Dataset Information

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

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.

SUBMITTER: Bennett-Lenane H

PROVIDER: S-EPMC8466847 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Bennett-Lenane Harriet H O'Shea Joseph P JP Murray Jack D JD Ilie Alexandra-Roxana AR Holm René R Kuentz Martin M Griffin Brendan T BT

Pharmaceutics 20210905 9

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 c ...[more]

PMID: 34575483

Similar Datasets

Project description:The concept of using precipitation inhibitors (PIs) to sustain supersaturation is well established for amorphous formulations but less in the case of lipid-based formulations (LBF). This study applied a systematic in silico-in vitro-in vivo approach to assess the merits of incorporating PIs in supersaturated LBFs (sLBF) using the model drug venetoclax. sLBFs containing hydroxypropyl methylcellulose (HPMC), hydroxypropyl methylcellulose acetate succinate (HPMCAS), polyvinylpyrrolidone (PVP), PVP-co-vinyl acetate (PVP/VA), Pluronic F108, and Eudragit EPO were assessed in silico calculating a drug-excipient mixing enthalpy, in vitro using a PI solvent shift test, and finally, bioavailability was assessed in vivo in landrace pigs. The estimation of pure interaction enthalpies of the drug and the excipient was deemed useful in determining the most promising PIs for venetoclax. The sLBF alone (i.e., no PI present) displayed a high initial drug concentration in the aqueous phase during in vitro screening. sLBF with Pluronic F108 displayed the highest venetoclax concentration in the aqueous phase and sLBF with Eudragit EPO the lowest. In vivo, the sLBF alone showed the highest bioavailability of 26.3 ± 14.2%. Interestingly, a trend toward a decreasing bioavailability was observed for sLBF containing PIs, with PVP/VA being significantly lower compared to sLBF alone. In conclusion, the ability of a sLBF to generate supersaturated concentrations of venetoclax in vitro was translated into increased absorption in vivo. While in silico and in vitro PI screening suggested benefits in terms of prolonged supersaturation, the addition of a PI did not increase in vivo bioavailability. The findings of this study are of particular relevance to pre-clinical drug development, where the high in vivo exposure of venetoclax was achieved using a sLBF approach, and despite the perceived risk of drug precipitation from a sLBF, including a PI may not be merited in all cases.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets