Analytical methods for the determination of biomarkers of exposure to phthalates in human urine samples

Accepted Manuscript Title: Analytical methods for the determination of biomarkers of exposure to phthalates in human urine samples Author: A. Ramesh Kumar, P. Sivaperumal PII: DOI: Reference:

S0165-9936(15)00248-4 http://dx.doi.org/doi:10.1016/j.trac.2015.06.008 TRAC 14521

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Trends in Analytical Chemistry

Please cite this article as: A. Ramesh Kumar, P. Sivaperumal, Analytical methods for the determination of biomarkers of exposure to phthalates in human urine samples, Trends in Analytical Chemistry (2015), http://dx.doi.org/doi:10.1016/j.trac.2015.06.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1

Analytical Methods for the Determination of Biomarkers of Exposure to

2

Phthalates in Human Urine Samples

3

A. Ramesh Kumara and P. Sivaperumal

4

National Institute of Occupational Health, Indian Council of Medical Research,

5

Meghaninagar, Ahmedabad. 380 016, India

6

a

7

Email: [email protected] (A. Ramesh Kumar);

8

[email protected] (P. Sivaperumal)

Corresponding author: Tel.: +91 79 22688868; Fax: +91 79 22686110

9 10

Highlights

11 12



We review analytical methods for the quantitation of urinary phthalate metabolites

13



We discuss extraction and clean-up strategies for GC-MS and LC-MS/MS

14



We outline intra- and inter-individual variations of biomarker levels

15



Suggestions for minimizing uncertainties in phthalate metabolite data

16

Abstract

17

This article presents an overview of the analytical methods for the determination of

18

biomarkers of exposure to phthalates in human urine samples. Phthalates are non-persistent

19

chemicals; hence urine is the ideal matrix for biomonitoring besides being non-invasive and

20

simple to collect. Phthalate monoesters and oxidative secondary metabolites are the suitable

21

biomarkers of exposure to short chain and long chain phthalates, respectively. The

22

determination of urinary phthalate metabolites greatly reduces the “phthalate blank problem”

23

which arises due to the ubiquitous presence of this chemical in laboratory atmosphere. We 1

Page 1 of 40

1

discuss sample preparation and analytical methodologies for the determination of urinary

2

phthalate metabolites by GC-MS and LC-MS/MS techniques. Issues on the validity of

3

urinary phthalate metabolite data such as intra-and inter-personal variations, variability in

4

population sub-groups and variability due to time and type of urine sample collection are

5

discussed. Measures to minimize uncertainties associated with urinary phthalate metabolite

6

concentration are suggested.

7

Keywords: Biomarkers of exposure; Gas chromatography- mass spectrometry (GC-MS);

8

Human biomonitoring; Liquid chromatography-tandem mass spectrometry (LC-MS/MS);

9

Oxidative secondary metabolites; Phthalate exposure; Phthalate metabolites; Phthalate

10

monoesters; Urinary metabolite analysis

11

Abbreviations:

12

Dimethyl phthalate (DMP), Diethyl phthalate(DEP), Di-n-butyl phthalate (DnBP), Di

13

isobutyl phthalate (DiBP), Benzylbutyl phthalate (BzBP), Di-2-ethylhexyl phthalate (DEHP),

14

Di-n-pentyl phthalate (DnPP), Dipropylheptyl phthalate (DPHP), Di-n-octyl phthalate

15

(DnOP), Di-isononyl phthalate (DINP), Di-isodecyl phthalate (DIDP), Monomethyl phthalate

16

(MMP), Monoethyl phthalate (MEP), Mono-nbutyl phthalate (MBP), Mono-isobutyl

17

phthalate (MiBP), Monobenzyl phthalate (MBzP), Mono-(2-ethylhexyl) phthalate (MEHP),

18

Mono-(2-ethyl-5-oxohexyl)

19

hydroxyhexyl) phthalate (MEHHP, 5OH-MEHP), Mono-2-ethyl-5-carboxypentyl phthalate

20

(MECPP, 5cx-MEPP), Mono(2-carboxymethylhexyl) phthalate (2cx-MMHP), Mono-n-octyl

21

phthalate(MOP), Mono-isononyl phthalate (MNP), Mono-isodecyl phthalate(MDP), Mono-n-

22

hexyl phthalate (MHxP), Mono-n-heptyl phthalate (MHpP), Mono-carboxy-n-heptyl

23

phthalate (MCHpP), Monocarboxy-isooctyl phthalate (MCOP), Mono-hydroxyisononyl

24

phthalate (MHNP), Mono-oxoisononyl phthalate (MONP), Mono-carboxyisononyl phthalate

phthalate

(MEOHP,

5oxo-MEHP),

mono-(2-ethyl-5-

2

Page 2 of 40

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(MCNP), Mono-2-(propyl-6-hydroxy-heptyl)-phthalate (OH-MPHP), Mono-2-(propyl-6-

2

oxoheptyl)-phthalate

3

MPHxP), mono-(4-methyl-7-hydroxy-octyl) phthalate (7OH-MMeOP), mono-(4-methyl-7-

4

oxo-octyl)phthalate

5

(7carboxy-MMe-HP), N,O-Bis(trimethylsilyl) trifluoroacetamide (BSTFA), N-methyl-N-

6

trimethylsilyl trifluoroacetamide (MSTFA), Trimethyl silyl diazomethane (TMSDM),

7

triethyloxonium tetrafluoroborate (TEOTFB), 1,1,1,3,3,3-hexafluoroisopropanol (HFIP)

8

1. Introduction

9

Phthalic acid esters or phthalates are a group of high production volume industrial chemicals

10

having a myriad of commercial applications. They are used as plasticisers in different

11

polymers, additives in construction materials, clothing, cables, coatings, rubber, paints,

12

packing material, medical devices and personal care products. Since, phthalates are not

13

chemically bound to the polymer chain, it is released into the environment as the product

14

ages. Therefore, phthalates have become ubiquitous environmental contaminants and

15

widespread exposure of the general population to several phthalates has been reported [1].

(oxo-MPHP),

(7oxo-MMeOP),

Mono-2-(propyl-6-carboxy-hexyl)-phthalate

(cx-

mono-(4-methyl-7-carboxy-heptyl)phthalate

16

Phthalates have received considerable attention over recent years due to its proven

17

toxicity in animal studies. Some long chain phthalates viz. DEHP, DiNP, DPHP, DiDP etc.

18

are suspected of causing endocrine disruption in humans [2]. It is widely accepted that

19

unambiguous assessment of human exposure to phthalates can only be achieved by biological

20

monitoring of specific biomarkers. Therefore, human biological monitoring (HBM) is

21

increasingly been applied by various agencies to monitor phthalate exposure among general

22

population (see Fig. 1) [1, 3-4].

23

Analytical capabilities are at the core of HBM studies/programs. Advances in

24

analytical instrumentation together with the development of sensitive multi-analyte methods

25

have allowed the detection and quantitation of exogenous chemicals and their metabolites in 3

Page 3 of 40

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human body fluids at increasingly lower levels (i.e. ng L-1). Analytical methods for the

2

determination of biomarkers of phthalate exposure are based on chromatography coupled to

3

mass spectrometric techniques. The primary objective of this review is to survey various

4

sample preparation and quantitation methods for the determination of biomarkers of exposure

5

to phthalates in human urine samples as reported over the period 2000 to 2014. We also

6

discuss issues on intra- and inter-personal variations in urinary phthalate metabolite data and

7

suggest some measures to minimize these variations for meaningful data interpretation.

8

2. Human Metabolism of Phthalates

9

Knowledge of metabolism, excretion ratios, and elimination kinetics of phthalates in humans

10

are important for selecting appropriate biomarkers and matrix for valid biomonitoring.

11

Phthalates are non-persistent chemicals, hence upon ingestion they are rapidly metabolized

12

by hydrolysis and subsequent oxidation reactions (Phase I). The hydrolyzed monoesters and

13

oxidation products can be glucuronidated (Phase II) and predominantly excreted via urine. In

14

the oxidation reactions, the alkyl side chain of the monoester is transformed to oxidative

15

products (alcohols, ketones and carboxylic acids) by ω, ω-1, β oxidation [5]. The extent of

16

phase I and phase II reactions depend on the alcohol moiety as well as on the physiological

17

characteristics of humans [6]. The relatively polar and short chain phthalates (number of

18

carbon atoms <8 in side chain) such as DMP, DEP, DnBP, DiBP are rapidly hydrolyzed to

19

monoesters and eliminated as free monoester as well as glucuronide conjugates. Elimination

20

half-life of short chain phthalates is about 5-6 h [5]. In contrast, long chain phthalates such as

21

DEHP, DiNP, DPHP metabolize to their monoesters, which are extensively transformed to

22

secondary oxidized metabolites (see Fig. 2) [5]. For long chain phthalates, the elimination

23

occurs in at least two phases. In the first phase, monoesters are predominantly eliminated,

24

followed by the oxidative metabolites in a long second phase. For example, only between 2

4

Page 4 of 40

1

and 7 % of the ingested DEHP is excreted as the monoester with an elimination half-life of

2

about 12 h [7]. Details on urinary excretion ratios of phthalates are available elsewhere [5, 7].

3 4

2.1

5

Before the Centers for Disease Control and Prevention (CDC) proposed a method to measure

6

phthalate monoester metabolites in urine [8], the parent phthalate diesters were measured as

7

biomarkers of exposure in occupationally exposed groups [9]. The measurement of phthalate

8

diesters has certain inherent drawbacks [5] viz. 1) The ubiquitous presence of phthalates in

9

the laboratory environment poses an analytical challenge known as “phthalate blank

10

problem”, which is practically very difficult to control [10]. Phthalates are detected even in

11

the purest laboratory chemicals, solvents, sampling equipments and analytical equipment.

12

These circumstances hamper the reliable quantitation of phthalates in real-life scenarios.

13

2)The presence of esterases/lipases in body fluids have to be deactivated with the addition of

14

acid to avoid hydrolysis of diesters into monoesters, and 3. Phthalates have very short half-

15

life; hence their blood levels are 10-100 times less than that of urinary concentrations.

Biomarkers of Phthalate Exposure

16

The measurement of phthalate metabolites in urine proposed by CDC greatly reduced

17

these analytical challenges and opened a new avenue for exposure assessment of phthalates

18

among general population [11]. Tough metabolites could be detected in other body fluids

19

such as amniotic fluid, breast milk, saliva and seminal plasma, the presence of esterases in

20

these matrices hydrolyse externally contaminated phthalates into their monoesters. Urine

21

samples, generally do not have esterases activity, unless it is cross-contaminated with other

22

matrices. Urine is a relatively abundant matrix, and its collection is, in general, simple and

23

noninvasive.

24

During the initial periods of phthalate exposure assessment, only the phthalate

25

monoester metabolites were measured [8]. However, monoesters may also result from the 5

Page 5 of 40

1

hydrolysis of external phthalate diester contamination due to other lipase activity and several

2

abiotic processes. Further, monoester metabolites of long chain phthalates such as DEHP,

3

DiNP and DPHP undergo extensive oxidation and the oxidised metabolites are eliminated in

4

higher proportions (4-10 fold) than monoesters [5]. These secondary oxidized metabolites are

5

not susceptible to external contamination and their elimination half-lives are higher than that

6

of the corresponding monoesters allowing low-level background exposures to be more easily

7

detected. These characteristics favored their use as more appropriate biomarkers especially

8

for long chain phthalates [11].

9

The monoester and oxidative metabolites are excreted as free (unconjugated) as well

10

as glucuronyl conjugated forms. Generally, polar short chain phthalates do not require the

11

addition of hydrophilic group (i.e. glucuronyl) to increase their water solubility. On the other

12

hand, long chain phthalates are predominantly eliminated as conjugated metabolites [6].

13

Therefore, the conjugated as well as free metabolites are important biomarkers in different

14

population sub-groups and are useful to track exposure patterns.

15 16

3. Analytical Methods

17

The quantitation of biomarkers of exposure to phthalates follows the general workflow of the

18

organic contaminant analysis viz. sample pretreatment, extraction and clean-up, concentration

19

and reconstitution in suitable solvent, and quantitation by chromatography coupled to mass

20

spectrometric techniques.

21

3.1

22

Urinary phthalate metabolites include both free as well as glucuronyl conjugated forms.

23

Identifying and measuring the conjugated species may, however, be challenging. Conjugated

24

standards are not always readily available, and sensitive and accurate analytical methods are

25

required to measure the concentrations of these species at trace levels. CDC has proposed an 6

Sample pretreatment

Page 6 of 40

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alternate approach to measure the total concentration of phthalate metabolites (i.e. free plus

2

conjugated species) after an enzymatic hydrolysis using β-glucuronidase (from Escherichia

3

coli) [8]. This approach has got wide acceptance till date, because β-glucuronidase obtained

4

from Escherichia coli does not show other lipase activity, in addition to its specificity to

5

glucuronyl conjugates. Helix protima based β-glucuronidase is not recommended, due to its

6

lipase and arylsulfatase activity that may lead to hydrolysis of externally contaminated

7

diesters [8]. Acid hydrolysis is not specific to glucuronyl conjugates, hence it could lead to

8

the cleavage of ester bond as well. Some authors preferred to determine both conjugated as

9

well as free metabolites, as the free form of metabolite is the biologically active form [6, 12].

10

Quantitative hydrolysis is achieved by incubation of urine sample (pH 6.7) with β-

11

glucuronidase at 37 °C for 90 min [13]. The deconjugation efficiency is ensured by the

12

addition of 4-methyl umbeliferyl glucuronide and quantifying the surrogate 4-methyl

13

umbeliferone (4-Me-Umb). Addition of acid such as CH3COOH, HCOOH etc. to stop the

14

enzymatic activity is recommended to avoid the hydrolysis of externally contaminated

15

phthalates by lipases if any, found in β-glucuronidase [13]. Also, acidification keeps the –

16

COOH group of metabolite protonated facilitating binding to reversed phase high

17

performance liquid chromatography (RP-HPLC).

18

Dilution of urine samples is generally practiced to reduce sample-to-sample matrix

19

variation that can affect analyte recovery. Direct analysis without any extraction/cleanup

20

popularly called as “dilute and shoot” methods have been reported as a rapid screening tool in

21

dope testing [14, 15]. However, further cleanup/concentration steps are required for the

22

removal of endogenous matrix, which otherwise cause clogging of HPLC column and

23

influence analyte ionization in electrospray ionization (ESI) source. Liquid-liquid extraction

24

(LLE), offline and online solid phase extraction (SPE) methods are generally used. Offline

7

Page 7 of 40

1

methods (LLE and SPE) generally include a protein precipitation step by ACN, methanol or

2

freeze-drying [12].

3

3.2

4

Traditional LLE methods are still used due to its simplicity and cost-effectiveness. LLE is

5

generally followed in gas chromatography-mass spectrometry (GC-MS) methods. The polar

6

functional group of phthalate metabolites should be blocked in order to make the analytes GC

7

amenable. Generally, two stages LLE are required i.e. pre- and post-derivatization [16-17].

8

Generally, urine samples are acidified with HCl to pH < 2 prior to LLE, inorder to keep the –

9

COOH group fully protonated, which in turn facilitates its extraction in non-polar solvents.

10

For pre-derivatization LLE, methyl tert-butyl ether, hexane-ether, hexane-dichloromethane

11

are preferred, whereas for post derivatization LLE, hexane and iso-octane are the solvents of

12

choice [16-20]. Traditional column cleanup using fluorisil and silica have also been used in

13

combination with LLE [20-21]. Derivatization is followed by evaporation of the extract and

14

redissolution in non-polar solvent.

Liquid-liquid extraction methods

15

Notwithstanding its advantages, the inherent demerits of LLE viz. time and solvent

16

consumption, and labor intensiveness make LLE less attractive for large scale biomonitoring

17

studies.

18

3.3

19

SPE is one of the most important sample preparation approach for extraction of phthalate

20

metabolites from urine, with offline and online modes.

21

3.3.1

22

Offline SPE methods mostly utilize reversed phase hydrophobic-hydrophilic balanced (HLB)

23

polymeric sorbents. As the homologous phthalate metabolites show wide range of polarities,

24

HLB sorbents are the choice of most workers [8,15,22-23]. These include SPE phases such as

25

polyamide,

Solid phase extraction methods

Offline SPE

poly[n-vinylpyrrolidone-divinylbenzene(DVB)], 8

methacrylate-DVB

and

Page 8 of 40

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hydroxylated polystyrene-DVB (e.g. OASIS HLB, STRATA XL, Super select HLB, Bond

2

Elut Plexa, ISOLUTE ENV+). The advantages of HLB sorbents are; stability in wide pH

3

range, no silanol interactions and no impact of sorbent drying. Further it retains acidic, basic

4

and neutral analytes. Sorbent particle size of 30-60 µm is preferred, because it allows rapid

5

passage of proteins and other biomolecules of urine [24]. For a typical urine volume of 1.0

6

mL, sorbent mass of 150-200 mg and 5-6 mL capacity cartridges are preferred. Another

7

choice of sorbent is mixed mode SPE phase, both polymeric (e.g. OASIS MAX) and bonded

8

silica (e.g. Bond Elut certify, SimpliQ) with multiple types of retention points. They are

9

useful in the extraction of phthalate metabolites and other multiclass analytes [25-27]. SPE

10

catridges should be chosen carefully, as some brands are reported to contribute high blank

11

levels of phthalate monoesters [8]. This problem could be avoided by utilizing glass SPE

12

catridges [15].

13

Variants of SPE, such as solid phase micro extraction (SPME) has been proposed for

14

phthalate metabolites. A solvent free SPME technique utilizing gas phase on-fibre

15

diazomethane derivatization was proposed by Alzaga et al [28]. Another novel extraction

16

procedure using magnetic SPE (MSPE) using magnetite coated multiwall carbon

17

nanoparticles (MWCNT) was proposed by Rastikari et al. [29]. This procedure offers

18

excellent preconcentration of phthalate metabolites and the LODs are in the range of 0.025-

19

0.050 µg L-1 levels.

20

SPE provides better selectivity and higher recoveries, and uses much less solvents

21

than conventional LLE. Automated SPE manifolds greatly enhances SPE steps, but still

22

evaporation and reconstitution of urine extracts consumes time and solvent. Hence, offline

23

SPE methods become less attractive when sample throughput is a high priority. Further,

24

offline SPE methods produce substantial amount of waste (disposable SPE cartridges).

25

3.3.2

Online SPE methods 9

Page 9 of 40

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Online SPE coupled with HPLC allows for automated, sensitive, and selective bioanalysis.

2

Coupling is done by connecting a small, typically 2–15 mm long and 1–4.6 mm id pre-

3

column, used as SPE column, to a conventional HPLC via a switching valve. The choice of

4

sorbent is a key point in online SPE, because it can control parameters such as selectivity,

5

affinity, and capacity. As a rule of thumb, the sorbent used in the SPE column should be

6

similar with the material packed in the analytical column. The SPE column needs to have

7

weaker absorbency for the analytes of interest than for the analytical column. This assures

8

that during the elution from the SPE column to the analytical column, the analyte band will

9

refocus on the front of the analytical column [24]. To avoid breakthrough effects, the sample

10

should usually be aqueous and loaded on to the SPE with a non-eluting RP solvent (aqueous

11

solvent) [30]. Typical SPE-HPLC column pairs used for phthalate metabolites analysis are

12

C18 bonded silica (Hysphre-C18HD: Intertsil ODS-3), monolithic–bonded silica pairs

13

(Chromolith Flash RP-18e: BETASIL Phenyl) [13, 31-32]. Monolithic SPE columns provide

14

better mass transfer and analyte enrichment than packed columns [32].

15

Another development in online SPE is the use of restricted assess material (RAM),

16

which selectively retain the analytes, while the matrix is flushed into the waste based on size-

17

exclusion mechanism. Koch et. al. [33] were the first to utilize RAM precolumn

18

(LiChrospher RP–ADS) coupled to LC-MS/MS for the online cleanup and determination of

19

monoesters and secondary phthalate metabolites [33-35]. RAM is coupled to HPLC via a

20

switching valve and offers fully automated online enrichment of analytes and sample clean-

21

up. It consist of a hydrophilic and an electroneutral external particle surface (alkyl-diol silica)

22

and a hydrophobic reversed-phase internal surface (C4,C8, or C18), and are specially designed

23

for the direct and repetitive injection of samples. The bimodal properties allow retention of

24

low molecular analytes at the lipophilic pore surface, while macromolecular constituents are 10

Page 10 of 40

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excluded (>15 kDa). After the enrichment and cleanup step, the analyte fraction is transferred

2

by back-flush mode from the precolumn onto an analytical column. All transfer and elution

3

processes and the conditioning of both columns are performed with continuously pumping

4

devices, resulting in a fully automated system and high sample throughput, hence it is well

5

suited for large scale biomonitoring programs [24]. The online SPE require typically 0.1 mL

6

urine, besides less consumption of solvents and minimal sample handling. As the whole urine

7

extract is transferred to HPLC, online methods give better sensitivity compared to offline

8

SPE. The column switching procedure involves a change in flow direction on the RAM phase

9

during analyte transfer leading to sharp analyte peaks. These characteristics make RAM

10

precolumns a promising one for urinary phthalate metabolites analysis.

11

3.3.3 Offline vs online SPE methods

12

Both offline and online SPE methods have their own advantages and disadvantages. One

13

important source affecting SPE extraction efficiency is the variation in salt content of urine

14

samples [8], but stable isotope internal standards correct such variations. Both the methods

15

give comparable LODs, accuracy and reproduciability for phthalate metabolites, as the

16

sensitivity enhancement observed in online methods is compensated by the larger sample

17

volume used in offline methods (1.0-2.0 mL). For on-line SPE, sample handling is minimized

18

and, therefore, operator exposure to hazardous chemicals and errors occurring because of

19

manual sample handling are absent. Addition of β-glucuronidase, spiking internal standards

20

and dilution can be done online using customized HPLC autosampler program [32].

21

Additionally, the use of on-line SPE does not involve the evaporation and reconstitution of

22

the urine extract and, therefore, analyte losses by evaporation are eliminated. On-line SPE is

23

also better suited to large scale biomonitoring studies, which require high sample throughput.

11

Page 11 of 40

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The drawbacks of online SPE include the possibility of cross contamination and

2

column overloading, as the analyte may present in wide magnitude concentration ranges in

3

unknown samples. To overcome this, separate binary pumps for SPE and HPLC, and

4

efficient cleaning are required [24].

5

4.

Quantitation strategies

6

4.1

GC-MS methods

7

Direct analysis of phthalate metabolites by GC-MS is not feasible due to polar and low

8

volatile nature of the analytes. Blocking the -COOH functional group by derivatization is the

9

commonly used strategy for GC-MS methods. Common derivatizating agents used are

10

BSTFA, MSTFA (silylation) [18-20,29], HFIP (alkoxylation) [16], TEOTFB (ethylation)

11

[17], diazomethane and TMSDM (methylation) [21, 22]. Electron impact ionization (EI) is

12

the most commonly used ionization technique in GC-MS methods (Table 1), whereas

13

negative chemical ionization (NCI) is preferred in GC-HRMS (magnetic sector) [16]. Non-

14

polar columns (e.g. DB-5, Ultra-2, HP- 5 MS, VF-5 MS) are predominantly used for

15

separation. Intermediate polar column (e.g. Rxi 17) finds useful for separating secondary

16

metabolites of DPHP [16]. Most of the methods have used quadrupole mass spectrometer

17

with isotopically labelled analytes as internal standards for quantitation using selected ion

18

monitoring (SIM) mode. The LOQs reported are in the sub-µg L-1 to <5 µg L-1 range for

19

different phthalate metabolites (Table 1). Lower LOQs are achievable with other mass

20

analyzers such as high resolution (magnetic sector), and quadrapole ion trap (QIT) mass

21

spectrometers [16, 22]. GC-HRMS is particularly useful for the quantitation of isomeric

22

metabolites of DPHP and DIDP/DINP, which are otherwise not resolvable by low resolution

23

mass analysers [16].

24

The advantages of GC-MS based methods are its availability in most laboratories,

25

simplicity, robustness, reproducibility and freedom from ion suppression effects. On the other 12

Page 12 of 40

1

hand, though most of the derivatization reactions are very specific, it works well only with

2

water-free extracts. In addition, derivatization procedures are laborious, and handling of

3

diazomethane, a highly toxic and potentially explosive compound requires special care.

4

Further, the longer run times make GC-MS methods less attractive for large scale

5

biomonitoring studies.

6 7

4.2

LC-MS/MS methods

8

LC coupled to triple quadrupole mass spectrometer (MS/MS) has been the standard

9

workhorse in the quantitation of small molecules such as phthalate metabolites, as it offers

10

good sensitivity, reproducibility, and a broad dynamic range [37]. It is typically used in

11

multiple reaction monitoring (MRM) mode, where the collision energy and product ion mass-

12

to-charge ratio are pre-optimized for each analyte of interest to give the best signal. ESI in

13

negative mode is the most frequently used ionization technique for phthalate metabolites. ESI

14

is preferred to CI, as metabolites having two –COOH groups do not ionize appreciably in CI

15

[38]. LOQs of the reported methods generally range from sub-µg L-1 to < 3 µg L-1 levels for

16

different metabolites. Most of the researchers determined the metabolites MMP, MEP, MBP,

17

MiBP, MBzP and MEHP, MEHHP and MEOHP (Table 2). Among all phthalates,

18

determination of metabolites of DEHP was of interest to several researchers and methods

19

have been proposed to determine up to 8 DEHP metabolites [35,39-40].

20

CDC has published a series of five analytical methods, each method is an improved

21

version of its previous one in terms of LOD, number of analytes and degree of automation

22

[6,13,32,38,42]. Betasil phenyl analytical column was used in all the methods, probably due

23

to its enhanced sensitivity for polar analytes. Its superior performance such as better peak

24

shape and peak separation for phthalate metabolites were reported by other workers as well

25

[25]. The first method reported in the year 2000 determined 8 phthalate monoesters using 13

Page 13 of 40

1

manual SPE and APCI-LC-MS/MS for quantitation [8]. Isomeric pair of MEHP/MOP was

2

adequately resolved, but the structural isomeric pair of MBP/MiBP could not be resolved.

3

Similar problem has been reported by others workers as well [31,33]. This issue was rectified

4

by changing column dimensions, reducing mobile phase flow rate, changing mobile phase

5

gradient and increasing run time [38]. With further modifications in column dimensions and

6

adopting a novel mobile phase gradient, separation and quantitation of up 22 metabolites

7

including three isomeric pairs viz. MBP/MiBP, MEHP/MOP and MECPP/MCHpP were

8

achieved [13]. This is the maximum number of phthalate metabolites determined so far in a

9

single run. Cross validation of all the methods showed that the analytical methodology is

10

rugged, specific and accurate [43].

11

Matrix effects are one of the major problem affecting ESI efficiency especially, in

12

multiclass analytical methods. Because, optimal conditions for extraction and cleanup are

13

needed for wide variety of analytes, including phthalate metabolites [26]. Methods developed

14

exclusively for phthalate metabolites report signal enhancement (2-4 fold) for the fast eluting

15

MEP and MBP, as well as complete signal suppression [8]. Dilution of urine samples and/or

16

injection of low sample volume (i.e. 5 µL) could minimize such problems. Phthalates may

17

originate from the HPLC components and interfere with analyte. Herrero et al. used a delay

18

column placed before the injection valve to remove such interferences [22]. Most of these

19

interferences are not of a major issue, because, quantitation is done by stable isotope labelled

20

internal standard method.

21

Recently, UHPLC-MS/MS has been reported for the quantification of phthalate

22

metabolites with substantial reduction in run time and solvent consumption [22,15,26-27].

23

UHPLC is advantageous, especially, for the determination of multiclass analytes [26-27], as

24

well as in “dilute and shoot methods” due to its lesser run time [15,40]. Holms et al.

25

demonstrated capillary packed column for the online cleanup and separation of phthalate 14

Page 14 of 40

1

metabolites. This system is suitable for large volume injection (e.g. 200 µL) and offers better

2

performance characteristics in combination with QIT mass analyser [41].

3

Quantitation of metabolites of long chain phthalates such as commercial DIDP,

4

DPHP and DiNP is more challenging, because of the complex mixture of alkyl chain isomers

5

and the greater number of homologues metabolites. Separation and quantification of these

6

isomeric metabolites are rather difficult due to the low resolving power of LC-MS/MS.

7

Consequently, quantification is done by peak integration i.e. summing the isomeric

8

compounds with identical MRMs eluting in the same time window [34]. Koch et al. used a

9

novel approach to find the major alkyl side chain of the commercial DINP mixtures [34]. The

10

authors analyzed the commercial isononyl alcohols (INA) used for the production of DINP

11

by GC-MS and identified 4-methyloctanol-1 to be the major INA side chain. With the

12

analogy of major DEHP secondary metabolites, the authors postulated 7OH-MMeOP, 7oxo-

13

MMeOP and 7carboxy-MMe-HP as the major secondary metabolites of DINP. Using these

14

three synthesized standards, the authors quantified all oxidised DINP isomers with hydroxy

15

(OH-MINP), oxo (oxo-MINP) and carboxy (carboxy-MINP) functional groups. This

16

procedure allows the reliable determination of the three oxidised secondary metabolites of

17

DINP [34]. Subsequent biomonitoring studies have focussed on the determination of these

18

secondary metabolites of DINP as the more specific biomarkers than the monoester MINP

19

[44].

20

4.3 Quality Assurance

21

Strict collection, handling and storage protocols are necessary to preserve sample integrity

22

and state-of-the art analytical facility, qualified and experienced personal are required to

23

produce reliable data. Urinary phthalate metabolites are chemically stable for several years if

24

preserved at -70 °C [45]; hence problems related to analyte instability is not likely to arise. 15

Page 15 of 40

1

Frequent freeze-thaw cycles could be avoided by aliquoting the sample into several parts. It is

2

commonly assumed that commercial certified calibration standards are accurate. However,

3

significant inaccuracies have been reported while comparing certified standards of phthalate

4

metabolites [46]. Participation in proficiency testing (PT) programs (e.g. German external

5

quality assessment scheme G-EQUAS) [47] would improve the comparability of data, and we

6

hope several such programs for phthalate metabolites would be made available in the near

7

future. The recent availability of certified reference materials i.e. NIST SRMs 3672/3673

8

would be useful as quality control material for phthalate metabolite analysis [48].

9

5.

Occurrence in human urine samples

10

During the recent decade, increasing interest towards research on human exposure to

11

phthalates is observed [2,12,18,40]. Urinary phthalate metabolites are being monitored in

12

national biomonitoring programs of several countries and the results have shown the presence

13

of several metabolites in nearly all samples. Table 3 shows typical concentration ranges of

14

phthalate metabolites among general population/sub-groups and the results reveal widespread

15

exposure. In some cases, the exposure is above the acceptable daily intake (ADI) values [50].

16

Considering the ubiquitous presence of this class of chemical, phthalates could be referred as

17

“non-persistent, but persistently present” chemical.

18

6.

19

Urinary phthalate metabolite data represent an integrated measure of exposure from all

20

sources and routes and the usefulness of the data strongly depends on our ability to interpret

21

and draw meaningful conclusions. In general, biomarker concentrations in spot urine samples

22

are interpreted as direct surrogates for exposure levels and the variation in measured levels is

23

linked to the magnitude and variation of external exposure. Thus, reverse dosimetry

24

approaches have been used to estimate the daily intake (DI) values and compared to reference

Factors influencing the interpretation of urinary phthalate metabolite data

16

Page 16 of 40

1

dose (RfD) for risk assessment [12,55]. However, there are several factors that contribute to

2

variation in the measured concentrations. Broadly, these can be categorized into 1) variations

3

related to timing of sample collection relative to exposure events, 2) variations related to

4

physiological characteristics of urine and 3) variations related to toxicokinetics of phthalates.

5

We provide a brief overview of these issues here, and we refer the readers to the cited articles

6

for more details [64-66].

7

Phthalate metabolites are mostly measured in spot urine samples with an implicit

8

assumption that the measured concentration in a single urinary aliquot is a reasonable

9

surrogate for 24-h average urinary concentration. This assumption holds good only if the

10

exposure is consistent and the chemical has long elimination half life [64]. However,

11

phthalates have very short half-lives (< 12 hrs) relative to intervals between exposures, and

12

substantial intra-individual, within-day variation in biomarker concentration have been

13

demonstrated. For example, higher proportion of primary metabolite i.e. MEHP was reported

14

in hospitalized pregnant women due to recent exposure to DEHP from medical devices [54],

15

in contrast to the higher proportion of oxidised secondary metabolites observed in general

16

population [1].

17

Variation in urinary flow rate due to fluid intake may result in substantial variations in

18

the biomarker concentration, besides age and gender influences. Creatinine correction has

19

been used widely in biomonitoring studies as a method to adjust for variations in hydration

20

status [67]. In this approach, the measured biomarker concentration (µg L-1) is divided by the

21

measured creatinine concentration (g L-1) of the sample, resulting in a measure of mass of

22

analyte per mass of creatinine (µg g-1). This approach is based on the fairly constant

23

creatinine excretion rate. However, urinary creatinine concentrations are influenced by age,

24

gender, physical activity, body mass index and race/ethnicity [68]. Therefore, both creatinine

25

corrected and uncorrected values should be reported and metabolite data derived from similar 17

Page 17 of 40

1

demographic groups should be compared i.e. children with children, adults with adults.

2

Alternatively, correction by urine specific gravity has been reported [69], but it is not as

3

widely used as creatinine correction.

4

The pharmacokinetic processes i.e. absorption, distribution, metabolism and

5

elimination of phthalates depend both on the nature of parent diester (Sec.2) as well as on the

6

physiological characteristics of the individual. It is well documented that variability is

7

biomarker specific. For example, some of the phthalate metabolites viz. MEP, MBP, MBzP

8

are less susceptible to intra- and inter-individual variations [53,69-70]. Some sections of

9

population-sub groups such as new born babies, children and pregnant women have

10

remarkably different phthalate metabolite elimination pattern compared to adults. For e.g.

11

newborns eliminate higher proportion of carboxy secondary metabolites of long chain

12

phthalates (DEHP, DiDP, DiDP) than their mothers [71]; ratio of MEHP to sum of DEHP

13

metabolites increase with age among children and teenagers [19]; variation of phthalate

14

metabolite concentrations during pregnancy is high than during other periods [69,72].

15

6.1

16

Validity of urinary phthalate metabolite data strongly depends on our ability to

17

minimize/compensate for the various factors that contribute to intra- and inter-personal

18

variations. Based on the published literature, we suggest some measures to minimize these

19

variations.

20

Selection of appropriate biomarkers: For long side chain phthalates viz. DEHP, DINP and

21

DIDP, the secondary oxidative metabolites are better biomarkers of exposure, than the

22

monoester metabolites and are more suitable to capture average background exposure [73-74]

23

(cf. Sec. 2.1). Therefore, while comparing population exposure to long side chain phthalates,

24

oxidative metabolite levels have to be compared. The same level of monoester metabolites of

25

short and long chain phthalates do not mean the same level of exposure, because urinary 18

Measures to minimize variation in biomarker concentration

Page 18 of 40

1

excretion of monoester metabolites of long chain and short chain phthalates vary

2

considerably i.e. 7 % for MEHP vs. ≈ 70 % for MMP, MEP, MBP and MBzP [5]. On the

3

other hand, MEHP would be a better biomarker of recent DEHP exposure [54].

4

Collection of spot/multiple spot/first morning/24-hr urine samples:

5

Phthalates are non-persistent chemicals with biological elimination half life vary between 6

6

and 24 h. Therefore, the timing of urine collection relative to exposure events and its

7

frequency are the crucial factors contributing to the temporal variation of biomarker

8

concentrations [65]. Frequent and constant sources of exposure result in less variation and

9

infrequent exposures lead to large variations. Comparison of phthalate metabolite data in

10

spot, first morning and 24 h urine samples of the same subjects showed moderate intraclass

11

correlation coefficients (ICC; the ratio of between subject variation to total variance) (≈0.4-

12

0.7) for MEP, MBP and MBzP, which implies the contribution of between-subject variation

13

to total variance is more than the within subject variation [66,74-75]. This indicates, the

14

exposure to these phthalates is constant and frequent. In such cases, single urinary sample

15

collected over a specific time period of the day may be sufficient [70]. Similarly, the

16

variability associated with large scale biomonitoring studies and long term exposure studies is

17

less, hence, spot urine samples are adequate [57,66,75]. However, caution should be

18

exercised while calculating DI values based on spot urinary phthalate metabolite

19

concentrations [66].

20

On the other hand, the metabolites of DEHP, DiDP and DINP showed low ICC

21

(<0.4), indicating within subject variation contribute more to the total variance. This results

22

from infrequent exposure e.g. diet [76]. Low ICC implies more statistical power needed to

23

detect differences between population sub-groups. With-in subject variation could be

24

minimized by collecting multiple urine samples, preferably at different times of the day [76]. 19

Page 19 of 40

1

Population sub-groups susceptible to within-subject variation are pregnant women, new

2

borns, children, and persons with varying life style habits [72,76-77]. For studies aimed at

3

calculation of DI estimates and risk assessment, first morning void or 24-hr urine samples

4

would be more appropriate [66].

5

Collection of additional information on samples: Recording more information about time and

6

volume of urine collection and previous void, lifestyle activities (e.g., diet, personal hygiene

7

practices), identifying the sources and types of exposures (e.g., intermittent vs. continuous) in

8

highly exposed and vulnerable groups i.e. children, pregnant women, hospitalized persons,

9

occupationally exposed etc. would help in the identification of sources and variability and

10

proper interpretation.

11

Statistical methods: Computation methods such as normalization based on regression

12

residuals have been proposed to minimize intra- and inter-individual variations in urinary

13

phthalate metabolite concentrations [78].

14

7.

15

Urinary phthalate metabolite analysis is a reliable tool to assess human exposure to

16

phthalates. As interest grows in the urinary biomonitoring of phthalate metabolites, demands

17

increase for reliable and sensitive analytical methods for quantitation. The last decade

18

witnessed the development of sensitive analytical methods for quantification of urinary

19

biomarkers. Sample preparation is dominated by both offline and online SPE methods,

20

whereas LC-MS/MS is the preferred technique for quantitation. Online SPE offers high

21

throughputs and unattended operation; hence, it is best suited for population based

22

biomonitoring studies.

23 24

Conclusions

Future research need to be focussed on identification and quantitation of suitable oxidative biomarkers of exposure to isomeric high molecular weight phthalates.

We predict

20

Page 20 of 40

1

that in the near future more nationally representative biomonitoring studies will be conducted

2

to assess human exposure to this important class of chemical. Analytical chemistry continues

3

to play a pivotal role in these studies.

4

Understanding of the sources and factors that contribute to variability and uncertainty

5

of biomarker levels can increase the value of the data. Some of the sources of variability can

6

be minimized/compensated with proper study designs and collecting additional information.

7

Finally, interpreting biomonitoring data with public health outcome requires more

8

epidemiological studies and the field users of biomarkers should realize the variability of the

9

measurement and use their professional judgement to interpret the results accordingly.

10 11

Acknowledgements

12

The authors thank the Director, National Institute of Occupational Health, Ahmedabad for

13

granting permission to publish this article. The authors also thank the anonymous reviewers

14

for their constructive comments.

15 16 17 18 19 20 21 22 23 24

21

Page 21 of 40

1 2 3 4 5 6 7 8

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and during pregnancy, Environ. Health Perspect. 120 (2012) 739–745.

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urinary phthalate

metabolite

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variability, J. Exposure Sci. Environ. Epidemiol. 25 (2015) 231-239.

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morning, and 24-h urine samples: Considerations for epidemiological studies, Environ.

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Sci. Technol. 47(2012) 958-967.

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week in the urinary concentrations of metabolites of diethyl phthalate and di (2-

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ethylhexyl) phthalate among eight adults: an observational study, Environ. Health

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Perspect. 118 (2010) 1748-1754.

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S.L. Teitelbaum J.A. Britton, A.M. Calafat, X. Ye, M.J. Silva and J.A. Reidy, M.P.

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Galvez, B.L. Brenner, M.S. Wolff, Temporal variability in urinary concentrations of

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States, Environ. Res. 106 (2008) 257-269. 32

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M. Mortamais, C. Chevrier, C. Philippat, C. Petit, A.M. Calafat, X. Ye, R. Slama,

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Correcting for the influence of sampling conditions on biomarkers of exposure to phenols

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and phthalates: a 2-step standardization method based on regression residuals, Environ.

4

Health, 11 (2012) 1-14.

5 6 7

Figure captions

8 9

Figure 1. General scheme of human biomonitoring of phthalates by urinary metabolites

10

analysis

11

Figure 2. Metabolism of long chain phthalates (e.g. DEHP)

33

Page 33 of 40

1 2

Table 1. Selected GC-MS analytical methods for the determination of biomarkers of phthalate exposure in human urine samples

3 Analytes

Pre-treatment/Extraction/Clean up

Analytical System

Analytical Column

Analytical performance characteristics

Ref.

OH-MPHP, oxo-MPHP, cx-MPHxP

ED, LLE with MTBE and derivatization with HFIP + DIC(catalyst). LLE with IOA

GC-HRMS NCI

Fused silica Rxi 17 (30 m × 0.25 mm× 0.25 µm) (60 m × 0.25 mm× 0.25 µm)

LOD: 0.05-0.1 µg/L Recovery: 98-106 % RSD: 1.3-7.4 %

[16]

MEHP, MEOHP, MEHHP MECPP

ED, LLE with diethyl ether, derivatization with TEOTFB in DCM. LLE with hexane.

GC(EI)-SIM-MS

DB-5 (30 m× 0.32 mm× 0.25 µm)

LOD: 13-25 µg/L Recovery: RSD: 6-16%

[17]

MEP, MiBp, MnBP, MBzP, MiNP, MEHP, MEOHP, MEHHP MEHP, 5-OH-MEHP, 5-oxo-MEHP

ED, LE with hexane:ether, derivatization with TMCS+BSTFA

GC(EI)-SIM-MS

Ultra-2 capillary column (25 m× 0.2 mm× 0.33 µm)

LOD: 0.05-0.2 µg/L Recovery: 61.6-100 % RSD: 2.1-16.32 %

[18]

ED, LLE with hexane:ether, derivatization with TMCS+BSTFA

GC(EI)-SIM-MS

Ultra-2, 5 % phenyl methyl siloxane (25 m× 0.2 mm× 0.33 µm)

MEP, MnBP, MiBP, MEHP, MiNP, MnOP, MBzP, MEOHP, MEHHP MEP, MBP, MEHP, MBzP

ED, Azeotropic distillation with DCM, SiO2 column clean up and derivatization with MSTFA+ PFP ED, LLE with hexane and derivatization with diazomethane. Fluorisil cleanup

GC(EI)-SIM-MS

DB-5 MS (30 m× 0.25 mm× 0.25 µm)

LOQ: 0.2-1.3 µg/L Recovery: 88.5-105.8 % RSD: 1.2-9.7 %

[20]

GC(EI)-SIM-MS

HP-5MS SV (30 m× 0.25 mm× 0.25 µm)

LOD: 0.05-0.1 µg/L Recovery: 86.3-119 % RSD: 0.6-6.1 %

[21]

-

[19]

34

Page 34 of 40

MMP, MEP, MiBP, MBP, MBzP MEHP, 5-OH-MEHP, 5-oxo-MEHP, 5-cxMEPP MMP, MEP, MBP, MEHP MMP, MEP, MBP, MEHP MBzP MBP, MBzP

ED, LLE with hexane and derivatization with TMSDM. Fluorisil clean up.

GC-ITMS (EI)

VF-5MS (50 m× 0.25 mm× 0.25 µm)

LOQ: 0.03-8.89µg/L RSD: 1.1-24 %

[22]

ED, SPME with on fibre (PDMS/DVP) gas phase derivatization with diazomethane ED, MWCNT-MNP SPE and magnetic separation. Derivatization with TMCS+BSTFA

SPME- GC(EI)SIM-MS

HP-5MS (30 m× 0.25 mm× 0.25 µm)

LOD: 0.02-4.4 µg/L RSD: 18 %

[28]

GC(EI)-SIM-MS

BPX5 5% Phenyl (30 m× 0.25 mm× 0.25 µm)

LOD: 0.025-0.050 µg/L Recovery: 92.6-98.8 % RSD: 5.28-11.22 %

[29]

ED, LLE with hexane+DCM, derivatization with diazomethane. LLE with hexane

GC-QIT-MS

WCOT apolar (50 m× mm× µm)

LOD: 3 µg/L RSD: <10 %

[36]

1 2

BSTFA, N,O-Bis(trimethylsilyl) trifluoroacetamide; DCM, Dichloromethane; DIC, N,N'-diisopropylcarbodiimide; ED, Enzymatic deconjucation (hydrolysis); EI, Electron

3

impact ionization; GC-MS, Gas chromatography-mass spectrometry; HFIP, 1,1,1,3,3,3-hexafluoroisopropanol; HRMS, High resolution mass spectrpmeter; IOA, isooctane;

4

LOD(Q), Limit of detection (quantification); LLE, Liquid-liquid extraction; MWCNT, multiwall carbon nanotubes; MSTFA, N-methyl-N-trimethylsilyl trifluoroacetamide;

5

MTBE, Methyl tertiarybutyl ether; MNP, Magnetic nano particles; NCI, negative chemical ionization; PFP, Pentafluoropyridine; PDMS/DVP, polydimethylsiloxane

6

/divinylbenzene; QIT, Quadrupole ion trap; RSD, Relative standard deviation; SIM, Selected ion monitoring; SPE, Soild phase extraction; SPME, solid-phase micro-

7

extraction; TEOTFB, triethyloxonium tetrafluoroborate; TMCS, Trimethyl chlorosilane; TMSDM,Trimethyl silyl diazomethane

8

35

Page 35 of 40

1

Table 2. Selected LC-MS/MS analytical methods for the determination of biomarkers of phthalate exposure in human urine samples

2 Analytes

Pre-

MEP, MBP, MCHP MBzP, MNP, MEHP MOP, MDP 22 metabolites

ED, Two stage SPE (Oasis HLB) Eluent: NH4OH and ACN, EA

MEP, MiBP, MBP, MBzP, MEHP 5OH-MEHP, 5oxo-MEHP

treatment/Extraction/Clean up

ED, Dilution with AA:ACN:W, Online SPE (Chromolith Flash RP-18e, (25 mm ×4.6 mm× 2 µm) Eluent: W:ACN (in 0.1%AA) ED, SPE (Oasis HLB) Eluent: ACN:EA

Analytical System

Analytical Column /Mobile phase

Analytical performance characteristics

Ref.

HPLC-MS/MS NCI RT: 7.7 m HPLC-MS/MS NESI RT: 29 m

Betasil phenyl (50 mm× 3 mm×5 µm) Acetate buffer in W: ACN BETASIL Phenyl (100 mm×2.1 mm, 3 µm) W:ACN (in 0.1%AA)

LOD: 0.6-1.5 µg/L RSD: 4.9-17.2 %

[8]

LOD: 0.2–1.1 µg/L Recovery: 61-109 % RSD: 3.3–14%

[13]

UHPLC-MS/MS NESI RT: 8.5 m

UPLC BEH Phenyl (100 mm× 2.1 mm×1.7 µm) 0.1% AA in W: ACN

LOQ: 0.1–10 µg/L Recovery: 78-102 % RSD: 13–29 %

[15]

MMP, MEP, MBP, MiBP, MBzP, MEHP, 5OHMEHP, 5cx-MEPP, 5oxo-MEHP MEP, MnBP, MBzP, MEHP, MEHHP

ED, Dilution with phosphate buffer, SPE (Oasis HLB); Eluent: ACN, EA and acidified (FA) ACN

UHPLC-MS/MS NESI RT: 7 m

BEH- Phenyl column (50 mm × 2.1 mm× 1.7 µm) 0.1 % FA in W: ACN;

LOD: 0.06-0.49 µg/L RSD: 1.4-14 %

[22]

ED, SPE (Oasis HLB) Eluent: MeOH

HPLC-MS/MS RT: 9 m

ED, Dilution, Automated SPE (SimpliQ) Eluent: ACN

HPLC-MS/MS NESI RT: 12 m

LOD: 0.05-3.0 µg/L RSD: 1.8-7.8 % Recovery: 91.9-100.6 % LOD: 0.1–1.0 µg/L Recovery: 95.1-112.2 % RSD: 3.7–10.2 %

[23]

MMP, MEP, MBP, MBzP MEHP and other multiclass analytes

Synergy polar 4u-RP-C18 (150 mm× 4.6 mm×4 µm) ACN:W(5 % AA) BETASIL Phenyl (50 mm×2.1 mm, 3 µm) W:ACN in 0.1 % AA

[25]

36

Page 36 of 40

MEP, MiBP, MBP, MBzP MEHP, 5OH-MEHP, 5oxo-MEHP and other multiclass analytes 14 phthalate metabolites and other multiclass analytes

ED, SPE (Bond Elut-Certify LRC) Eluent: ACN

UHPLC-MS/MS NESI RT: 13.66 m

Kinetex phenyl-hexyl (100 mm×2.1 mm, 1.7 µm) W:ACN in 0.1 % AA

LOD: 0.13–0.37 µg/L Recovery: 89.2-106.4 % RSD: 1.5–17.7 %

[26]

ED, SPE (Oasis MAX mixed mode) Eluent: MeOH IN 1 % FA

UHPLC-MS/MS NESI RT: 10 m

LOD: 0.11–0.84 µg/L Recovery: 79.4-125 % RSD: 3.9–22.0 %

[27]

MEP, MBzP, DEHP

ED, Online SPE (Hysphre- C18HD (10 mm×2 mm×3 µm) ED, Online SPE Chromolith flash RP-18 e (25mm×4.6) 0.1 % AA in W:ACN ED, Online RAM Column (Lichrospher RP-8 ADS (25 mm×4 mm×25 µm) Eluent: 0.1 % AA in 90 % MA (back flush mode)

LOD: 0.7-1.7 µg/L RSD: 0.8-4.8 % Recovery: 84-96.5 % LOD: 0.11–4.3 µg/L Recovery: 87.9-103.8 % RSD: 2.3–13.5 % LOQ: 2.4-8.3 µg/L RSD: 76.6-122 % Recovery: 82.8-121.9 %

[31]

16 phthalate metabolites

HPLC-MS/MS NESI RT: 16 m HPLC-MS/MS NESI RT: 27 m LC×LC -MS/MS NESI RT: 25 m

Acquity UPLC BEH C18 (100 mm×2.1 mm, 1.7 µm) W:ACN in 0.1 % AA Intertsil ODS-3 (50 mm × 2.1 mm×5 µm) 0.1 % AA in ACN: W BETASIL Phenyl (150 mm×2.1 mm, 3 µm) in back flush mode Luna phenyl-hexyl (150 mm× 4.6 mm×3 µm) 0.1 % AA in W:ACN

LC×LC -MS/MS NESI RT: 35 m

Fusion-RP, (250 mm× 3 mm×4 µm) ACN in 0.1 % AA

LOD: 0.5 µg/L RSD: 3.1-11.5 % Recovery: 82.8-121.9 %

[34]

LC×LC-MS/MS NESI RT: 22 m

Betasil phenyl (150 mm× 3 mm×4.6 µm) 1 % AA in ACN

LOD: 0.25-0.5 µg/L RSD: 2.8-9.7 % Recovery: 92-105.8 %

[35]

HPLC-MS/MS NESI RT: 25 m UHPLC-MS/MS RT: 9.5 m NESI

BETASIL Phenyl (100 mm×2 mm, 3 µm) 0.1 % AA in W:ACN Acquity BEH C18 (100 mm× 2.1 mm×1.7 µm) 0.1% FA in W:ACN

LOD: 0.23–1.59 µg/L Recovery: 59-102% RSD: 2.1–12.7 % LOQ: 1.2–2.6 µg/L Recovery: 90.2-102 % RSD: 0.9–12 %

[38]

MEP, MBP, MBzP, MEHP 5OH-MEHP, 5oxoMEHP, MOP 7 oxo-MMeOHP, 7cX-MMeHP, 7oxoMMeOP MEHP, 5OH-MEHP, 5oxo-MEHP, 5cx-MEPP, 2cx-MMHP 15 phthalate metabolites

MEHP, MEHHP, MEOHP 5 cx-MEPP, 2cx-MMHP

ED, Online RAM Column (Lichrospher RP-8 ADS (25 mm×4 mm×25 µm ) Eluent: 10 % ACN in 0.1 % AA (back flush mode) ED, Online RAM Column (Lichrospher RP-8 ADS(25 mm×4 mm×25 µm) Eluent: 1 % AA in ACN (back flush mode) ED, Automated SPE (Nexus ABS Elut) Eluent: ACN:EA ED, LLE with EA and reconstitution in 0.01% FA W:ACN

[32]

[33]

[40]

37

Page 37 of 40

MEP, MBP, MBzP, MEHP

ED, PP, Dilution with water Online column switching back flush mode , Hypercarb PGC (30 mm×0.32 mm, 5 µm)

HPLC-QITMS NESI RT: 8 m

Hypercarb PGC (100 mm×0.32 mm, 5 µm) W (10 mM NH4AC): W:THF (10:30)

LOD:1.6-3.5 µg/L Recovery:RSD: 4.0-18 %

[41]

1 2

AA, Acetic acid; ACN, Acetonitrile; AmA, Ammonium acetate; EA, Ethyl acetate; FA, Formic acid; ED, Enzymatic deconjucation; HPLC, High performance liquid

3

chromatography; LLE, Liquid-liquid extraction; LOD(Q), Limit of detection (quantitation); MeOH, Methanol; MS, Mass spectrometry; NCI, Negative chemical ionization;

4

NESI, Negative electrospray ionization; PP, Protein precipitation; PGC, Porous graphitic carbon; QIT, Quadruple ion trap; RSD, Relative standard deviation; RT, Run time;

5

SPE, Solid phase extraction; THF, Tetrahydrofuran; UHPLC, Ultra high performance liquid chromatography; W, Water

6

Table 3. Occurrence of phthalate metabolites in human urine samples from various countries Country (Sampling Year)*

Population

N (Age, years)

No. of analytes

Belgium (200912) Canada (200709) China (2010)

General

152 (18-84)

9

General

3236(6-49)

11

1.4-56.0 (GM)

General

183 (3-43)

14

18.6-3160 (range)

Denmark (200809) Egypt (2009)

Children

441 (3-6)

8

4.6-78.0 (GM)

Girlsb

11

Finland 2008)

Infantsc

28 (10-13) 29 (10-13) 58 (<1.16) 67 (<1.16)

0.2-7740 (range) 0.1-4290 (range) LOD- 1985 LOD-26,011

(2006-

12

Average concentration/ Range (µg L-1)

95th(µg L-1) Percentile

Detection Rate (%)

No. of samples above RfD/TDI

5-540a

Ref.

[49]

9.8-824.2

1-100 %

14.6-251.0

All samples have at least one 84.4-100 %

[3] 39 % > TDI of DBP

[50]

[51] [52]

0.18-118d 0.65-668d

2.5-100 % 10.6-100 %

[53]

38

Page 38 of 40

France (2007)

Pregnant women

279

16

1.1-2003.6

5.3-605.1

3.5-100 %

Germany (2008)

Infants Mothers

47 (<1) 52

12 12

LOD-224.5e LOD-4592.5e

ND-120.5 ND-424.6

1-100% 7-100%

India

General

15

14

ND-150 (GM)

Italy (2013)

General

157 (19-58)

5

17.01-884.4 21.91-641.4

Japan (2011)

General

35

14

M: 3.62-64.73 (GM) F: 2.49-54.81 (GM) ND-18.2 (GM)

Korea (2011)

393 (< 6) 365 (20-39) 140 (20-39) 157 (20-39)

3

ND-620.1 ND-1003 ND-242.0 ND-498.5

58.1-265.5 55.3-131.9 32.8-74.3 35.5-88.3

Kuwait (2011)

Children Mothers Male adults Female adults General

46

14

0.4-411 (GM)

Malaysia (2011)

General

29

14

0.6-18.6 (GM)

Mexico 2003)

(2001-

Pregnant Women

30 923-30)

11

1.1-38.1 (GM)

Netherland (2004) Norway (20012004)

Pregnant women Children

100 (18-41)

14

0.8-112.0

623 (<10)

11

LOD-6710 (range)

(2011)

(GM)

All samples have at least one 74- 100%

≈ 30 % > RfD and TDI 8.5 % > TDIof DiBP 21 % > TDI of DiBP and DEHP ≈ 47% > RfD of DEHP

[54]

[12]

[55]

[23]

All samples have at least one 98.7-100 % 98.9-95.1 % 85-98.6 % 81.5-98.1 %

[55]

[19]

4, 11 and 46 % > RfD of DEP, DBP and DEHP 3 % > RfD of DEHP

[54]

2.40-897

All samples have at least one 67-100 %

[55]


2-100 %

[57]

8.3-380.4

8 metabolites detected in all samples

[58]

[56]

39

Page 39 of 40

Poland 2011)

(2007-

Mother Children

165 (31.4±4.2) 148 (2-2.5) 139 (27.5±5.2)

11 11

0.02-1692 0.02-1331

Puerto Rico (2010-2012)

Pregnant women

11

1.7-102.2

Sweden (2012)

Elderly population

1076 (>70)

10

LOD-1820 (range)

Spain (20042008)

Pregnant Women Children General

118 (17-43) 19 (4)

11

60 (21-67)

5

3.9-31.2 (GM)

United States (2009-2010)

General

2749

15


Vietnam (2011)

General

30

14

0.8-21.0 (GM)

Taiwan (2011)

(GM)

0.5-563.2 1.0-119.6

49-100 % 14-100 %

[59]

3.2-1880

92.5-100 %

[60]

LOD-33.3f

4 metabolites detected in all samples 85-100 % 100 %

[61]

15.1-193.6

100 %

[63]


All samples have at least one All samples have at least one

[1]

1.5-324g 4-755g

[62]

[55]

1 2

*

3

TDI Tolerable daily intake of Europian food safety and standard authority; µg/kg-bw/day (DBP:10); a75th percentile; b Urban and rural girls; c Full term and preterm infants; d

4

90th percentile; e Multiple urine samples from infants (n=207)and mothers (n=332) were collected; f 75th percentile; g median range

If sampling time is not specified then year of publication is given. GM, Geometrical mean; Reference dose (RfD) of USEPA and TDI for DEHP is 20 and 50 µg/kg-bw/day

5

40

Page 40 of 40