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

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.

Effects of 2-acetyl-4-tetrahydroxybutylimidazole exposure during gestation and lactation on maternity and offspring immune function in Balb/c mice

2-Acetyl-4-tetrahydroxybutylimidazole (THI), a by-product of Class Ⅲ caramel color, is generally recognized to cause lymphopenia in mammals. However, it remains unknown whether THI exposure during gestation and lactation causes damage to the immune system of offspring. In this study, pregnant Balb/c mice were gavaged with 0, 0.5, 2.5 and 12.5 mg/kg THI from gestation day (GD) 6 to postanal day (PND) 21, after which we treated another batch of dams from GD6 to PND21 and the offspring for 3 weeks after weaning with 0, 2, 10, 50 mg/L THI in drinking water respectively, and investigated the immunological anomalies of dams and offspring. The results showed that lymphopenia was observed in dams but not in weaning pups on PND21, which were exposed to THI during gestation and lactation. 2 mg/L THI and 2.5 mg/kg THI began to cause a remarkable reduction of the numbers of white blood cells and lymphocytes in dams. Besides both the cellular and the humoral immune response was not affected in weaning pups, which were measured by plaque-forming cell (PFC) assay and delayed-type hypersensitivity (DTH) assay respectively. Furthermore, THI could be detected in the plasma of dams with a dose-dependent manner, but not in that of both female and male weaning pups. In both male and female offspring being treated with 10 and 50 mg/L THI for another 3 weeks after weaning, lymphocytopenia was observed and T lymphocytes including CD4+ and CD8+ cells were significantly reduced in their spleens except lymph nodes. 10 and 50 mg/L THI treatment increased CD4+ and CD8+ single positive cells in thymus of female and male weaning mice. Mitogen-induced proliferation ability of T cells in the spleen and lymph nodes was impaired in female weaning mice exposed 50 mg/L THI, while male weaning mice treated with 10 and 50 mg/L THI showed impairment in the spleen but not lymph nodes. Based on the results in this study, no observed adverse effect level (NOAEL) for 3-week THI treatment in weaning mice was considered to be 2 mg/L (0.30 mg/kg bw for female mice and 0.34 mg/kg bw for male mice). And NOAEL for THI treatment in dams might be set to 0.5 mg/kg bw/day. Collectively from the perspective of NOAEL, offspring are not more sensitive than dams or adult mice.

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.

Similarity-Based Virtual Screening to Find Antituberculosis Agents Based on Novel Scaffolds: Design, Syntheses and Pharmacological Assays

A method to identify molecular scaffolds potentially active against the Mycobacterium tuberculosis complex (MTBC) is developed. A set of structurally heterogeneous agents against MTBC was used to obtain a mathematical model based on topological descriptors. This model was statistically validated through a Leave-n-Out test. It successfully discriminated between active or inactive compounds over 86% in database sets. It was also useful to select new potential antituberculosis compounds in external databases. The selection of new substituted pyrimidines, pyrimidones and triazolo[1,5-a]pyrimidines was particularly interesting because these structures could provide new scaffolds in this field. The seven selected candidates were synthesized and six of them showed activity in vitro.

The European Human Biomonitoring Initiative (HBM4EU): Human biomonitoring guidance values (HBM-GVs) for the aprotic solvents N-methyl-2-pyrrolidone (NMP) and N-ethyl-2-pyrrolidone (NEP)

Toxicologically and/or epidemiologically derived guidance values referring to the internal exposure of humans are a prerequisite for an easy to use health-based interpretation of human biomonitoring (HBM) results. The European Joint Programme HBM4EU derives such values, named human biomonitoring guidance values (HBM-GVs), for priority substances which could be of regulatory relevance for policy makers and have been identified by experts of the participating countries, ministries, agencies and stakeholders at EU and national level. NMP and NEP are such substances for which unresolved policy relevant issues should be clarified by targeted research. Since widespread exposure of the general population in Germany to NMP and NEP was shown for the age groups 3–17 years and 20–29 years, further investigations on exposure to NMP and NEP in other European countries are warranted. The HBM-GVs derived for both solvents focus on developmental toxicity as decisive endpoint. They amount for the sum of the two specific urinary NMP metabolites 5-HNMP and 2-HMSI and likewise of the two specific urinary NEP metabolites 5-HNEP and 2-HESI to 10 mg/L for children and 15 mg/L for adolescents/adults. The values were determined following a consultation process on the value proposals within HBM4EU. A health-based risk assessment was performed using the newly derived HBM-GV_GenPop and exposure data from two recent studies from Germany. The risk assessment revealed that even when considering the combined exposure to both substances by applying the Hazard Index approach, the measured concentrations are below the HBM-GV_GenPop in all cases investigated (i.e., children, adolescents and young adults).

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HBM-GVs are a prerequisite for an easy to use health-based risk assessment of human biomonitoring results.

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For NMP and NEP metabolites in urine, respectively, HBM-GVs were set for children and adolescents/adults.

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First HBM exposure data indicate widespread exposure of German children, adolescents and young adults to NMP and NEP.

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The Hazard Index approach revealed that even when combined exposure to both solvents is assessed, HBM-GVs are not exceeded.

ExHuMId: A curated resource and analysis of Exposome of Human Milk across India

Human milk is a vital source of nourishment for infants. However, numerous environmental contaminants also find their way into human milk, making up the major part of a newborn's external exposome. While there are chemical regulations in India and scientific literature on environmental contaminants is available, the systematic compilation, monitoring, and risk management of human milk contaminants are inadequate. We have harnessed the potential of this large body of literature to develop the Exposome of Human Milk across India (ExHuMId) version 1.0 containing detailed information on 101 environmental contaminants detected in human milk samples across 13 Indian states, compiled from 36 research articles. ExHuMId also compiles the detected concentrations of the contaminants, structural and physicochemical properties, and factors associated with the donor of the sample. We also present findings from a three-pronged analysis of ExHuMId and two other resources on human milk contaminants, with a focus on the Indian scenario. Through a comparative analysis with global chemical regulations and guidelines, we identify human milk contaminants of high concern, such as potential carcinogens, endocrine disruptors and neurotoxins. We then study the physicochemical properties of the contaminants to gain insights on their propensity to transfer into human milk. Lastly, we employ a systems biology approach to shed light on potential effects of human milk contaminants on maternal and infant health, by identifying contaminant-gene interactions associated with lactation, cytokine signalling and production, and protein-mediated transport. ExHuMId 1.0 is accessible online at: https://cb.imsc.res.in/exhumid/.

A comprehensive review on non-clinical methods to study transfer of medication into breast milk - A contribution from the ConcePTION project

Breastfeeding plays a major role in the health and wellbeing of mother and infant. However, information on the safety of maternal medication during breastfeeding is lacking for most medications. This leads to discontinuation of either breastfeeding or maternal therapy, although many medications are likely to be safe. Since human lactation studies are costly and challenging, validated non-clinical methods would offer an attractive alternative. This review gives an extensive overview of the non-clinical methods (in vitro, in vivo and in silico) to study the transfer of maternal medication into the human breast milk, and subsequent neonatal systemic exposure. Several in vitro models are available, but model characterization, including quantitative medication transport data across the in vitro blood-milk barrier, remains rather limited. Furthermore, animal in vivo models have been used successfully in the past. However, these models don't always mimic human physiology due to species-specific differences. Several efforts have been made to predict medication transfer into the milk based on physicochemical characteristics. However, the role of transporter proteins and several physiological factors (e.g., variable milk lipid content) are not accounted for by these methods. Physiologically-based pharmacokinetic (PBPK) modelling offers a mechanism-oriented strategy with bio-relevance. Recently, lactation PBPK models have been reported for some medications, showing at least the feasibility and value of PBPK modelling to predict transfer of medication into the human milk. However, reliable data as input for PBPK models is often missing. The iterative development of in vitro, animal in vivo and PBPK modelling methods seems to be a promising approach. Human in vitro models will deliver essential data on the transepithelial transport of medication, whereas the combination of animal in vitro and in vivo methods will deliver information to establish accurate in vitro/in vivo extrapolation (IVIVE) algorithms and mechanistic insights. Such a non-clinical platform will be developed and thoroughly evaluated by the Innovative Medicines Initiative ConcePTION.

Review of the Effects of Perinatal Exposure to Endocrine-Disrupting Chemicals in Animals and Humans

Maternal exposure to endocrine-disrupting chemicals (EDCs) is associated with long-term hormone-dependent effects that are sometimes not revealed until maturity, middle age, or adulthood. The aim of this study was to conduct descriptive reviews on animal experimental and human epidemiological evidence of the adverse health effects of in utero and lactational exposure to selected EDCs on the first generation and subsequent generation of the exposed offspring. PubMed, Web of Science, and Toxline databases were searched for relevant human and experimental animal studies on 29 October 29 2018. Search results were screened for relevance, and studies that met the inclusion criteria were evaluated and qualitative data extracted for analysis. The search yielded 73 relevant human and 113 animal studies. Results from studies show that in utero and lactational exposure to EDCs is associated with impairment of reproductive, immunologic, metabolic, neurobehavioral, and growth physiology of the exposed offspring up to the fourth generation without additional exposure. Little convergence is seen between animal experiments and human studies in terms of the reported adverse health effects which might be associated with methodologic challenges across the studies. Based on the available animal and human evidence, in utero and lactational exposure to EDCs is detrimental to the offspring. However, more human studies are necessary to clarify the toxicological and pathophysiological mechanisms underlying these effects.

Distribution of hydrophilic and lipophilic antibacterial drugs in skim milk, cream, and casein

This study determined the distribution of drugs to different milk fractions according to their physicochemical properties. Hydrophilic drugs tend to concentrate in skim milk, whereas lipophilic drugs tend to concentrate in cream. The concentration of a drug in casein is related to its degree of binding to milk proteins. Thus, we aimed to determine whether withdrawal time in whole milk differs from that in cream, casein, and skim milk. Amoxicillin and tylosin were selected as prototype hydrophilic and lipophilic drugs, respectively. The study was conducted in vitro and in vivo to determine whether in vitro conditions reflect the distribution of drugs in the different milk fractions in vivo. The in vivo study was conducted using a crossover design on 6 healthy Holstein dairy cattle. First, amoxicillin (i.m., single dose, 14 mg/kg) was administered to cows. Following a 1-wk washout period, tylosin (i.m., single dose, 15 mg/kg) was administered. Concentrations of amoxicillin and tylosin in milk and milk fractions were measured using HPLC-UV. In the in vitro study, 0.04 to 400 μg/g of amoxicillin and 0.05 to 50 μg/g of tylosin were spiked to drug-free milk and the concentrations in milk and milk fractions were measured. In addition, the percentage of total protein in milk and milk fractions was determined. Amoxicillin accumulated more in skim milk than in cream and casein, both in vitro (92%) and in vivo (73%, skim milk-to-whole milk ratio). The distribution of tylosin in whole and skim milk was similar to that of amoxicillin in the in vitro study, in contrast to the accumulation of tylosin in cream seen in vivo. However, the accumulation ratio of tylosin in cream was lower than expected. By either method, tylosin was less concentrated in casein than in skim milk and cream. The percentage of total protein was similar in skim milk and whole milk and higher than in cream. Thus, amoxicillin accumulates less in cream and casein, suggesting that these fractions would pose a lower risk to the consumer. Tylosin was still present at the maximum residue limit (50 μg/kg) 24 h after injection in the casein fraction and 48 h after injection in the cream fraction.

Simple physicochemical properties related with lipophilicity, polarity, molecular size and ionization status exert significant impact on the transfer of drugs and chemicals into human breast milk

OBJECTIVES

The transfer of xenobiotic compounds into human breast milk has raised serious concerns in the last few years. The present study is aimed to assess whether simple physicochemical properties exert significant impact on human breast milk transfer of drugs and chemicals.

METHODS

A large data set of 375 xenobiotic compounds with available experimental milk to plasma (M/P) ratios was systematically compiled from the literature and explored with their physicochemical properties being further analyzed with respect to their extent to transfer into breast milk.

RESULTS

Xenobiotic compounds with increased breast milk transfer (M/P ≥ 1) were characterized by enhanced lipophilicity and decreased molecular size (p < 0.05). Enhanced polarity and hydrogen bonding capacity were more frequently observed in xenobiotic compounds with reduced breast milk transfer (p < 0.0001). Xenobiotic compounds presenting increased positive charge at pH 7.4 were characterized by enhanced breast milk transfer (p < 0.001). Xenobiotic compounds presenting increased negative charge at pH 7.4 were characterized by decreased breast milk transfer (p < 0.001).

CONCLUSIONS

The present study supports evidence that simple physicochemical properties related with lipophilicity, polarity, molecular size and ionization status exert significant impact on drugs and chemicals transport into human breast milk.

Search of Chemical Scaffolds for Novel Antituberculosis Agents

A method to identify chemical scaffolds potentially active against Mycobacterium tuberculosis is presented. The molecular features of a set of structurally heterogeneous antituberculosis drugs were coded by means of structural invariants. Three techniques were used to obtain equations able to model the antituberculosis activity: linear discriminant analysis, multilinear regression, and shrinkage estimation–ridge regression. The model obtained was statistically validated through leave- n-out test, and an external set and was applied to a database for the search of new active agents. The selected compounds were assayed in vitro, and among those identified as active stand reserpine, N,N,N′,N′-tetrakis-(2-pyridylmethyl)-ethylenediamine (TPEN), trifluoperazine, pentamidine, and 2-methyl-4,6-dinitro-phenol (DNOC). They show activity comparable to or superior to ethambutol, used in combination with other drugs for the prevention and treatment of Mycobacterium avium complex and drug-resistant tuberculosis.

Editor's Highlight: Transplacental and Lactational Transfer of Firemaster® 550 Components in Dosed Wistar Rats

Firemaster® 550 (FM 550) is a commercial mixture of organophosphate and brominated flame retardants currently in use as a replacement for pentaBDE. Its organophosphate components include triphenyl phosphate (TPHP) and a suite of isopropylated triarylphosphate isomers (ITPs); its brominated components include 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB) and bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate (BEH-TEBP). Taken together, these chemicals have been shown to be endocrine disrupting and potentially toxic, and human exposure to them is widespread. In this study, maternal transfer of FM 550 components, and in some cases their metabolites, was investigated in dosed Wistar rats. Gestational and lactational transfer were examined separately, with dams orally exposed to 300 or 1000 µg of FM 550 for 10 consecutive days during gestation (gestational day [GD] 9-18) or lactation (postnatal day [PND] 3-12). Levels of parent compounds were measured in fetus and whole pup tissue homogenates, and in dam and pup serum, and several metabolites were measured in dam and pup urine. EH-TBB body burdens resulting from lactational transfer were approximately 200- to 300-fold higher than those resulting from placental transfer, whereas low levels of BEH-TEBP were transferred during both lactation and gestation. TPHP and ITPs were rapidly metabolized by the dams and were not detected in whole tissue homogenates. However, diphenyl phosphate (DPHP) and mono-isopropylphenyl phenyl phosphate (ip-PPP) were detected in urine from the dosed animals. This study is the first to confirm ip-PPP as a urinary metabolite of ITPs and establish a pharmacokinetic profile of FM 550 in a mammalian model.

KEY WORDS

Firemaster 550 ;: lactational transfer ;: gestational transfer; metabolites; rodent.

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.

Prediction of Drug Transfer into Milk Considering Breast Cancer Resistance Protein (BCRP)-Mediated Transport

PURPOSE

Drug transfer into milk is of concern due to the unnecessary exposure of infants to drugs. Proposed prediction methods for such transfer assume only passive drug diffusion across the mammary epithelium. This study reorganized data from the literature to assess the contribution of carrier-mediated transport to drug transfer into milk, and to improve the predictability thereof.

METHODS

Milk-to-plasma drug concentration ratios (M/Ps) in humans were exhaustively collected from the literature and converted into observed unbound concentration ratios (M/Punbound,obs). The ratios were also predicted based on passive diffusion across the mammary epithelium (M/Punbound,pred). An in vitro transport assay was performed for selected drugs in breast cancer resistance protein (BCRP)-expressing cell monolayers.

RESULTS

M/Punbound,obs and M/Punbound,pred values were compared for 166 drugs. M/Punbound,obs values were 1.5 times or more higher than M/Punbound,pred values for as many as 13 out of 16 known BCRP substrates, reconfirming BCRP as the predominant transporter contributing to secretory transfer of drugs into milk. Predictability of M/P values for selected BCRP substrates and non-substrates was improved by considering in vitro-evaluated BCRP-mediated transport relative to passive diffusion alone.

CONCLUSIONS

The current analysis improved the predictability of drug transfer into milk, particularly for BCRP substrates, based on an exhaustive data overhaul followed by focused in vitro transport experimentation.

Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods

A quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. The six molecular descriptors that were considered to account for the effect of molecular structure on the retention time are number of nitrogen, Solvation connectivity index-chi 1, BalabanYindex, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted. A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. The standard error values of ANN model for training, test, and validation sets are 1.559, 1.517, and 1.249, respectively, which are less than those obtained reveals by multiple linear regressions model (2.402, 1.858, and 2.036, resp.). Results obtained the reliability and good predictability of nonlinear QSRR model to predict the retention time of pesticides.

Toxicokinetic Considerations in Predicting Toxicity

The ability of a compound to elicit a toxic effect within an organism is dependent upon three factors (i) the external exposure of the organism to the toxicant in the environment or via the food chain (ii) the internal uptake of the compound into the organism and its transport to the site of action in sufficient concentration and (iii) the inherent toxicity of the compound. The in silico prediction of toxicity and the role of external exposure have been dealt with in other chapters of this book. This chapter focuses on the importance of ‘internal exposure’ i.e. the absorption, distribution, metabolism and elimination (ADME) properties of compounds which determine their toxicokinetic profile. An introduction to key concepts in toxicokinetics will be provided, along with examples of modelling approaches and software available to predict these properties. A brief introduction will also be given into the theory of physiologically-based toxicokinetic modelling.

Prediction of micelle–water partition coefficient from the theoretical derived molecular descriptors

The micelle-water partition coefficients of 81 organic compounds in SDS solution were predicted by quantitative structure-property relationship method. The multiple linear regression (MLR) and artificial neural network (ANN) techniques were used to build linear and nonlinear model, respectively. In this work the proposed QSPR models, both by MLR and ANN, contain identical descriptors which are zero order of Kier-Hall index, count of Hydrogen donors site [Zefirovs PC], average valency of a C atom, atomic charge weighted by partial positively charged surface area and minimum one electron reaction index for a C atom. The MLR model gave a root mean square (RMS) of 0.166, 0.25, and 0.289 for training, prediction and test sets, respectively, whereas ANN gave an RMS error of 0.06, 0.21, and 0.20 for training, prediction, and test sets, respectively. Comparison the results of these two methods reveals that those obtained by the ANN model are much better.

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.

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.

QSAR modeling of anti-invasive activity of organic compounds using structural descriptors

The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds. The standard deviation ratios for the both training and validation sets are less than unity, indicating a satisfactory predictive capability for classification of the nature of the anti-invasive activity data. The proposed model can be used for the prediction of the anti-invasive activity of novel classes of compounds enabling a virtual screening of large databases of anticancer drugs.

Breast cancer resistance protein (BCRP/ABCG2) transports fluoroquinolone antibiotics and affects their oral availability, pharmacokinetics, and milk secretion

The breast cancer resistance protein (BCRP/ABCG2) is an ATP-binding cassette drug efflux transporter that extrudes xenotoxins from cells in intestine, liver, mammary gland, and other organs, affecting the pharmacological and toxicological behavior of many compounds, including their secretion into the milk. The purpose of this study was to determine whether three widely used fluoroquinolone antibiotics (ciprofloxacin, ofloxacin, and norfloxacin) are substrates of Bcrp1/BCRP and to investigate the possible role of this transporter in the in vivo pharmacokinetic profile of these compounds and their secretion into the milk. Using polarized cell lines, we found that ciprofloxacin, ofloxacin, and norfloxacin are transported by mouse Bcrp1 and human BCRP. In vivo pharmacokinetic studies showed that the ciprofloxacin plasma concentration was more than 2-fold increased in Bcrp1(-/-) compared with wild-type mice (1.77 +/- 0.73 versus 0.85 +/- 0.39 microg/ml, p < 0.01) after oral administration of ciprofloxacin (10 mg/kg). The area under the plasma concentration-time curve in Bcrp1(-/-) mice was 1.5-fold higher than that in wild-type mice (48.63 +/- 5.66 versus 33.10 +/- 4.68 min x microg/ml, p < 0.05) after i.v. administration (10 mg/kg). The milk concentration and milk/plasma ratio of ciprofloxacin were 2-fold higher in wild-type than in Bcrp1(-/-) lactating mice. We conclude that Bcrp1 is one of the determinants for the bioavailability of fluoroquinolones and their secretion into the milk.

QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor

This work is devoted to the development of quantitative structure-activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log 1/IC50. The former model can be used for physico-chemical interpretation, while the latter possesses a superior predictive ability.

Excretion pattern of co-planar and non-planar tetra- and hexa-chlorobiphenyls in ovine milk and faeces

This study employed the gas chromatography with electron capture detection to determine residual levels and excretion patterns of two pairs of structurally diverse polychlorinated biphenyl (PCB) congeners (IUPAC Nos. 54, 80, 155, and 169) administered to lactating sheep by intramuscular injection. PCB levels and excretion patterns in blood, milk, and faeces were time-dependent and differed from the composition of PCB congeners administered. Lactational transfer substantially exceeded the faecal transfer. Between days 3 and 7, the amount of PCB congeners 54 and 169 excreted in milk was around 50- and 800-fold higher than the amount of these two congeners excreted via faeces. During the same period, the relative contribution of co-planar PCB congeners (80 and 169) in PCB pattern decreased in blood and increased in milk and faeces compared with non-planar PCBs (54 and 155). On day 3, the ratio PCB 169 to 54 was 7-fold higher in milk than in faeces. PCB congeners with log Kow values under 6.5 reached peaks of their excretion in milk within the first three days after administration, while the super-lipophilic PCB 169 congener with log Kow value of over 7 has not reached the plateau until day 10, but afterwards, its level remained relatively high throughout the observation period. During the 57-day follow-up period, the excretion of PCB 80, 155, and 169 in milk was 4.5-, 14-, and 46-fold greater compared with PCB 54. Differences in levels and patterns were explained with some physico-chemical properties of individual PCB congeners, such as lipophilicity, planarity, metabolic stability, sorption/diffusion properties.

Simultaneous non-destructive determination of two components of combined paracetamol and amantadine hydrochloride in tablets and powder by NIR spectroscopy and artificial neural networks

The two components (paracetamol and amantadine hydrochloride) were simultaneously determined in combined paracetamol and amantadine hydrochloride tablets and powder by using near-infrared (NIR) spectroscopy and artificial neural networks (ANNs). The ANN models of three pretreated spectra (first-derivative, second-derivative and standard normal variate (SNV), respectively) were established. The mathematical corrected models of tablets were compared with those of the powder. In the models, the concentrations of paracetamol and amantadine hydrochloride as the active components were determined simultaneously and compared with the results of their separate determination. The parameters that affected the network were studied and the concentrations of the test set samples were predicted. The degree of approximation, a new evaluation criterion of the network was employed to prove the accuracy of the predicted results.

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.

Measurement of radioactivity in breast milk following 99mTc-Leukoscan injection

BACKGROUND

There are no published data on the excretion of radioactivity into breast milk following the administration of 99mTc sulesomab (Leukoscan). We report the results of measurements made on breast milk samples from one patient following Leukoscan injection.

RESULTS

The activity concentration in the samples decayed mono-exponentially with an effective half-life of 3.1 h. Based on data from this patient, the interruption period required to reduce the radiation dose to the infant to less than 1 mSv following an administration of 750 MBq 99mTc-leukoscan would be 10 h.

CONCLUSION

This information is useful as a guide to the likely period of interruption required when counselling a breastfeeding patient prior to a Leukoscan study.