Prediction of drug transfer into human milk from theoretically derived descriptors

Breast Milk Excretion of Dinalbuphine Sebacate Injection Administered After Cesarean Section

Ensuring the safety of analgesics during lactation is crucial for women of childbearing potential. Available data regarding the transfer of nalbuphine for postoperative acute pain via breast milk are limited to the postmarketing experience. This lactation study aimed to assess nalbuphine and dinalbuphine sebacate concentrations in breast milk from lactating women with postoperative pain treated with dinalbuphine sebacate extended-release injection (150 mg dinalbuphine sebacate/2 mL Naldebain). Breast milk was collected throughout the 5-day posthospitalization interval from 20 mothers injected with one dose of extended-release dinalbuphine sebacate intramuscularly. Maternal safety was assessed during the study period. Nalbuphine was detectable in 71% of milk samples collected from all mothers, whereas dinalbuphine sebacate was undetectable or below the quantitation limit (0.1 ng/mL). The mean nalbuphine concentration in milk was approximately 10.55 ng/mL, with the peak concentration reaching up to 12.7 ng/mL. The mean relative infant dose was 0.39% (coefficient of variation, 65%). The mean pain intensity at rest was reduced to mild pain from Day 2 morning to discharge. Overall, the maternal safety profile was tolerable. The breast milk of women who receive one dose of dinalbuphine sebacate injection postpartum contains low nalbuphine concentration. In addition, dinalbuphine sebacate injection potentially reduces maternal pain intensity during the first postpartum week and offers low toxicity risk among breastfed infants.

Machine Learning Models for Prediction of Xenobiotic Chemicals with High Propensity to Transfer into Human Milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via the transfer of environmental chemicals from maternal exposome is a significant concern for infant health. The milk to plasma concentration (M/P) ratio is a critical metric that quantifies the extent to which these chemicals transfer from maternal plasma into breast milk, impacting infant exposure. Machine learning-based predictive toxicology models can be valuable in predicting chemicals with a high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated data set, to date, of 375 chemicals with known milk-to-plasma concentration (M/P) ratios. Our support vector machine (SVM)-based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and an external test data set. Specifically, the SVM-based classifier on (internal) test data achieved a classification accuracy of 77.33%, a specificity of 84%, a sensitivity of 64%, and an F-score of 65.31%. When evaluated on an external test data set, our SVM-based classifier is found to be generalizable with a sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in the earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. Overall, this study attests to the immense potential of predictive computational toxicology models in characterizing the myriad of chemicals in the human exposome.

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants. A physiologically based pharmacokinetic model was built for sotalol, a renally eliminated drug, to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeeding-relevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

PURPOSE

Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/P_AUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer.

METHODS

We collected M/P ratio data from literature, which were curated and divided into M/P_AUC ≥ 1 and M/P_AUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM).

RESULTS

M/P ratios of 403 compounds were collected, M/P_AUC data were obtained for 173 compounds, while 230 compounds only had M/P_non-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models.

CONCLUSIONS

We built a M/P_AUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model.

Statistical Methods in the Study of Protein Binding and Its Relationship to Drug Bioavailability in Breast Milk

Protein binding (PB) is indicated as the factor most severely limiting distribution in the organism, reducing the bioavailability of the drug, but also minimizing the penetration of xenobiotics into the fetus or the body of a breastfed child. Therefore, PB is an important aspect to be analyzed and monitored in the design of new drug substances. In this paper, several statistical analyses have been introduced to find the relationship between protein binding and the amount of drug in breast milk and to select molecular descriptors responsible for both pharmacokinetic phenomena. Along with descriptors related to the physicochemical properties of drugs, chromatographic descriptors from TLC and HPLC experiments were also used. Both methods used modification of the stationary phase, using bovine serum albumin (BSA) in TLC and human serum albumin (HSA) in HPLC. The use of the chromatographic data in the protein binding study was found to be positive -the most effective application of normal-phase TLC and HPLC_HSA data was found. Statistical analyses also confirmed the prognostic value of affinity chromatography data and protein binding itself as the most important parameters in predicting drug excretion into breast milk.

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

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 and r 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.

Biological barriers, and the influence of protein binding on the passage of drugs across them

Drug-protein binding plays a key role in determining the pharmacokinetics of a drug. The distribution and protein binding ability of a drug changes over a lifetime, and are important considerations during pregnancy and lactation. Although proteins are a significant fraction in plasma composition, they also exist beyond the bloodstream and bind with drugs in the skin, tissues or organs. Protein binding influences the bioavailability and distribution of active compounds, and is a limiting factor in the passage of drugs across biological membranes and barriers: drugs are often unable to cross membranes mainly due to the high molecular mass of the drug-protein complex, thus resulting in the accumulation of the active compounds and a significant reduction of their pharmacological activity. This review describes the consequences of drug-protein binding on drug transport across physiological barriers, whose role is to allow the passage of essential substances-such as nutrients or oxygen, but not of xenobiotics. The placental barrier regulates passage of xenobiotics into a fetus and protects the unborn organism. The blood-brain barrier is the most important barrier in the entire organism and the skin separates the human body from the environment.

Translational studies on a ready-to-use intramuscular injection of penethamate for bovine mastitis

Bovine mastitis caused by bacterial infections of the mammary gland (udder) of dairy cows is a costly pathology for the dairy industry due to direct and indirect losses in production. Penethamate, a pro-drug of benzylpenicillin, is used by intramuscular injection (IM). The existing products are powders which must be reconstituted in water-for-injection and this presents difficulties in the field. Penethamate is too unstable to be formulated as an aqueous formulation but a chemically stable suspension formulation was possible in certain oils; however, some literature suggests that such formulations would have unacceptable prolonged release. The translational research proceeded iteratively from lab to the target species, rather than via laboratory animal trials. Pilot studies in cows suggested that some oily suspensions would give concentrations of benzylpenicillin, (in both blood and milk) comparable with those of the reconstituted product. A physicochemical screen and a low level in vitro-in vivo correlation (IVIVC) was cautiously used to guide selection of formulations for subsequent animal trials which have resulted in a lead formulation for good laboratory practices (GLP), good clinical practices (GCP) studies.

Quantification of volatile metabolites derived from garlic in human breast milk

Maternal garlic intake during pregnancy and the breastfeeding period has been reported to be associated with the potential of modulating later garlic acceptance in infants. However, the metabolism of garlic constituents in humans and their elimination and potential excretion into human milk are not yet fully understood. In previous studies, we identified volatile garlic-derived metabolites in human milk as well as in human urine, namely allyl methyl sulfide, allyl methyl sulfoxide and allyl methyl sulfone. To monitor the excretion of these garlic metabolites in a larger cohort, we quantified these metabolites in a total of 18 human milk sets, whereby each set comprised of one sample collected before and three samples after garlic consumption. The analyses revealed that the concentrations of the metabolites were most abundant 1-3.5 h after garlic consumption, with distinct differences between test persons regarding metabolite concentrations as well as temporal excretion.

Modeling drug passage into human milk

Breastfeeding has positive health consequences for both the breastfed infant and the nursing mother.(1,2) Although information on drug use during lactation is available through sites such as LactMed,(3) available information is often incomplete. Unlike pregnancy, in which large numbers of pregnant women need to be studied to assure safety, measurement of drug concentrations in breastmilk in a relatively few subjects can provide valuable information to assess drug safety. This article reviews methods of measuring and predicting drug passage into breastmilk.

Quantitative and qualitative prediction of corneal permeability for drug-like compounds

A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q(2)=0.584 and SPRESS=0.378 for MLR and Q(2)=0.774 and SPRESS=0.087 for MLP-NN. The obtained results revealed the suitability of MLP-NN for the prediction of corneal permeability. The contribution of each descriptor to MLP-NN model was evaluated. It indicated the importance of the molecular volume and weight. The pattern recognition methods principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been employed in order to investigate the possible qualitative relationships between the molecular descriptors and the corneal permeability. The PCA and HCA results showed that, the data set contains two groups. Then, the same descriptors used in quantitative modeling were considered as inputs of counter propagation neural network (CPNN) to classify the compounds into low permeable (LP) and very low permeable (VLP) categories in supervised manner. The overall classification non error rate was 95.7% and 95.4% for the training and prediction test sets, respectively. The results revealed the ability of CPNN to correctly recognize the compounds belonging to the categories. The proposed models can be successfully used to predict the corneal permeability values and to classify the compounds into LP and VLP ones.

QSAR study of PETT derivatives as potent HIV-1 reverse transcriptase inhibitors

A series of phenylethylthiazolylthiourea (PETT) derivatives was subjected to quantitative structure-activity relationship (QSAR) analysis to find the structural requirements for ligand binding. The structural invariants used in this study were those obtained from whole molecular structures: chemical, quantum, topological, geometrical, constitutional and functional groups. Four chemometrics methods including multiple linear regressions (MLRs), factor analysis-MLR (FA-MLR), principal component regression analysis (PCRA) and partial least squares combined with genetic algorithm for variable selection (GA-PLS) were employed to make connections between structural parameters and enzyme inhibition. Using the pool of all types of calculated descriptors a QSAR model was derived for selected calibration set compounds indicating the importance of geometrical and chemical parameters on the Human Immunodeficiency Virus Type-1 (HIV-1) reverse transcriptase inhibitory activity. The results of FA-MLR analysis revealed the effects of geometrical and chemical indices on the inhibitory activity too. GA-PLS analysis showed the constitutional and geometrical indices to be the most significant parameters on inhibitory activity. A comparison between the different statistical methods employed indicated that PCRA represented superior results and it could explain and predict 74% and 79% of variances in the pIC(50) data, respectively.

In vitro and in vivo interaction of moxidectin with BCRP/ABCG2

The study characterizes the interaction between BCRP/ABCG2 and moxidectin by means of cellular transport, and pharmacokinetic studies in Bcrp1 (-/-) and wild-type mice. Milbemycin moxidectin ([(3)H]-moxidectin) was tested for its ability to be transported across MCDK-II epithelial monolayer cultures transfected with BCRP. In a second approach, accumulation assays by BCRP-expressing Xenopus laevis oocytes were carried out. Finally, pharmacokinetic studies were performed in order to establish the role of the transporter in milk secretion and tissue distribution. The efflux was negligible in polarized cells but moxidectin was efficiently transported in BCRP-expressing X. laevis oocytes. The transport was blocked by an acridone derivative, a novel BCRP inhibitor. Moxidectin secretion into breast milk was decreased in Bcrp1-knockout mice and the milk to plasma ratio was 2-fold higher in wild-type mice after i.v. administration. Drug accumulation in intestinal content, bile, and intestine was higher in wild-type mice but the plasma concentration was not different. Moxidectin is identified as a BCRP substrate since its Bcrp1-mediated secretion into breast milk and the involvement of Bcrp1 in intestinal and bile secretion has been demonstrated. This interaction has pharmacokinetic and toxicological consequences. The most important toxicological consequences of the interaction between BCRP and moxidectin may be related with the presence of drug residues in milk.

QSAR study of p56(lck) protein tyrosine kinase inhibitory activity of flavonoid derivatives using MLR and GA-PLS

Quantitative relationships between molecular structure and p56(lck) protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R(2) = 0.74 and Q(2) = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56(lck) protein tyrosine kinase inhibitors than those provided previously.

Prediction of blood-brain partitioning: a model based on ab initio calculated quantum chemical descriptors

A new model for the prediction of log BB, a penetration measure through the blood-brain barrier, based on a molecular set of 82 diverse molecules is developed. The majority of the descriptors are derived from quantum chemical ab initio calculations, augmented with a number of classical descriptors. The quantum chemical information enables one to compute fundamental properties of the molecules. The best set of descriptors was selected by sequential selection and multiple linear regression was used to develop the QSAR model. The predictive capability of the model was tested using internal and external test procedures and the domain of applicability was determined to identify reliable predictions. The selected set of descriptors shows a significant correlation with the experimental log BB. The proposed model could reproduce the data with an error approaching the experimental uncertainty and satisfies the available validation procedures. The obtained results indicate that the use of quantum chemical information in describing molecules improves the behavior of the model.

Prediction of Milk/Plasma Drug Concentration (M/P) Ratio Using Support Vector Machine (SVM) Method

PURPOSE

Development of reliable computational models to predict/classify milk-to-plasma (M/P) drug concentration ratio remains a challenging object. Support vector machine (SVM) method, as a new algorithm, was constructed to distinguish the potential risk of drugs to nursing infants.

METHODS

Each drug was represented by a large pool of descriptors, of which five were found to be most important for constructing the predictive models. Next, two classification models, linear discriminant analysis (LDA) and SVM, were developed with bootstrapping validation based on the selected molecular descriptors.

RESULTS AND CONCLUSIONS

The classification accuracy of training set and test set for SVM was 90.63 and 90.00%, respectively. The total accuracy for SVM was 90.48%, which was higher than that of LDA (77.78%). Comparison of the two methods shows that the performance of SVM was better than that of LDA, which implies that the SVM method is an effective tool in evaluating the risk of drugs when experimental M/P ratios have not been investigated.

Individualized drug and dose: The clinical pharmacologist's calling or curse?

1. The role of the clinical pharmacologist is to promote the rational, safe and effective use of medicines. This revolves around the notion of variability, between and within patients and between and within drugs, in terms of both pharmacokinetics and pharmacodynamics. Ideal therapeutics involves tailoring the drug and its dosing to the individual patient, taking into account this variability. 2. In the 25 years of my membership of the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists, three themes have dominated my research: (i) drugs and breast feeding; (ii) aminoglycoside dosing; and (iii) pharmacogenetics. In all these, the research has been orientated towards identifying factors involved in variability and working towards dose individualization based on the understanding of these factors. 3. Our model for predicting drug concentrations in milk has assisted not only in estimating the safety of drug ingestion via breast milk, but also in the understanding of the processes involved in drug transfer. 4. The aminoglycoside studies have assisted in the understanding of the basis behind extended interval dosing, leading to a model for dose prediction that is widely used, especially in Australasia. 5. Pharmacogenetics is a field widely acclaimed as having a huge future in terms of individualization of drug therapy. Our early studies in this area lend only cautious support to this optimism.

Drug use and breastfeeding

Section 1 describes the benefits of breastfeeding to both mother and infant as well as the potential risks to the infant from maternal drug use. The extent of adverse drug-related events and the need for quality information on drug transfer is stated. Section 2 describes the physiology of lactation and the effects of drugs that stimulate or decrease milk production. Section 3 deals with transport mechanisms for drug passage into milk and factors that may modify the infant's exposure to drugs. The critical descriptors of 'absolute' and 'relative' infant dose are defined to give an objective measure of infant exposure to drugs in milk. Section 4 reviews new or commonly used drugs under the headings of analgesics and anti-inflammatory agents, neurological, endocrine, psychotropic and antihypertensive drugs. Section 5 concludes with an expert opinion of the drug industry and drug use in lactation, herbal preparations, the process of 'risk-benefit' analysis, minimising infant exposure, understanding drug-related adverse events and fostering good experimental design for drugs in lactation studies.

Quantitative Structure-Pharmacokinetic Relationships for Drug Distribution Properties by Using General Regression Neural Network

Quantitative Structure-Pharmacokinetic Relationships (QSPkR) have increasingly been used for developing models for the prediction of the pharmacokinetic properties of drug leads. QSPkR models are primarily developed by means of statistical methods such as multiple linear regression (MLR). These methods often explore a linear relationship between the pharmacokinetic property of interest and the structural and physicochemical properties of the studied compounds, which are not applicable to those agents with nonlinear relationships. Hence, statistical methods capable of modeling nonlinear relationships need to be developed. In this work, a relatively new kind of nonlinear method, general regression neural network (GRNN), was explored for modeling three drug distribution properties based on diverse sets of drugs. The three properties are blood-brain barrier penetration, binding to human serum albumin, and milk-plasma distribution. The prediction capability of GRNN-developed models was compared to those developed using MLR and a nonlinear multilayer feedforward neural network (MLFN) method. For blood-brain barrier penetration, the computed r(2) and MSE values of the GRNN-, MLR-, and MLFN-developed models are 0.701 and 0.130, 0.649 and 0.154, and 0.662 and 0.147, respectively, by using an independent validation set. The corresponding values for human serum albumin binding are 0.851 and 0.041, 0.770 and 0.079, and 0.749 and 0.089, respectively, and that for milk-plasma distribution are 0.677 and 0.206, 0.224 and 0.647, and 0.201 and 0.587, respectively. These suggest that GRNN is potentially useful for predicting QSPkR properties of chemical agents.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous)

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Antibiotics and breast-feeding: a critical review of the literature

Continuous breast-feeding, an integral component of the postpartum period, is often threatened upon maternal initiation of antibiotics. The real risk of antibiotic use while breast-feeding must be carefully analysed with regard to all the variables that influence the extent of antibiotic distribution into breast milk, including breast milk composition, physicochemical properties of the antibiotic (molecular weight, lipid solubility, pH, protein binding), length of feeding, and maternal disposition. In addition, infant disposition, including ability to absorb, metabolize, eliminate, and tolerate any amounts of antibiotic, must also be considered prior to maternal administration of antibiotic. The milk to plasma (M/P) ratio is a frequently quoted parameter used to predict drug distribution into breast milk. However, its utility is questionable and often fraught with misinterpretation. An alternative approach when the amount of antibiotic concentration in breast milk is known (through clinical trials) is to calculate an estimated or expected infant drug exposure factoring in known/expected milk consumption, drug concentration and bioavailability. In this review, the following antibiotic classes and current literature regarding their distribution into breast milk are critically reviewed: beta-lactam antibiotics, fluoroquinolones, sulfonamides, macrolides, aminoglycosides, tetracyclines, nitrofurantoin, metronidazole, vancomycin, clindamycin and chloramphenicol. In the majority of instances, these antibiotics do not distribute into breast milk in sufficient concentrations to be of any clinical consequence in the breast-feeding infant.

Models and methods for predicting drug transfer into human milk

Adverse effects in infants due to the ingestion of drugs and other xenobiotics remain an area of concern. A key parameter in assessing infant exposure via breast milk, the milk to plasma concentration ratio (M/P), has not been determined in vivo in humans for most drugs. There are various methods for predicting M/P, which involve in vitro experiments in mammary cell monolayers, assessment of drug binding to plasma and milk protein and lipid, in vivo experiments in animals, and regression models based on a compound's physicochemical characteristics. This article reviews these approaches in terms of their utility, advantages and disadvantages. Some combination of these methods is necessary for reasonably accurate prediction of M/P in humans.

Molecular descriptors that influence the amount of drugs transfer into human breast milk

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 (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), 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.

Modeling the partition of volatile aroma compounds from a cloud emulsion

Parameters determining the partitioning behavior of volatile compounds between a cloud emulsion and the gas phase were measured under static equilibrium headspace conditions, using volatiles (e.g., ethyl hexanoate, cymene, and octanol) representing different volatilities and different degrees of hydrophobicity. The significant factors were the molecular characteristics of the volatile and the concentration of the oil phase. The nature of the lipid (C8 and C12 triglycerides), particle size, and emulsifier type (modified starch and gum arabic) did not significantly alter volatile partitioning. An empirical model based on the partition behavior and physicochemical parameters of 67 volatile compounds was produced. This predicted the partition of volatiles (R(2) = 0.83) in cloud emulsions as a function of lipid content. The significant terms (P < 0.05) in the empirical model were Log P, Log solubility, the dipole vector, and the oil fraction.