Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors
Research output: Contribution to journal › Journal article › peer-review
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 (r(2) = 0.83 andr(2) = 0.86,n= 28) and RF (r(2) = 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.
Original language | English |
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Journal | SAR and QSAR in Environmental Research |
Volume | 31 |
Issue number | 6 |
Pages (from-to) | 457-475 |
Number of pages | 19 |
ISSN | 1062-936X |
DOIs | |
Publication status | Published - 2020 |
- Human breast milk, milk-to-plasma ratio, chromatographic data, QSPR, statistical modelling, molecular descriptors, PROTEIN BINDING, SERUM-ALBUMIN, QSAR, VALIDATION, MODELS, REGRESSION, EXCRETION
Research areas
ID: 246830236