QSAR treatment of drugs transfer into human breast milk

Katritzky, Alan R.; Dobchev, Dimitar A.; Hür, Evrim; Fara, Dan C.; Karelson, Mati

doi:10.1016/j.bmc.2004.12.015

Cited by 41 publications

(47 citation statements)

References 78 publications

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“…Several physicochemical and biological properties such as aqueous solubilities, vapor pressures, water-air partition coefficients, 15,16 partitioning behavior of organic solutes in aqueous biphasic systems, 17 blood-air and tissue-air partition coefficients of organic solutes, 18,19 partition of drugs between human milk and plasma, 20 and blood-to-brain distribution coefficients of drugs 21 were investigated by our group.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

et al. 2008

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Interactions of solutes with diverse ionic liquid solvents have been investigated by quantitative structureproperty relationship (QSPR) methodology. Ostwald solubility coefficients and partition coefficients of organic solutes in eight different ionic liquids are correlated by molecular descriptors calculated solely from their structures. Two-to four-parameter best multilinear regression models were obtained with coefficients of determination ranging from 0.913 to 0.992. Additionally, several models were obtained with the same descriptors for all eight ionic liquids (ILs). Charge-related type descriptors contributed significantly to most of the models. The QSPR models were validated using the leave-one-out cross validation method.

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Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

et al. 2008

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“…Agatonovic-Kustrin et al [18,19] developed a genetic neural network model using a set of 60 drug compounds and examined a larger set of 123 drugs and applied an artificial neural network to predict their degree of transfer into breast milk. Katritzky et al [7] investigated the prediction of M/P ratios for a set of 115 drugs using multiple linear regression. Zhao et al [20] used a support vector machine, SVM, method to analyze M/P ratios for 126 drugs.…”

Section: Ei ð%þ ¼ 100 â ðM=pþ â A=infantdrugclearance ð1þmentioning

confidence: 99%

“…Thus, there is increasing concern regarding the presence of drugs and contaminants in breast milk, leading to potentially harmful effects to the nursing infants. [7,8] Numerous reports have been published on levels of polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDT) in human milk in the USA. [9,10] The occurrence of drugs and environmental pollutants in human milk in recent reviews has worsened the fear in extremities.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Kar

Roy

2013

Molecular Informatics

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A large set of 185 compounds with diverse molecular structures and different mechanisms of therapeutic actions was used to develop and validate statistically significant classification and regression based QSTR models for predicting partitioning of drugs/chemicals into breast milk. Pharmacophore mapping was also carried out which showed four important features required for lower risk of secretion into milk: (i) hydrophobic group (HYD), (ii) ring aromatic group (RA), (iii) negative ionizable (NegIon) and (iv) hydrogen bond donor (HBA). QSTR and pharmacophore models were rigorously validated internally as well as externally to check the possibilities of any chance correlation and judge the predictive potential of the models. Pharmacological distribution diagrams (PDDs) were used for the classification model as a visualizing technique for the identification and selection of chemicals with lower partitioning into milk. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for secretion into milk. The developed models were also implemented to screen milk/plasma partitioning potential for a huge number DrugBank database (http://www.drugbank.ca/) compounds.

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“…Unfortunately, the data come from highly diverse sources 5,7,9,[31][32][33][34][35][36][37][38][39][40][41][42][43][44] and have been determined by very different methods with different errors. So, instead of directly predicting partition coefficients, it may be more consistent in terms of accuracy to classify molecules into two groups namely: partition-like (PL), when the experimental log p value is > system break point (SBP), and partition-unlike compounds (PUL), otherwise for an specific BPS.…”

Section: Data Basementioning

confidence: 99%

“…Their use today range from medicinal chemistry to environmental sciences. 3,4 For instance, breast milk to plasma concentration ratio 5 of a drug is generally used to estimate the infant's exposure to drugs through breast milk. As many women need to take medication while breast feeding, the relevance of this measure is revealed by estimating if a risk to the infant can exceed the benefits of breast feeding.…”

Section: Introductionmentioning

confidence: 99%

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

et al. 2007

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Predicting tissue and environmental distribution of chemicals is of major importance for environmental and life sciences. Most of the molecular descriptors used in computational prediction of chemicals partition behavior consider molecular structure but ignore the nature of the partition system. Consequently, computational models derived up-to-date are restricted to the specific system under study. Here, a free energy-based descriptor (DeltaG(k)) is introduced, which circumvent this problem. Based on DeltaG(k), we developed for the first time a single linear classification model to predict the partition behavior of a broad number of structurally diverse drugs and other chemicals (1300) for 38 different partition systems of biological and environmental significance. The model presented training/predicting set accuracies of 91.79/88.92%. Parametrical assumptions were checked. Desirability analysis was used to explore the levels of the predictors that produce the most desirable partition properties. Finally, inversion of the partition direction for each one of the 38 partition systems evidences that our models correctly classified 89.08% of compounds with an uncertainty of only +/-0.17% independently of the direction of the partition process used to seek the model. Other 10 different classification models (linear, neural networks, and genetic algorithms) were also tested for the same purposes. None of these computational models favorably compare with respect to the linear model indicating that our approach capture the main aspects that govern chemicals partition in different systems.

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QSAR treatment of drugs transfer into human breast milk

Cited by 41 publications

References 78 publications

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

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