Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors

ABSTRACT

In silico methods are often used for predicting pharmacokinetic properties of drugs due to their simplicity and cost-effectiveness. This study evaluates the penetration of 83 active pharmaceutical ingredients into human breast milk with an experimental milk-to-plasma ratio (M/P) obtained from the literature. Multiple linear regression (MLR), partial least squares (PLS) and random forest (RF) regression methods were compared to uncover the relationship between physicochemical, pharmacokinetic and membrane crossing properties of active pharmaceutical ingredients (APIs) using their rapid reference measurement value (R_f values), thin-layer chromatography (TLC) data from albumin-impregnated plates. Molecular descriptors of APIs proven to be important for their crossing into breast milk, including protein binding, ionisation state and lipophilicity and TLC data, have been included in the development of the prediction models. The best regression results were achieved by MLR (r² = 0.83 and r² = 0.86, n = 28) and RF (r² = 0.85, n = 58). In addition, the discriminant function analysis (DFA) was performed on acidic, basic and neutral drugs separately and showed a prediction accuracy of 93%, with M/P included as the discriminating variable.

KEYWORDS:

• Human breast milk
• milk-to-plasma ratio
• chromatographic data
• QSPR
• statistical modelling
• molecular descriptors

Disclosure statement

The author declares no conflict of interest, financial or otherwise.

Data statement

The authors confirm that the data supporting the findings of this study are available within the supplementary materials.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2020.1772365.

Additional information

Funding

This work was supported by the Medical University of Lodz under internal grant no. 502-34-106.