Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute–Lipid Interactions-Reference-Cited by-同舟云学术

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Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute–Lipid Interactions

Published:2024-05-23 Issue:7 Volume:21 Page:3343-3355
ISSN:1543-8384
Container-title:Molecular Pharmaceutics
language:en
Short-container-title:Mol. Pharmaceutics

Author:

Lange Justus Johann¹^ORCID,Anelli Andrea²,Alsenz Jochem²,Kuentz Martin³^ORCID,O’Dwyer Patrick J.¹^ORCID,Saal Wiebke²,Wyttenbach Nicole²,Griffin Brendan T.¹^ORCID

Affiliation:

1. School of Pharmacy, University College Cork, College Road, Cork T12 R229, Cork County, Ireland

2. Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Limited, Grenzacherstrasse 124, Basel 4070, Switzerland

3. Insitute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz CH-4231, Basel City, Switzerland

Funder

H2020 Marie Sklodowska-Curie Actions

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.molpharmaceut.4c00080

Reference70 articles.

1. Lipophilicity and hydrophobicity considerations in bio-enabling oral formulations approaches – a PEARRL review

2. Computational prediction of formulation strategies for beyond-rule-of-5 compounds

3. Biopharmaceutical challenges associated with drugs with low aqueous solubility—The potential impact of lipid-based formulations

4. High throughput solubility measurement in drug discovery and development

5. Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary

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