Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations: A pilot study
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Date
2021-09-05
Authors
Bennett-Lenane, Harriet
O'Shea, Joseph P.
Murray, Jack D.
Ilie, Alexandra-Roxana
Holm, René
Kuentz, Martin
Griffin, Brendan T.
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Published Version
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.
Description
Keywords
Lipid-based drug delivery , Computational pharmaceutics , Machine learning , Supersaturated lipid-based formulations
Citation
Bennett-Lenane, H., O'Shea, J. P., Murray, J. D., Ilie, A.-R., Holm, R., Kuentz, M. and Griffin, B. T. (2021) 'Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations: A pilot study', Pharmaceutics, 13(9), 1398 (14pp). doi: 10.3390/pharmaceutics13091398