Molecular descriptors that influence the amount of drugs transfer into human breast milk
Introduction
Breastfeeding is an essential physiologic process that provides nutrition and protects a child against infection and immunologic disorders. The frequency of various diseases and metabolic disorders are less in a breastfed infant. When drugs are administered to a nursing mother, a small part of them may appear in her milk [1]. Most drugs pass into breast milk, but the dose is reduced and usually does not produce a pharmacological effect. Certain drugs, however, do reach greater levels in milk than in the mother. Because of the infant's small size and the difference in metabolism between infants and adults, occasionally this transfer of medication can be harmful to the infant [2], [3], [4]. The amount of drug excreted into milk depends on a number of kinetic factors: (1) lipid solubility of the drug; (2) molecular size of the drug; (3) blood level attained in the maternal circulation; (4) protein binding in the maternal circulation; (5) oral bioavailability in the infant and the mother; and (6) half-life in the maternal and infant's plasma compartments.
Human milk is a suspension of protein and fat globules in a carbohydrate-based solution [5]. The mechanisms by which medications are transferred into breast milk are no different than those governing passage into any other maternal body fluid or organ system. Drugs enter milk primarily by passive diffusion reaching concentration equilibrium with the concentration in the blood, but also by active secretory methods that can concentrate the drug in the breast milk [6], [7], [8].
The most important determinant of drug penetration into milk is the mother's plasma level. Drugs enter milk and, in most cases, exit milk as a function of the mother's plasma level. Of the many factors, perhaps the two most important and useful are the degree of protein binding and lipid solubility. Drugs that are extremely lipid soluble penetrate milk in higher concentrations (i.e. CNS active drugs). Protein binding also plays a very important role. Drugs that have high maternal protein binding almost invariably produce lesser levels in milk. Most drugs circulate in the maternal plasma bound to a large molecular weight protein called albumin. An unbound component remains freely soluble in the plasma and transfers into milk, while the bound fraction stays in the maternal plasma unable to reach the tissues. For weak acids and bases, excretion into breast milk is governed by factors such as their pKa, their concentration in plasma and the pH of the milk and plasma. In some instances, drugs become ion trapped in milk. Due to the lower pH of human milk, the physicochemical structure of the drug changes and prevents its perfusion back into the maternal circulation. Hence, they become ion trapped in the milk. Also, drugs may concentrate in the milk due to specialized transport systems, which ‘pump’ substances into the milk. In addition, a small water-soluble molecule, such as alcohol, may pas into the milk through aqueous pores in the membrane.
Assessing the risk of maternal medication to the breast-feed infant requires knowledge of the concentration of drug that might be present in the milk. This concentration can be calculated for a particular drug if the milk to plasma (M/P) concentration ratio is known for the drug. The M/P value is an attempt to identify the equilibrium concentration between breast milk and blood. It is equivalent to the drug concentration in the breast milk divided by the maternal serum concentration. As almost all drugs pass into milk from maternal plasma by passive diffusion, the M/P ratio is affected by the composition of the milk (aqueous, lipid, protein and pH) and the physicochemical characteristics of the drug (degree of ionization, molecular weight protein binding and lipophilicity).
Lower molecular weight drugs with high lipophilicity that are less protein-bound, nonionized, are more likely to diffuse into breast milk. The more lipid-soluble a medication, the more likely it will diffuse into milk. Breast milk has a lower pH (7.08) than plasma (7.42), causing weak bases to pass more readily into the milk than weak acids. Drugs that tend to concentrate in milk are weak bases, with low plasma protein binding and high lipid solubility. Additionally, medications may be transferred into breast milk incorporated within fat globules or bound to proteins, primarily casein and lactalbumin. Highly protein-bound drugs, though, are unlikely to cross extensively into breast milk, since these drugs bind preferentially to serum albumin [9], [10]. Finally, large molecules are not able to diffuse passively into the milk.
There is a tremendous need for more information on the safety of drugs while breastfeeding. The milk to plasma drug concentration ratios has been determined experimentally for many drugs. This ratio is most reliable when it comes from studies where the area under the concentration–time profiles has been measured over a whole dose interval. Unfortunately, most of the data on breastfeeding and drugs is based on single case reports or small case series. M/P data based on single time point concentration measurements in the two phases can be misleading because the time course of concentrations in milk and plasma may not parallel each other. Using these experimental data, empirical regression equations have been developed relating milk plasma ratio to the drug pKa, the octanol water partition coefficient and plasma protein binding [11]. A log-transformed phase-distribution model appears to have good predictive performance [12]. The disadvantage of these methods is that plasma protein binding for the drug must be known or experimentally determined. Since these physico-chemical drug properties are not always available, a theoretical method that could predict the milk plasma ratio from the drug structure would be of interest.
The aim of this research was to simplify and upgrade a previously developed genetic neural network (GNN) model for prediction of M/P ratio given only the molecular structure of the drug [13]. The model is based only on theoretical molecular descriptors that can be calculated directly from molecular structure. This approach has potential use for drug information services when experimental physico-chemical properties of the drug are not available and experimental milk plasma ratios have not been investigated.
An artificial neural network is a biologically inspired computer program designed to simulate the way in which the human brain processes information. ANNs are composed of hundreds of single processing elements (PE). Each PE has weighted inputs, transfer function and one output. PEs are connected with coefficients (weights) and are organized in a layered feed forward topology, the input layer, the output layer and the hidden layers between them. The number of layers and the number of units in each layer determines the function complexity.
Neural networks gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience with appropriate learning exemplars. The input layer neurons receive data from a data file. The output neurons provide the ANN's response to the input data. Units in the input layer do not process. They simply pass an output value onto units in the second layer. Each hidden or output unit performs a biased weighted sum of inputs and passes this activation signal through an activation function (also known as a transfer function) to produce their output.
Multilayer perceptron (MLP) is the most commonly used type of feed-forward network. MLPs use a linear activation signal or post synaptic potential (PSP) to combine incoming inputs and usually a non-linear activation function (also known as a transfer function). PEs are defined by their weights and threshold. Linear PSP units perform a weighted sum of their inputs, biased by the threshold value. Thus, a parameterized model is formed, with the weights and thresholds (biases) as the free parameters of the model. The standard activation function for MLPs is the logistic function. It is an S-shaped (sigmoid) curve, with output in the range (0.1).
When the network is executed, the input variable values are placed in the input units and then the hidden and output layer units are progressively executed. Each unit in the proceeding layers calculates its PSP of the neuron by taking the weighted sum of the outputs of the units in the preceding layer and subtracting the threshold. The activation signal is than passed through the activation function to produce the output of the neuron. When the entire network has been executed, the outputs of the output layer act as the output of the entire network.
Section snippets
Experimental
Neural Networks TM (StatSoft Inc., Tulsa) was used for building the QSAR model. For calculating drug properties from molecular structure, BioMed CAChe Project leader (Fujitsu America, Inc.) and ACD/ChemSketch (Toronto, Canada) were used.
Results and discussion
The first step in developing QSAR was to calculate molecular descriptors. Seventy-one calculated structural features, including constitutional, topological, geometrical, quantum chemical and physicochemical descriptors, were generated for each drug molecule. The next step was to select descriptors that effect drug passage into breast milk.
Selection of the important molecular descriptors and examination of the variable contribution to the model through output sensitivity is an important aspect
Conclusion
A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. Unlike previously reported models, the QSPR model described here does not require experimental parameters and could potentially provide useful prediction of M/P ratio of new drugs and reduce the need for actual compound synthesis and M/P ratio measurements. Model can be used to estimate the activities of other molecules only from a sketch of their
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