Over the past decades, several in vitro methods have been tested for their ability to predict either human intestinal absorption (HIA) or penetration across the blood-brain barrier (BBB) of drugs. Micellar liquid chromatography (MLC) has been a successful approach for retention time measurements of drugs to establish models together with other molecular descriptors. Thus far, MLC approaches have only made use of commercial surfactants such as sodium dodecyl sulfate (SDS) and polyoxyethylene (23) lauryl ether (Brij35), which are not representative for the phospholipids present in human membranes. Miltefosine, a phosphocholine-based lipid, is presented here as an alternative surfactant for MLC measurements. By using the obtained retention factors and several computed descriptors for a set of 48 compounds, two models were constructed: one for the prediction of HIA and another for the prediction of penetration across the BBB expressed as log BB. All data were correlated to experimental HIA and log BB values, and the performance of the models was evaluated. Log BB prediction performed better than HIA prediction, although HIA prediction was also improved a lot (from 0.5530 to 0.7175) compared to in silico predicted HIA values.

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Machine-Learning (ML) methods, such as Artificial Neural Networks (ANN) bring the data-driven design of chemical reactions within reach. Simultaneously with the verification of the absence of any bias in the...

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