Sciences: A Real Variety of Its Applications.
Molecules (Basel, Switzerland)
Mar 21, 2020
Flow chemistry is an area of contemporary chemistry exploiting the hydrodynamic conditions of flowing liquids to provide particular environments for chemical reactions. These particular conditions of enhanced and strictly regulated transport of reage...
Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study.
International journal of pharmaceutics
Jun 26, 2019
The aim of this work was to develop a PAT platform consisting of four complementary instruments for the characterization of electrospun amorphous solid dispersions with meloxicam. The investigated methods, namely NIR spectroscopy, Raman spectroscopy,...
Application of artificial neural networks for Process Analytical Technology-based dissolution testing.
International journal of pharmaceutics
Jun 25, 2019
This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, ...
Exploiting machine learning for end-to-end drug discovery and development.
Nature materials
Apr 18, 2019
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from...
Organic synthesis in a modular robotic system driven by a chemical programming language.
Science (New York, N.Y.)
Nov 29, 2018
The synthesis of complex organic compounds is largely a manual process that is often incompletely documented. To address these shortcomings, we developed an abstraction that maps commonly reported methodological instructions into discrete steps amena...
An application of deep learning to detect process upset during pharmaceutical manufacturing using passive acoustic emissions.
International journal of pharmaceutics
Sep 22, 2018
The multivariate nature of a fluidized bed system creates process complexity that increases the risk of production upset. This research explores the use of passive acoustic emissions monitoring paired with an artificial neural network to detect fluid...
A meta-learning framework using representation learning to predict drug-drug interaction.
Journal of biomedical informatics
Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.
Biotechnology and bioengineering
Apr 23, 2018
Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monit...
Statistical moments in modelling of swelling, erosion and drug release of hydrophilic matrix-tablets.
International journal of pharmaceutics
Feb 1, 2018
Statistical moments were evaluated as suitable parameters for describing swelling and erosion processes (along with drug release) in hydrophilic controlled release matrix tablets. The effect of four independent formulation variables, corresponding to...
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