Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties-Reference-Cited by-同舟云学术

Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

Published:2023 Issue:3 Volume:2 Page:692-701
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Pereira Diaz Laura¹²^ORCID,Brown Cameron J.¹²^ORCID,Ojo Ebenezer¹,Mustoe Chantal¹²^ORCID,Florence Alastair J.¹²^ORCID

Affiliation:

1. EPSRC CMAC Future Manufacturing Research Hub, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK

2. Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK

Abstract

A Machine Learning (ML) approach was proposed to optimize the manufacturing-route selection from the physical particle properties of a pharmaceutical material.

Funder

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00106C

Reference40 articles.

1. J.Maier , UK Industrial Digitalisation Review , 2017

2. Improving Powder Flow Properties of a Direct Compression Formulation Using a Two-Step Glidant Mixing Process

3. Systematic development of a high dosage formulation to enable direct compression of a poorly flowing API: A case study

4. Engineered particles demonstrate improved flow properties at elevated drug loadings for direct compression manufacturing

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