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

Selection of a suitable animal model to evaluate secretion of drugs in the human milk: a systematic approach

Lack of data on drug secretion in human milk is a concern for safe use of drugs during postpartum.Clinical studies are often difficult to perform; despite substantial improvements in computational methodologies such as physiologically based pharmacokinetic modelling, there is limited clinical data to validate such models for many drugs.Various factors that are likely to impact milk to plasma ratio were identified. A literature search was performed to gather available data on milk composition, total volume of milk produced per day, milk pH, haematocrit, and renal blood flow and glomerular filtration rate in various animal models.BLAST nucleotide and protein tools were used to evaluate the similarities between humans and animals in the expression and predominance of selected drug transporters, metabolic enzymes, and blood proteins.A multistep analysis of all the potential variables affecting drug secretion was considered to identify most appropriate animal model. The practicality of using the animal in a lab setting was also considered.Donkeys and goats were identified as the most suitable animals for studying drug secretion in milk and future studies should be performed in goats and donkeys to validate the preliminary observations.

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.

Safety of medicines during breastfeeding - from case report to modeling : A contribution from the ConcePTION project

INTRODUCTION

Despite many research efforts, current data on the safety of medicines during breastfeeding are either fragmented or lacking, resulting in restrictive labeling of most medicines. In the absence of pharmacoepidemiologic safety studies, risk estimation for breastfed infants is mainly derived from pharmacokinetic (PK) information on the medicine. This manuscript provides a description and a comparison of the different methodological approaches that can yield reliable information on medicine transfer into human milk and the resulting infant exposure.

AREA COVERED

Currently, most information on medicine transfer in human milk relies on case reports or traditional PK studies, which generate data that can hardly be generalized to the population. Some methodological approaches, such as population PK (popPK) and physiologically-based PK (PBPK) modeling, can be used to provide a more complete characterization of infant medicine exposure through human milk and simulate the most extreme situations, while decreasing the burden of sampling in breastfeeding women.

EXPERT OPINION

PBPK and popPK modeling are promising approaches to fill the gap of knowledge in medicine safety in breastfeeding, as illustrated with our escitalopram example.

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.

Developing an In Vitro to In Vivo Extrapolation (IVIVE) Model to Predict Human Milk-to-Plasma Drug Concentration Ratios

Determining the amount of drug transferred into human milk is critical for benefit-risk analysis of taking medication while breastfeeding. In this study, we developed an in vitro and in vivo extrapolation (IVIVE) model to predict human milk/plasma (M/P) drug concentration ratios. Drug unionized fractions at pH 7.0 (F_ni,7.0) and 7.4 (F_ni,7.4), drug fractions unbound in human plasma (f_up) and milk (f_um), and in vitro cell permeability in both directions (efflux ratio, ER) were incorporated into the IVIVE model. A multiple regression E_max model was chosen to predict f_um from f_up and polar surface area (PSA). A total of 97 drugs with experimental ER from Caco-2 cells were used to test the IVIVE model. The M/P ratios predicted by the IVIVE model had a 1.93-fold geometric mean fold error (GMFE) and 72% of predictions were within two-fold error (Pw2FE), which were superior to the performance of previously reported five models. The IVIVE model showed a reasonable prediction accuracy for passive diffusion drugs (GMFE = 1.71-fold, Pw2FE = 82%, N = 50), BCRP substrates (BCRP: GMFE = 1.91-fold, Pw2FE = 60%, N = 5), and substrates of P-gp and BCRP (GMFE = 1.74-fold, Pw2FE = 75%, N = 8) and a lower prediction performance for P-gp substrates (GMFE = 2.51-fold, Pw2FE = 55%, N = 22). By fitting the observed M/P ratios of 39 P-gp substrates, an optimized ER (1.61) was generated to predict the M/P ratio of P-gp substrates using the developed IVIVE model. Compared with currently available in vitro models, the developed IVIVE model provides a more accurate prediction of the drug M/P ratio, especially for passive diffusion drugs. The model performance is expected to be further improved when more experimental f_um and ER data are available.

Transporters in the Mammary Gland-Contribution to Presence of Nutrients and Drugs into Milk

A large number of nutrients and bioactive ingredients found in milk play an important role in the nourishment of breast-fed infants and dairy consumers. Some of these ingredients include physiologically relevant compounds such as vitamins, peptides, neuroactive compounds and hormones. Conversely, milk may contain substances-drugs, pesticides, carcinogens, environmental pollutants-which have undesirable effects on health. The transfer of these compounds into milk is unavoidably linked to the function of transport proteins. Expression of transporters belonging to the ATP-binding cassette (ABC-) and Solute Carrier (SLC-) superfamilies varies with the lactation stages of the mammary gland. In particular, Organic Anion Transporting Polypeptides 1A2 (OATP1A2) and 2B1 (OATP2B1), Organic Cation Transporter 1 (OCT1), Novel Organic Cation Transporter 1 (OCTN1), Concentrative Nucleoside Transporters 1, 2 and 3 (CNT1, CNT2 and CNT3), Peptide Transporter 2 (PEPT2), Sodium-dependent Vitamin C Transporter 2 (SVCT2), Multidrug Resistance-associated Protein 5 (ABCC5) and Breast Cancer Resistance Protein (ABCG2) are highly induced during lactation. This review will focus on these transporters overexpressed during lactation and their role in the transfer of products into the milk, including both beneficial and harmful compounds. Furthermore, additional factors, such as regulation, polymorphisms or drug-drug interactions will be described.

Inhibitors of Human ABCG2: From Technical Background to Recent Updates With Clinical Implications

The ATP-binding cassette transporter G2 (ABCG2; also known as breast cancer resistance protein, BCRP) has been suggested to be involved in clinical multidrug resistance (MDR) in cancer like other ABC transporters such as ABCB1 (P-glycoprotein). As an efflux pump exhibiting a broad substrate specificity localized on cellular plasma membrane, ABCG2 excretes a variety of endogenous and exogenous substrates including chemotherapeutic agents, such as mitoxantrone and several tyrosine kinase inhibitors. Moreover, in the normal tissues, ABCG2 is expressed on the apical membranes and plays a pivotal role in tissue protection against various xenobiotics. For this reason, ABCG2 is recognized to be an important determinant of the pharmacokinetic characteristics of its substrate drugs. Although the clinical relevance of reversing the ABCG2-mediated MDR has been inconclusive, an appropriate modulation of ABCG2 function during chemotherapy should logically enhance the efficacy of anti-cancer agents by overcoming the MDR phenotype and/or improving their pharmacokinetics. To confirm this possibility, considerable efforts have been devoted to developing ABCG2 inhibitors, although there is no clinically available substance for this purpose. As a clue for addressing this issue, this mini-review provides integrated information covering the technical backgrounds necessary to evaluate the ABCG2 inhibitory effects on the target compounds and a current update on the ABCG2 inhibitors. This essentially includes our recent findings, as we serendipitously identified febuxostat, a well-used agent for hyperuricemia as a strong ABCG2 inhibitor, that possesses some promising potentials. We hope that an overview described here will add value to further studies involving in the multidrug transporters.

Validation and clinical application of a novel LC-MS method for quantification of dolutegravir in breast milk

Aim: A novel, sensitive and reproducible method for quantification of dolutegravir (DTG) in dried breast milk spots (DBMS) was developed and validated for use in clinical studies. Its application enabled measurement of DTG pharmacokinetics in breastfeeding mothers and their infants. Results/methodology: Sample extraction was by liquid-liquid extraction using tert-butyl methy-ether, with DTG-d5 as an internal standard. DTG was eluted on a reverse phase C18 Waters XBridge (3.5 μm: 2.1 × 50 mm) column using a gradient mobile phase consisting of 0.1% formic acid in deionised water or methanol. The assay was validated over a calibration range of 10-4000 ng/ml. Conclusion: Stability, inter and intra-assay variability were acceptable according to FDA and EMA bioanalytical method guidelines. The assay is robust, accurate, precise and can be reliably applied for analysis of clinical samples in trials from low resource settings.

Drugs in Lactation

One impediment to breastfeeding is the lack of information on the use of many drugs during lactation, especially newer ones. The principles of drug passage into breastmilk are well established, but have often not been optimally applied prospectively. Commonly used preclinical rodent models for determining drug excretion into milk are very unreliable because of marked differences in milk composition and transporters compared to those of humans. Measurement of drug concentrations in humans remains the gold standard, but computer modeling is promising. New FDA labeling requirements present an opportunity to apply modeling to preclinical drug development in place of conventional animal testing for drug excretion into breastmilk, which should improve the use of medications in nursing mothers.

Clinical and Molecular Evidence of ABCC11 Protein Expression in Axillary Apocrine Glands of Patients with Axillary Osmidrosis

Accumulating evidence suggests that the risk of axillary osmidrosis is governed by a non-synonymous single nucleotide polymorphism (SNP) 538G>A in human ATP-binding cassette C11 (ABCC11) gene. However, little data are available for the expression of ABCC11 protein in human axillary apocrine glands that produce apocrine sweat—a source of odor from the armpits. To determine the effect of the non-synonymous SNP ABCC11 538G>A (G180R) on the ABCC11 in vivo, we generated transiently ABCC11-expressing transgenic mice with adenovirus vector, and examined the protein levels of each ABCC11 in the mice with immunoblotting using an anti-ABCC11 antibody we have generated in the present study. Furthermore, we examined the expression of ABCC11 protein in human axillary apocrine glands extracted from axillary osmidrosis patients carrying each ABCC11 genotype: 538GG, GA, and AA. Analyses of transiently ABCC11-expressing transgenic mice showed that ABCC11 538G>A diminishes the ABCC11 protein levels in vivo. Consistently, ABCC11 protein was detected in the human axillary apocrine glands of the 538GG homozygote or 538GA heterozygote, not in the 538AA homozygote. These findings would contribute to a better understanding of the molecular basis of axillary osmidrosis.

Identification of Febuxostat as a New Strong ABCG2 Inhibitor: Potential Applications and Risks in Clinical Situations

ATP-binding cassette transporter G2 (ABCG2) is a plasma membrane protein that regulates the pharmacokinetics of a variety of drugs and serum uric acid (SUA) levels in humans. Despite the pharmacological and physiological importance of this transporter, there is no clinically available drug that modulates ABCG2 function. Therefore, to identify such drugs, we investigated the effect of drugs that affect SUA levels on ABCG2 function. This strategy was based on the hypothesis that the changes of SUA levels might caused by interaction with ABCG2 since it is a physiologically important urate transporter. The results of the in vitro screening showed that 10 of 25 drugs investigated strongly inhibited the urate transport activity of ABCG2. Moreover, febuxostat was revealed to be the most promising candidate of all the potential ABCG2 inhibitors based on its potent inhibition at clinical concentrations; the half-maximal inhibitory concentration of febuxostat was lower than its maximum plasma unbound concentrations reported. Indeed, our in vivo study demonstrated that orally administered febuxostat inhibited the intestinal Abcg2 and, thereby, increased the intestinal absorption of an ABCG2 substrate sulfasalazine in wild-type mice, but not in Abcg2 knockout mice. These results suggest that febuxostat might inhibit human ABCG2 at a clinical dose. Furthermore, the results of this study lead to a proposed new application of febuxostat for enhancing the bioavailability of ABCG2 substrate drugs, named febuxostat-boosted therapy, and also imply the potential risk of adverse effects by drug-drug interactions that could occur between febuxostat and ABCG2 substrate drugs.

Regulation of the Axillary Osmidrosis-Associated ABCC11 Protein Stability by N-Linked Glycosylation: Effect of Glucose Condition

ATP-binding cassette C11 (ABCC11) is a plasma membrane protein involved in the transport of a variety of lipophilic anions. ABCC11 wild-type is responsible for the high-secretion phenotypes in human apocrine glands, such as that of wet-type ear wax, and the risk of axillary osmidrosis. We have previously reported that mature ABCC11 is a glycoprotein containing two N-linked glycans at Asn838 and Asn844. However, little is known about the role of N-linked glycosylation in the regulation of ABCC11 protein. In the current study, we investigated the effects of N-linked glycosylation on the protein level and localization of ABCC11 using polarized Madin-Darby canine kidney II cells. When the N-linked glycosylation in ABCC11-expressing cells was chemically inhibited by tunicamycin treatment, the maturation of ABCC11 was suppressed and its protein level was significantly decreased. Immunoblotting analyses demonstrated that the protein level of the N-linked glycosylation-deficient mutant (N838Q and N844Q: Q838/844) was about half of the ABCC11 wild-type level. Further biochemical studies with the Q838/844 mutant showed that this glycosylation-deficient ABCC11 was degraded faster than wild-type probably due to the enhancement of the MG132-sensitive protein degradation pathway. Moreover, the incubation of ABCC11 wild-type-expressing cells in a low-glucose condition decreased mature, glycosylated ABCC11, compared with the high-glucose condition. On the other hand, the protein level of the Q838/844 mutant was not affected by glucose condition. These results suggest that N-linked glycosylation is important for the protein stability of ABCC11, and physiological alteration in glucose may affect the ABCC11 protein level and ABCC11-related phenotypes in humans, such as axillary osmidrosis.

Knockdown of SALL4 inhibits the proliferation and reverses the resistance of MCF-7/ADR cells to doxorubicin hydrochloride

Background

Breast cancer is the most frequent malignancy in women and drug resistance is the major obstacle for its successful chemotherapy. In the present study, we analyzed the involvement of an oncofetal gene, sal-like 4 (SALL4), in the tumor proliferation and drug resistance of human breast cancer.

Results

Our study showed that SALL4 was up-regulated in the drug resistant breast cancer cell line, MCF-7/ADR, compared to the other five cell lines. We established the lentiviral system expressing short hairpin RNA to knockdown SALL4 in MCF-7/ADR cells. Down-regulation of SALL4 inhibited the proliferation of MCF-7/ADR cells and induced the G1 phase arrest in cell cycle, accompanied by an obvious reduction of the expression of cyclinD1 and CDK4. Besides, down-regulating SALL4 can re-sensitize MCF-7/ADR to doxorubicin hydrochloride (ADMh) and had potent synergy with ADMh in MCF-7/ADR cells. Depletion of SALL4 led to a decrease in IC50 for ADMh and an inhibitory effect on the ability to form colonies in MCF-7/ADR cells. With SALL4 knockdown, ADMh accumulation rate of MCF-7/ADR cells was increased, while the expression of BCRP and c-myc was significantly decreased. Furthermore, silencing SALL4 also suppressed the growth of the xenograft tumors and reversed their resistance to ADMh in vivo.

Conclusion

SALL4 knockdown inhibits the growth of the drug resistant breast cancer due to cell cycle arrest and reverses tumor chemo-resistance through down-regulating the membrane transporter, BCPR. Thus, SALL4 has potential as a novel target for the treatment of breast cancer.