Literature DB >> 18563797

Process modeling in the pharmaceutical industry using the discrete element method.

William R Ketterhagen1, Mary T am Ende, Bruno C Hancock.   

Abstract

The discrete element method (DEM) is widely used to model a range of processes across many industries. This paper reviews current DEM models for several common pharmaceutical processes including material transport and storage, blending, granulation, milling, compression, and film coating. The studies described in this review yielded interesting results that provided insight into the effects of various material properties and operating conditions on pharmaceutical processes. Additionally, some basic elements common to most DEM models are overviewed. A discussion of some common model extensions such as nonspherical particle shapes, noncontact forces, and interstitial fluids is also presented. While these more complex systems have been the focus of many recent studies, considerable work must still be completed to gain a better understanding of how they can affect the processing behavior of bulk solids.

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Year:  2009        PMID: 18563797     DOI: 10.1002/jps.21466

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  11 in total

1.  3D simulation of internal tablet strength during tableting.

Authors:  Simo Matti Siiriä; Osmo Antikainen; Jyrki Heinämäki; Jouko Yliruusi
Journal:  AAPS PharmSciTech       Date:  2011-05-04       Impact factor: 3.246

Review 2.  Process optimization and particle engineering of micronized drug powders via milling.

Authors:  A Brunaugh; H D C Smyth
Journal:  Drug Deliv Transl Res       Date:  2018-12       Impact factor: 4.617

3.  Efficient Voronoi volume estimation for DEM simulations of granular materials under confined conditions.

Authors:  Göran Frenning
Journal:  MethodsX       Date:  2015-02-16

4.  Modeling of Disintegration and Dissolution Behavior of Mefenamic Acid Formulation Using Numeric Solution of Noyes-Whitney Equation with Cellular Automata on Microtomographic and Algorithmically Generated Surfaces.

Authors:  Reiji Yokoyama; Go Kimura; Christian M Schlepütz; Jörg Huwyler; Maxim Puchkov
Journal:  Pharmaceutics       Date:  2018-12-03       Impact factor: 6.321

5.  Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation.

Authors:  Su Bin Yeom; Du Hyung Choi
Journal:  Pharmaceutics       Date:  2019-06-06       Impact factor: 6.321

Review 6.  Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry.

Authors:  Su Bin Yeom; Eun-Sol Ha; Min-Soo Kim; Seong Hoon Jeong; Sung-Joo Hwang; Du Hyung Choi
Journal:  Pharmaceutics       Date:  2019-08-15       Impact factor: 6.321

Review 7.  The Future of Pharmaceutical Manufacturing Sciences.

Authors:  Jukka Rantanen; Johannes Khinast
Journal:  J Pharm Sci       Date:  2015-08-17       Impact factor: 3.534

8.  Main factor causing "faster-is-slower" phenomenon during evacuation: rodent experiment and simulation.

Authors:  Hyejin Oh; Junyoung Park
Journal:  Sci Rep       Date:  2017-10-20       Impact factor: 4.379

9.  Process Modeling and Simulation of Tableting-An Agent-Based Simulation Methodology for Direct Compression.

Authors:  Niels Lasse Martin; Ann Kathrin Schomberg; Jan Henrik Finke; Tim Gyung-Min Abraham; Arno Kwade; Christoph Herrmann
Journal:  Pharmaceutics       Date:  2021-06-30       Impact factor: 6.321

Review 10.  Direct Compaction Drug Product Process Modeling.

Authors:  Alexander Russell; John Strong; Sean Garner; William Ketterhagen; Michelle Long; Maxx Capece
Journal:  AAPS PharmSciTech       Date:  2022-01-31       Impact factor: 3.246

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