Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

• Main Authors: Harriet Bennett-Lenane, Joseph P. O’Shea, Jack D. Murray, Alexandra-Roxana Ilie, René Holm, Martin Kuentz, Brendan T. Griffin
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Pharmaceutics
• Subjects: lipid-based drug delivery; computational pharmaceutics; machine learning; supersaturated lipid-based formulations
• Online Access: https://www.mdpi.com/1999-4923/13/9/1398

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 sLBF_Capmul^MC (<i>r</i>² 0.90 vs. 0.56) and sLBF_Maisine^LC (<i>r</i>² 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.