| 總結: | Breast milk is the most complete infant nutrition, which is why breastfeeding is recommended as the optimal feeding choice for most infants. However, humans are also constantly exposed to environmental pollutants, often with potentially synergistic effects and at levels that can cause side effects. Because milk is the only nutrient source for the infant, a newborn will be exposed to all the xenobiotics present in the milk. The milk-to-plasma-concentration ratio is a key parameter used to estimate an infant’s exposure to different xenobiotics. Due to the countless number of chemicals released into environment, computational in silico methods and quantitative structure-activity relationships (QSARs) are gaining more and more attention in assessing this risk. The ability to predict the approximate amount of a chemical that might be present in milk from its structure can be very useful in the clinical setting. Molecular descriptors are numerical values that characterize properties of molecules, i.e., experimentally measured physicochemical properties (empirical) or calculated values from algorithms, such as two-dimensional fingerprints or three-dimensional structure. In silico QSAR models enable us to identify the essential structural characteristics that are responsible for secretion of a xenobiotic into milk. These models can be used to screen the milk/plasma partitioning potential for a huge number of compounds using data in existing xenobiotics/drugs databases. © 2016 by Taylor & Francis Group, LLC.
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