Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations. A pilot study
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Authors
Bennett-Lenane, Harriett
O'Shea, Joseph
Murray, Jack
Ilie, Alexandra Roxana
Holm, Rene
Griffin, Brendan
Author (Corporation)
Publication date
05.09.2021
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Collections
Type
01A - Journal article
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Editor (Corporation)
Supervisor
Parent work
Pharmaceutics
Special issue
DOI of the original publication
Link
Series
Series number
Volume
13
Issue / Number
9
Pages / Duration
Patent number
Publisher / Publishing institution
MDPI
Place of publication / Event location
Basel
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Abstract
In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.
Keywords
Lipid-based drug delivery, Computational pharmaceutics, Machine learning, Supersaturated lipid-based formulations
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ISBN
ISSN
1999-4923
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
Bennett-Lenane, H., O’Shea, J., Murray, J., Ilie, A. R., Holm, R., Kuentz, M., & Griffin, B. (2021). Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations. A pilot study. Pharmaceutics, 13(9). https://doi.org/10.3390/pharmaceutics13091398