Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors (original) (raw)
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Molecular descriptors that influence the amount of drugs transfer into human breast milk
Journal of Pharmaceutical and Biomedical Analysis, 2002
Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. 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. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (xV2, x2 and x1) and shape indices (k3, k2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.
Prediction of drug transfer into human milk from theoretically derived descriptors
Analytica Chimica Acta, 2000
The goal of this study was to develop a genetic neural network (GNN) model to predict the degree of drug transfer into breast milk, depending on the molecular structure descriptors, and to compare it with the current model. A supervised network with back-propagation learning rule and multilayer perceptron (MLP) architecture was used to correlate activity with descriptors that were preselected by a genetic algorithm. The set of 60 drug compounds and their experimentally derived M/P values used in this study were gathered from literature. A total of 61 calculated structural features including constitutional, topological, chemical, geometrical and quantum chemical descriptors were generated for each of the 60 compounds. The M/P values were used as the ANNs output and calculated molecular descriptors as the inputs.
Prediction of Milk/Plasma Concentration Ratio of Drugs
Annals of Pharmacotherapy, 2003
OBJECTIVE: The milk to plasma (m/p) concentration ratio of drugs is used to estimate the amount of drug offered to the suckling infant. Published literature was reviewed to identify drugs for which sufficient data exist for calculation of m/p ratio and to examine whether the existing empiric data agree with the published method of Atkinson for mathematical prediction of m/p ratios based on physiochemical characteristics. METHODS: Using a comprehensive reference text, we identified studies reporting sufficient data to calculate m/p ratio based on the AUC for milk and plasma. Subsequently, we calculated the m/p ratio with Atkinson's formula based on pKa, lipophilicity, and protein binding. We then correlated the empiric versus predicted (calculated) m/p ratios. RESULTS: Of 192 drugs of which at least some data on milk accumulation have been published, there were sufficient data to quantify m/p ratios for only 69 medications (78 studies). There was no significant correlation betwee...
QSAR treatment of drugs transfer into human breast milk
Bioorganic & Medicinal Chemistry, 2005
A satisfactory model is developed using CODESSA PRO CODESSA PRO for the correlation and prediction of milk to plasma concentration ratios (M/P ratio) for diverse pharmaceuticals. A set of experimentally derived M/P ratio values were collected from the literature for 115 widely used pharmaceuticals. The experimental logarithmic M/P ratios were tested with more than 850 theoretical molecular descriptors including constitutional, topological, geometrical, quantum chemical, thermodynamic, and electrostatic types. Based on the data set, for 100 commonly used drugs, a seven-parameter QSAR model was derived that shows a satisfactory (R 2 = 0.791) correlation between predicted and observed values of log (M/P) ratio.
Pharmacokinetics of Toxic Chemicals in Breast Milk: Use of PBPK Models to Predict Infant Exposure
Environmental Health Perspectives, 2002
Maternal milk has been recognized by public health officials as the most beneficial source of nourishment during infancy. The U.S. Department of Health and Human Services, through the Healthy People 2010 objectives, has set a target goal of early postpartum breast-feeding rates of 75% by the year 2010 (1). This emphasis on breast-feeding is motivated by the fact that breast milk provides the most complete form of nutrition for infants, imparts increased protection from diseases, and improves maternal health through the physiologic responses associated with lactation. However, potential risks associated with breast-feeding also need to be factored into the overall public health assessment when women are encouraged to breastfeed their newborn infants (2). For example, through the process of breast-feeding, it is possible for the mother to transfer to the suckling infant potentially toxic chemicals to which the mother has previously been exposed. Due to the rapid mental and physical changes that are taking place, neonates can be more susceptible to adverse effects resulting from chemical exposures (2,3). Historically, the study of prescription drugs has provided a basis for understanding the governing principles behind transfer of chemicals through breast milk (4). These factors can be separated into two broad categories: maternal characteristics and chemical characteristics (5,6). Maternal characteristics include the degree of maternal exposure, physiology of the mother, maternal age, and parity (number of pregnancies). Chemical characteristics refer to aspects of the compound that affect its ability to be taken up in milk, such as the lipid solubility, degree of ionization, molecular weight, and ability to bind to maternal blood and/or milk components.
Drug distribution within human milk phases
Journal of Pharmaceutical Sciences, 1985
C Phase distribution and protein binding of drugs in human milk have been measured. The analytical method is reproducible, rapid, and requires only small sample volumes. Five drugs were studied: diazepam, phenobarbital, warfarin, phenytoin, and disopyramide. Ex periments were carried out at 37°C on milk samples with variable fat and protein contents. Results for the distribution of drugs between the skimmed-milk phase and fat-rich phase are presented, as well as the results of the dialysis of drugs in skimmed milk. It is shown that, among the physicochemical properties of a drug, the lipid solubility seems to be the most important property for predicting variations in drug concentra tions in milk. The potential significance of the findings with respect to in vivo distribution of drugs into human milk is discussed.
An exploratory study of a simple approach for evaluating drug solubility in milk related vehicles
Milk related materials are frequently used as a vehicle for drug product administration. Therefore, drug solubility information in milk related vehicles is desirable for prediction of how they may influencein vivodrug release and bioavailability. However, there are very limited data published on this topic. This study explored a practical method to address the key challenges associated with solubility assessment in milk, including the sample equilibration time and cleanup procedures. Amitriptyline, acetaminophen, dexamethasone, nifedipine, piroxicam, and prednisolone were selected as model drugs to represent a wide range of physicochemical properties. Their solubilities were determined at room temperature in pH 6.8 phosphate buffer, skim milk, whole milk, reconstituted whole milk powder, and unprocessed raw milk. The overall results confirmed that milk greatly improves the solubility of poorly water-soluble drugs. However, the extent of improvement and mechanism of solubilization ap...
Dissolution Technologies
• Human drug delivery: Cow milk is a potential vehicle for delivering drugs to pediatric and geriatric patients (4-6). Therefore, the issue of drug solubility in milk, or its adsorption to milk proteins and fats, is relevant for both human and animal health. This led the USP to initiate an effort to define the composition of milk in normal and mastitic cattle and the variability that may exist across cow nutritional and health status. • Milk has been suggested as a component of fedstate simulated gastric fluids (SGFs) (7).
Applied Sciences
The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection...
Prediction of Drug Absorption Using Multivariate Statistics
Journal of Medicinal Chemistry, 2000
Literature data on compounds both well-and poorly-absorbed in humans were used to build a statistical pattern recognition model of passive intestinal absorption. Robust outlier detection was utilized to analyze the well-absorbed compounds, some of which were intermingled with the poorly-absorbed compounds in the model space. Outliers were identified as being actively transported. The descriptors chosen for inclusion in the model were PSA and AlogP98, based on consideration of the physical processes involved in membrane permeability and the interrelationships and redundancies between available descriptors. These descriptors are quite straightforward for a medicinal chemist to interpret, enhancing the utility of the model. Molecular weight, while often used in passive absorption models, was shown to be superfluous, as it is already a component of both PSA and AlogP98. Extensive validation of the model on hundreds of known orally delivered drugs, "drug-like" molecules, and Pharmacopeia, Inc. compounds, which had been assayed for Caco-2 cell permeability, demonstrated a good rate of successful predictions (74-92%, depending on the dataset and exact criterion used).