Can we Predict Drug Excretion into Saliva? A Systematic Review and Analysis of Physicochemical Properties

BACKGROUND AND OBJECTIVES

Saliva is a patient-friendly matrix for therapeutic drug monitoring (TDM) but is infrequently used in routine care. This is due to the uncertainty of saliva-based TDM results to inform dosing. This study aimed to retrieve data on saliva-plasma concentration and subsequently determine the physicochemical properties that influence the excretion of drugs into saliva to increase the foundational knowledge underpinning saliva-based TDM.

METHODS

Medline, Web of Science and Embase (1974-2023) were searched for human clinical studies, which determined drug pharmacokinetics in both saliva and plasma. Studies with at least ten subjects and five paired saliva-plasma concentrations per subject were included. For each study, the ratio of the area under the concentration-time curve between saliva and plasma was determined to assess excretion into saliva. Physicochemical properties of each drug (e.g. pKa, lipophilicity, molecular weight, polar surface area, rotatable bonds and fraction of drug unbound to plasma proteins) were obtained from PubChem and Drugbank. Drugs were categorised by their ionisability, after which saliva-to-plasma ratios were predicted with adjustment for protein binding and physiological pH via the Henderson-Hasselbalch equation. Spearman correlation analyses were performed for each drug category to identify factors predicting saliva excretion (α = 5%). Study quality was assessed by the risk of bias in non-randomised studies of interventions tool.

RESULTS

Overall, 42 studies including 40 drugs (anti-psychotics, anti-microbials, immunosuppressants, anti-thrombotic, anti-cancer and cardiac drugs) were included. The median saliva-to-plasma ratios were similar for drugs in the amphoteric (0.59), basic (0.43) and acidic (0.41) groups and lowest for drugs in the neutral group (0.21). Higher excretion of acidic drugs (n = 5) into saliva was associated with lower ionisation and protein binding (correlation between predicted versus observed saliva-to-plasma ratios: R2 = 0.85, p = 0.02). For basic drugs (n = 21), pKa predicted saliva excretion (Spearman correlation coefficient: R = 0.53, p = 0.02). For amphoteric drugs (n = 10), hydrogen bond donor (R = - 0.76, p = 0.01) and polar surface area (R = - 0.69, p = 0.02) were predictors. For neutral drugs (n = 10), protein binding (R = 0.84, p = 0.004), lipophilicity (R = - 0.65, p = 0.04) and hydrogen bond donor count (R = - 0.68, p = 0.03) were predictors. Drugs considered potentially suitable for saliva-based TDM are phenytoin, tacrolimus, voriconazole and lamotrigine. The studies had a low-to-moderate risk of bias.

CONCLUSIONS

Many commonly used drugs are excreted into saliva, which can be partly predicted by a drug's ionisation state, protein binding, lipophilicity, hydrogen bond donor count and polar surface area. The contribution of drug transporters and physiological factors to the excretion needs to be evaluated. Continued research on drugs potentially suitable for saliva-based TDM will aid in adopting this person-centred TDM approach to improve patient outcomes.

Determining the exposure of maternal medicines through breastfeeding: the UmbrelLACT study protocol-a contribution from the ConcePTION project

INTRODUCTION

Breastfeeding is beneficial for the health of the mother and child. However, at least 50% of postpartum women need pharmacotherapy, and this number is rising due to the increasing prevalence of chronic diseases and pregnancies at a later age. Making informed decisions on medicine use while breastfeeding is often challenging, considering the extensive information gap on medicine exposure and safety during lactation. This can result in the unnecessary cessation of breastfeeding, the avoidance of pharmacotherapy or the off-label use of medicines. The UmbrelLACT study aims to collect data on human milk transfer of maternal medicines, child exposure and general health outcomes. Additionally, the predictive performance of lactation and paediatric physiologically based pharmacokinetic (PBPK) models, a promising tool to predict medicine exposure in special populations, will be evaluated.

METHODS AND ANALYSIS

Each year, we expect to recruit 5-15 breastfeeding mothers using pharmacotherapy via the University Hospitals Leuven, the BELpREG project (pregnancy registry in Belgium) or external health facilities. Each request and compound will be evaluated on relevance (ie, added value to available scientific evidence) and feasibility (including access to analytical assays). Participants will be requested to complete at least one questionnaire on maternal and child's general health and collect human milk samples over 24 hours. Optionally, two maternal and one child's blood samples can be collected. The maternal medicine concentration in human milk will be determined along with the estimation of the medicine intake (eg, daily infant dose and relative infant dose) and systemic exposure of the breastfed child. The predictive performance of PBPK models will be assessed by comparing the observed concentrations in human milk and plasma to the PBPK predictions.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee Research UZ/KU Leuven (internal study number S67204). Results will be published in peer-reviewed journals and presented at (inter)national scientific meetings.

TRIAL REGISTRATION NUMBER

NCT06042803.

Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

Generic Workflow to Predict Medicine Concentrations in Human Milk Using Physiologically-Based Pharmacokinetic (PBPK) Modelling-A Contribution from the ConcePTION Project

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for "non-lactating" adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (C_max) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage.

Inflammatory bowel disease in pregnancy and breastfeeding

Inflammatory bowel disease (IBD) has a peak age of diagnosis before the age of 35 years. Concerns about infertility, adverse pregnancy outcomes, and heritability of IBD have influenced decision-making for patients of childbearing age and their care providers. The interplay between the complex physiology in pregnancy and IBD can affect placental development, microbiome composition and responses to therapy. Current evidence has shown that effective disease management, including pre-conception counselling, multidisciplinary care and therapeutic agents to minimize disease activity, can improve pregnancy outcomes. This Review outlines the management of IBD in pregnancy and the safety of IBD therapies, including novel agents, with regard to both maternal and fetal health. The vast majority of IBD therapies can be used with low risk during pregnancy and lactation without substantial effects on neonatal outcomes.

Physiologically based pharmacokinetic model to predict drug concentrations of breast cancer resistance protein substrates in milk

Many mothers need to take some medications during breastfeeding, which may carry a risk to breastfed infants. Thus, determining the amount of a drug transferred into breast milk is critical for risk-benefit analysis of breastfeeding. Breast cancer resistance protein (BCRP), an efflux transporter which usually protects the body from environmental and dietary toxins, was reported to be highly expressed in lactating mammary glands. In this study, we developed a mechanistic lactation physiologically based pharmacokinetic (PBPK) modeling approach incorporating BCRP mediated transport kinetics to simulate the concentration-time profiles of five BCRP drug substrates (acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin) in nursing women's plasma and milk. Due to the lack of certain physiological parameters and scaling factors in nursing women, we combine the bottom up and top down PBPK modeling approaches together with literature reported data to optimize and determine a set of parameters that are applicable for all five drugs. The predictive performance of the PBPK models was assessed by comparing predicted pharmacokinetic profiles and the milk-to-plasma (M/P) ratio with clinically reported data. The predicted M/P ratios for acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin were 2.48, 3.70, 3.55, 1.21, and 5.78, which were all within 1.5-fold of the observed values. These PBPK models are useful to predict the PK profiles of those five drugs in the milk for different dosing regimens. Furthermore, the approach proposed in this study will be applicable to predict pharmacokinetics of other transporter substrates in the milk.

A perspective on the current use of the phase distribution model for predicting milk-to-plasma drug concentration ratio

The phase distribution model, proposed by Atkinson and Begg in 1990, has been widely used for predicting breastmilk-to-plasma drug concentration ratio. However, misrepresentations of the equations have been noted in recent publications. In this perspective, we revisit the derivation of the equations and provide an R/Shiny interface for the model with a view to helping scientists in this field acquire in-depth understanding of the theoretical background and implementation of the model.

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.

Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model

There is a risk of exposure to drugs in neonates during the lactation period due to maternal drug intake. The ability to predict drugs of potential hazards to the neonates would be useful in a clinical setting. This work aimed to evaluate the possibility of integrating milk-to-plasma (M/P) ratio predictive algorithms within the physiologically-based pharmacokinetic (PBPK) approach and to predict milk exposure for compounds with different physicochemical properties. Drug and physiological milk properties were integrated to develop a lactation PBPK model that takes into account the drug ionization, partitioning between the maternal plasma and milk matrices, and drug partitioning between the milk constituents. Infant dose calculations that take into account maternal and milk physiological variability were incorporated in the model. Predicted M/P ratio for acetaminophen, alprazolam, caffeine, and digoxin were 0.83 ± 0.01, 0.45 ± 0.05, 0.70 ± 0.04, and 0.76 ± 0.02, respectively. These ratios were within 1.26-fold of the observed ratios. Assuming a daily milk intake of 150 ml, the predicted relative infant dose (%) for these compounds were 4.0, 6.7, 9.9, and 86, respectively, which correspond to a daily ingestion of 2.0 ± 0.5 mg, 3.7 ± 1.2 µg, 2.1 ± 1.0 mg, and 32 ± 4.0 µg by an infant of 5 kg bodyweight. Integration of the lactation model within the PBPK approach will facilitate and extend the application of PBPK models during drug development in high-throughput screening and in different clinical settings. The model can also be used in designing lactation trials and in the risk assessment of both environmental chemicals and maternally administered drugs.

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.

Non-clinical Models to Determine Drug Passage into Human Breast Milk

BACKGROUND

Successful practice of clinical perinatal pharmacology requires a thorough understanding of the pronounced physiological changes during lactation and how these changes affect various drug disposition processes. In addition, pharmacokinetic processes unique to lactation have remained understudied. Hence, determination of drug disposition mechanisms in lactating women and their babies remains a domain with important knowledge gaps. Indeed, lack of data regarding infant risk during breastfeeding far too often results in discontinuation of breastfeeding and subsequent loss of all the associated benefits to the breastfed infant. In the absence of age-specific toxicity data, human lactation data alone are considered insufficient to rapidly generate the required evidence regarding risks associated with medication use during lactation.

METHODS

Systematic review of literature to summarize state-of-the art non-clinical approaches that have been developed to explore the mechanisms underlying drug milk excretion.

RESULTS

Several studies have reported methods to predict (to some extent) milk drug excretion rates based on physicochemical properties of the compounds. In vitro studies with primary mammary epithelial cells appear excellent approaches to determine transepithelial drug transport rates across the mammary epithelium. Several of these in vitro tools have been characterized in terms of transporter expression and activity as compared to the mammary gland tissue. In addition, with the advent of physiology-based pharmacokinetic (PBPK) modelling, these in vitro transport data may prove instrumental in predicting drug milk concentration time profiles prior to the availability of data from clinical lactation studies. In vivo studies in lactating animals have proven their utility in elucidating the mechanisms underlying drug milk excretion.

CONCLUSION

By combining various non-clinical tools (physicochemistry-based, in vitro and PBPK, in vivo animal) for drug milk excretion, valuable and unique information regarding drug milk concentrations during lactation can be obtained. The recently approved IMI project ConcePTION will address several of the challenges outlined in this review.

Opioids in Breast Milk: Pharmacokinetic Principles and Clinical Implications

Safety of maternal drug therapy during breastfeeding may be assessed from estimated levels of drug exposure of the infant through milk. Pharmacokinetic (PK) principles predict that the lower the clearance is, the higher the infant dose via milk will be. Drugs with low clearance (<1 mL/[kg·min]) are likely to cause an infant exposure level greater than 10% of the weight-adjusted maternal dose even if the milk-to-plasma concentration ratio is 1. Most drugs cause relatively low-level exposure below 10% of the weight-adjusted maternal dose, but opioids require caution because of their potential for severe adverse effects. Furthermore, substantial individual variations of drug clearance exist in both mother and infant, potentially causing drug accumulation over time in some infants even if an estimated dose of the drug through milk is small. Such PK differences among individuals are known not only for codeine and tramadol through pharmacogenetic variants of CYP2D6 but also for non-CYP2D6 substrate opioids including oxycodone, indicating difficulties of eliminating PK uncertainty by simply replacing an opioid with another. Overall, opioid use for pain management during labor and delivery and subsequent short-term use for 2-3 days are compatible with breastfeeding. In contrast, newly initiated and prolonged maternal opioid therapy must follow a close monitoring program of the opioid-naive infants. Until more safety data become available, treatment duration of newly initiated opioids in the postpartum period should be limited to 2-3 days in unsupervised outpatient settings. Opioid addiction treatment with methadone and buprenorphine during pregnancy may continue into breastfeeding, but infant conditions must be monitored.

Emerging Research Paradigm for Infant Drug Exposure Through Breast Milk

Background:

Information on drug secretion into milk is insufficient due to the exclusion of lactating women from clinical trials and drug development processes. As a result, non-adherence to the necessary drug therapy and discontinuation of breastfeeding occur, even if the predicted level of infant exposure is low. In contrast, inadvertent infant exposure to drugs in breast milk continues to happen due to lack of rational risk assessment, resulting in serious toxicity cases including death. This problem is multifactorial, but one of the key elements is the lack of pharmacokinetic information on drug secretion into milk and resultant infant exposure levels, the first line of evidence for risk assessment.

Methods:

Basic PK principles in drug excretion into milk were explained. The literature was scanned to identify approaches for PK data acquisition in this challenging field.

Results:

This review describes the feasibility to develop such approaches, and the knowledge gaps that still exist. A combination of population pharmacokinetics approach (to estimate averages and variations of drug concentration profiles in milk) and physiologically-based pharmacokinetics modeling of infants (to predict the population profiles of infant drug exposure levels) appears useful.

Conclusions:

In order to facilitate participant enrollment and PK data acquisition in a timely manner, networks of investigators become crucial.

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.

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.

Organic cation transporter/solute carrier family 22a is involved in drug transfer into milk in mice

Drug transfer into milk is a general concern during lactation. So far, breast cancer resistance protein (Bcrp) is the only transporter known to be involved in this process, whereas participation of other transporters remains unclear. We investigated the importance of organic cation transporter (Oct) in drug transfer into milk in mice. The mammary glands of lactating versus nonlactating FVB strain mice revealed elevated mRNA levels of Oct1 and Bcrp, whereas Oct2 and Oct3 mRNA levels were decreased. Specific uptake of cimetidine, acyclovir, metformin, and terbutaline was observed in human embryonic kidney 293 cells transfected with murine Oct1 or Oct2. The milk-to-plasma concentration ratio (M/P) values of cimetidine and acyclovir were significantly decreased in Bcrp knockout and Oct1/2 double-knockout (DKO) mice compared with control FVB mice, whereas the M/P values of terbutaline and metformin were significantly decreased in Oct1/2 DKO mice alone. These are the first to suggest that Oct1 might be involved in secretory transfer of substrate drugs into milk.

Tacrolimus placental transfer at delivery and neonatal exposure through breast milk

AIM(S)

The current investigation aims to provide new insights into fetal exposure to tacrolimus in utero by evaluating maternal and umbilical cord blood (venous and arterial), plasma and unbound concentrations at delivery. This study also presents a case report of tacrolimus excretion via breast milk.

METHODS

Maternal and umbilical cord (venous and arterial) samples were obtained at delivery from eight solid organ allograft recipients to measure tacrolimus and metabolite bound and unbound concentrations in blood and plasma. Tacrolimus pharmacokinetics in breast milk were assessed in one subject.

RESULTS

Mean (±SD) tacrolimus concentrations at the time of delivery in umbilical cord venous blood (6.6 ± 1.8 ng ml(-1)) were 71 ± 18% (range 45-99%) of maternal concentrations (9.0 ± 3.4 ng ml(-1)). The mean umbilical cord venous plasma (0.09 ± 0.04 ng ml(-1)) and unbound drug concentrations (0.003 ± 0.001 ng ml(-1)) were approximately one fifth of the respective maternal concentrations. Arterial umbilical cord blood concentrations of tacrolimus were 100 ± 12% of umbilical venous concentrations. In addition, infant exposure to tacrolimus through the breast milk was less than 0.3% of the mother's weight-adjusted dose.

CONCLUSIONS

Differences between maternal and umbilical cord tacrolimus concentrations may be explained in part by placental P-gp function, greater red blood cell partitioning and higher haematocrit levels in venous cord blood. The neonatal drug exposure to tacrolimus via breast milk is very low and likely does not represent a health risk to the breastfeeding infant.

Development of an in vitro cell culture model to study milk to plasma ratios of therapeutic drugs

OBJECTIVE

To create an in vitro cell culture model to predict the M/P (concentration of drug in milk/concentration in maternal plasma) ratios of therapeutic drugs viz. rifampicin, theophylline, paracetamol, and aspirin.

MATERIALS AND METHODS

An in vitro cell culture model using CIT3 cells (mouse mammary epithelial cells) was created by culturing the cells on transwells. The cells formed an integral monolayer, allowing only transcellular transport as it happens in vivo. Functionality of the cells was confirmed through scanning electron microscopy. Time wise transfer of the study drugs from plasma to milk was studied and compared with actual (in vivo) M/P ratios obtained at reported tmax for the respective drugs.

RESULTS

The developed model mimicked two important intrinsic factors of mammary epithelial cells viz. secretory and tight-junction properties and also the passive route of drug transport. The in vitro M/P ratios at reported tmax were 0.23, 0.61, 0.87, and 0.03 respectively, for rifampicin, theophylline, paracetamol, and salicylic acid as compared to 0.29, 0.65, 0.65, and 0.22, respectively, in vitro.

CONCLUSION

Our preliminary effort to develop an in vitro physiological model showed promising results. Transfer rate of the drugs using the developed model compared well with the transfer potential seen in vivo except for salicylic acid, which was transferred in far lower concentration in vitro. The model has a potential to be developed as a non-invasive alternative to the in vitro technique for determining the transfer of therapeutic drugs into breast milk.

Utility of noninvasive biomatrices in pharmacokinetic studies

Blood and plasma are the biomatrices traditionally used for drug monitoring and their pharmacokinetic profiling. Blood is the circulating fluid in contact with all organs and tissues of body and thus is the most representative fluid for measuring systemic drug levels. However, venipuncture suffers from the caveat of being an invasive technique which often makes people reluctant to participate in clinical studies. Thus, there is a need for noninvasive bio-fluids that are ethically appropriate, cost-efficient and toxicologically relevant. These alternate bio-fluids may prove clinically useful as alternatives to plasma/serum in therapeutic drug monitoring, pharmacokinetic and toxicokinetic studies, doping control in sports medicine and to monitor local adverse effects. These may be of particular interest in the case of special population groups such as neonates, children, the elderly, terminally ill patients and pregnant or lactating women, and offer the advantage of circumvention of the demand for specialized personnel for sample collection. This review describes such noninvasive bio-fluids (saliva, sweat, tears and milk) that have been considered for pharmacokinetic drug analysis, emphasizing their sample preparation, its associated difficulties and their correlation with plasma.