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 › Research › peer-review

Standard

Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors. / Wanat, K.; Khakimov, B.; Brzezinska, E.

In: SAR and QSAR in Environmental Research, Vol. 31, No. 6, 2020, p. 457-475.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Wanat, K, Khakimov, B & Brzezinska, E 2020, 'Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors', SAR and QSAR in Environmental Research, vol. 31, no. 6, pp. 457-475. https://doi.org/10.1080/1062936X.2020.1772365

APA

Wanat, K., Khakimov, B., & Brzezinska, E. (2020). Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors. SAR and QSAR in Environmental Research, 31(6), 457-475. https://doi.org/10.1080/1062936X.2020.1772365

Vancouver

Wanat K, Khakimov B, Brzezinska E. Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors. SAR and QSAR in Environmental Research. 2020;31(6):457-475. https://doi.org/10.1080/1062936X.2020.1772365

Author

Wanat, K. ; Khakimov, B. ; Brzezinska, E. / Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors. In: SAR and QSAR in Environmental Research. 2020 ; Vol. 31, No. 6. pp. 457-475.

Bibtex

@article{997a8cbb47374c7ca413730b714de42a,
title = "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 (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.",
keywords = "Human breast milk, milk-to-plasma ratio, chromatographic data, QSPR, statistical modelling, molecular descriptors, PROTEIN BINDING, SERUM-ALBUMIN, QSAR, VALIDATION, MODELS, REGRESSION, EXCRETION",
author = "K. Wanat and B. Khakimov and E. Brzezinska",
year = "2020",
doi = "10.1080/1062936X.2020.1772365",
language = "English",
volume = "31",
pages = "457--475",
journal = "SAR and QSAR in Environmental Research",
issn = "1062-936X",
publisher = "Taylor & Francis",
number = "6",

}

RIS

TY - JOUR

T1 - Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors

AU - Wanat, K.

AU - Khakimov, B.

AU - Brzezinska, E.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Human breast milk

KW - milk-to-plasma ratio

KW - chromatographic data

KW - QSPR

KW - statistical modelling

KW - molecular descriptors

KW - PROTEIN BINDING

KW - SERUM-ALBUMIN

KW - QSAR

KW - VALIDATION

KW - MODELS

KW - REGRESSION

KW - EXCRETION

U2 - 10.1080/1062936X.2020.1772365

DO - 10.1080/1062936X.2020.1772365

M3 - Journal article

C2 - 32627677

VL - 31

SP - 457

EP - 475

JO - SAR and QSAR in Environmental Research

JF - SAR and QSAR in Environmental Research

SN - 1062-936X

IS - 6

ER -

ID: 246830236