Re-exploring the high-throughput potential of

Anal Bioanal Chem DOI 10.1007/s00216-013-7527-4

REVIEW

Re-exploring the high-throughput potential of microextraction techniques, SPME and MEPS, as powerful strategies for medical diagnostic purposes. Innovative approaches, recent applications and future trends Jorge Pereira & Catarina Luís Silva & Rosa Perestrelo & João Gonçalves & Vera Alves & José S. Câmara

Received: 5 October 2013 / Revised: 16 November 2013 / Accepted: 20 November 2013 # Springer-Verlag Berlin Heidelberg 2014

Abstract The human population continues to grow exponentially in the fast developing and most populated countries, whereas in Western Europe it is getting older and older each year. This inevitably raises the demand for better and more efficient medical services without increasing the economic burden in the same proportion. To meet these requirements, improvement of medical diagnosis is certainly a key aspect to consider. Therefore, we need powerful analytical methodologies able to go deeper and further in the characterization of human metabolism and identification of disease biomarkers and endogenous molecules in body fluids and tissues. The ultimate goal is to have a reliable and early medical diagnosis, mitigating the disease complications as much as possible. Microextraction techniques (METs) represent a key step in these analytical methodologies by providing samples in the suitable volumes and purification levels necessary for the characterization of the target analytes. In this aspect, solidphase microextraction (SPME) and, more recently, microextraction by packed sorbent (MEPS), are powerful sample preparation techniques, characterized by their reduced time of analysis, low solvent consumption, and broad application. Moreover, as miniaturized techniques, they can be Published in the topical collection Microextraction Techniques with guest editors Miguel Valcárcel Cases, Soledad Cárdenas Aranzana and Rafael Lucena Rodríguez. J. Pereira : C. L. Silva : R. Perestrelo : J. Gonçalves : V. Alves : J. S. Câmara CQM - Centro de Química da Madeira, Universidade da Madeira, Campus Universitário da Penteada, 9000-390 Funchal, Portugal J. Pereira : C. L. Silva : R. Perestrelo : J. Gonçalves : V. Alves : J. S. Câmara (*) Centro de Ciências Exactas e da Engenharia da Universidade da Madeira, Campus Universitário da Penteada, 9000-390 Funchal, Portugal e-mail: [email protected]

easily automatized to have a high-throughput performance in the clinical environment. In this review, we explore some of the most interesting MEPS and SPME applications, focusing on recent trends and applications to medical diagnostic, particularly the in vivo and near real time applications. Keywords Microextraction techniques (METs) . Solid-phase microextraction (SPME) . Microextraction by packed sorbent (MEPS) . Medical diagnosis . Applications

Introduction One of the major challenges facing our health care systems is to achieve more with less money and become more efficient in a scenario in which growing obesity and longer lifespans are contributing to a prevalence of highly disabling diseases, such as the cardiovascular, neurodegenerative, and oncologic ones. This will be only possible by improving both the early diagnostic strategies as well as the analytical performance of the technologies available nowadays. To address these two challenges, sample preparation will be a critical step due to the high demands of the clinical environment. Under these conditions continuous and accurate monitoring of life is often necessary and only regulated analytical methodologies can be accepted. This has an obvious cost that needs to be minimized as much as possible. Moreover, biological samples are highly complex dynamic matrices and most of the time they cannot be directly analyzed. In this sense, METs offer different solutions to fulfil this requirement as well as others that we will explore in this review. The initial sample extraction procedures, mostly based on liquid-liquid (LLE) or solid–liquid extractions (SPE), were developed in an academic environment and were not suitable

J. Pereira et al.

for the clinical environment because of a number of issues related to organic solvents usage and waste management, the high sample volume required to obtain a satisfactory analytical performance and the low automation and throughput possibilities. However, in recent years, there was a fast evolution in the miniaturization of the most common analytical procedures, particularly in the chromatographic separation, which raised the analytical limits higher than ever before and opened new possibilities in several fields, namely in the medical diagnosis. This required more efficient sample preparation techniques, and a large number of different approaches to sample extraction are nowadays available. These techniques, generically designated by METs, are possibly the most suitable to process biological samples in a clinical environment, given the low availability of the biological fluids (blood, serum, etc.), the complexity of the matrix, containing many inherent interferents, and the need to minimize cost through automation and high throughput solutions [1]. Most of them can be considered as improvements of the original LLE and SPE techniques, using the same extraction principles of the parental technique. For simplicity, we will consider only applications that already reached the commercial circuit, being therefore easily available to the whole scientific community. Therefore, METs can be generically organized as liquid-liquid or solid-phase extractions. Single-drop microextraction, which is based on the differential diffusion of the target analytes between two- or three-phase separations (reviewed in [2] and references within) is certainly the most popular and efficient form of liquid-liquid microextraction. Regarding the solid-phase METs, we will distinguish them according to the mechanism of dispersion of the target analytes in the stationary phase, a criterion also used by many other researchers ([3–6]). Accordingly, we can have diffusion of the analytes mediated by stirring or flow-through. In the first case, we have the fiber solid-phase microextraction (fiber SPME), which represent the first and most often used format of SPME, the stir-bar sorptive extraction (SBSE), and the thin film microextraction (TFME). In the second case, the one involving diffusion of the analytes mediated by flow-through, we have the in-tip SPME, in-tube SPME (IT-SPME), and inneedle SPME. Notably, as a recent in-needle SPME, microextraction by packed-sorbent (MEPS) became very popular and is nowadays widely used (reviwed in [7]). A comprehensive classification of METs is given in Fig. 1, highlighting SPME and MEPS, which we will explore with more detail in this review, particularly unveiling their high throughput potentials in the biomedical field.

High-throughput potential of METs SPME and MEPS are possibly the most successful miniaturized extraction techniques, presenting additional advantages

over conventional approaches, as using very low or no solvent at all, and having greater sensitivity than the exhaustive extraction procedures. Simultaneously, they are fast, simple, and user-friendly systems that can be easily automated [8] and, SPME and different configurations of MEPS (as the one reported by Candish et al. [9]) allow the integration of several procedures (sample extraction, concentration, and loading) in a single step, thus limiting the existence of experimental errors. Both techniques have several sorbent materials commercially available that can be packed in permanently used syringes of modern high-end autosampler devices (e.g., CTC PAL) [10]; new and promising materials are continuously being characterized. These techniques present a highthroughput potential able to correspond to the demands of medical and clinical analysis. In the end, the quality of the sample preparation procedure will determine the quality of the data produced and, consequently, the ability to unveil new, specific reliable diseases biomarkers to use in early diagnosis. SPME overview SPME, invented by Pawliszyn and co-workers in 1989, was the first successful modern MET [11]. Its basic principle involves the equilibration of the target analytes between the sample matrix to be analyzed and the stationary phase where the analytes should be retained, usually an organic polymeric phase coated on the outer surface of a fused silica fiber. The analytes are then thermally desorbed in a gas chromatography (GC) injector port, or removed by solvents for high performance liquid chromatography (HPLC) or electrophoresis applications, and subsequently analyzed. This combination allows an excellent analytical performance for the quantification of different chemical families [12]. Modes of SPME operation In its most common format, the fused silica SPME coated fiber can be introduced into the sample in three different ways, (i) direct extraction (DI-SPME), (ii) headspace (HS-SPME) and (iii) extraction with membrane protection. In DI-SPME, the coated fiber is directly immersed in the aqueous samples, and the analytes are transported directly from the sample matrix to the extracting phase. The sample agitation is often carried out with a small stirring bar to decrease the time necessary for equilibration and to improve the analytes transportation from the sample bulk to the fiber vicinity [13, 14]. In the HS-SPME mode the analytes are extracted from the gas phase above a gaseous, aqueous or solid sample. The primary reason for this modification is to protect the fiber from adverse effects caused by non-volatile, high molecular-weight substances present in the sample matrix (e.g. proteins). The headspace mode also allows matrix modifications (including pH adjustment) without affecting the fiber. In a system consisting of a liquid sample

Re-exploring the high-throughput potential of METs

Fig. 1 Microextraction techniques diagram

and its headspace, the amount of an analyte extracted by the fiber coating does not depend on the location of the fiber (in the liquid or gas phase) [13]. In the extraction with membrane protection mode the fiber is separated from the sample with a selective membrane, which lets the analytes through while blocking the interferences. The main purpose for the use of the membrane barrier is to protect the fiber against adverse effects caused by high molecular-weight compounds when very complex samples are analyzed. Although, extraction from headspace serves the same purpose, membrane protection enables the analysis of less volatile compounds [13]. Regardless the sampling mode using SPME is not an exhaustive extraction methodology and sampling can be performed at a specified time before achieving equilibrium [15, 16]. Experimental parameters affecting SPME extraction efficiency The amount of analytes extracted by SPME is dependent of several experimental parameters that can influence the SPME extraction efficiency. Among these, the nature of the fiber coating, the extraction time and extraction temperature, the ionic strength, and pH, should be pointed out [17–20]. Coating fibers Several SPME fiber coatings with different thickness and polarities combinations are commercially available, namely three poly(dimethylsiloxane) (PDMS) films of different thicknesses (7, 30, and 100 μm), 85 μm polyacrylate (PA), and the mixed phases of 65, 60 μm polydimethylsiloxane/ divinylbenzene (PDMS/DVB), 75 μm carboxen/ polydimethylsiloxane (CAR/PDMS), 65 μm carbowax/ divinylbenzene (CW/DVB), 50 μm carbowax/templated resin (CW/TR), and 50/30 μm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). At present, the types of

coatings available can be classified as non-polar, polar, and semi-polar (PDMS/DVB) coatings. Polar fibers are effective for extracting polar analytes and nonpolar fibers are effective for extracting the non-polar ones from different matrices. Fibers with different polarity provide high extraction selectivity and reduce the possibility of extracting interferences. For example, fiber coatings of PDMS/DVB, PDMS/CAR, and CW/DVB are more polar than those containing PA, which is why they are more often used to extract highly polar compounds such as alcohols and ethers; on the other hand, CAR gives the PDMS/CAR coating a greater specific surface area, as a result of which extraction of VOC analytes is very efficient [21]. Moreover, both PDMS (high-viscosity rubbery liquid) and PA (solid crystalline) extract analytes via absorption. The other coatings extract the analytes via adsorption. In this mechanism, the molecules can be associated with surfaces via van der Waals, dipole–dipole, and other weak intermolecular forces [22]. Recently, molecularly imprinted polymers (MIPs) have proven to be useful materials in many fields of chemistry or biology, mainly as a selective sorbent for SPE [23, 24], but also as coatings for SPME [23]. This was possible because MIPs as synthetic polymers have a predetermined selectivity for a given target analyte and/or a group of structurally related compounds [25] and, moreover, they are chemically stable and their preparation is easy and inexpensive [26]. Integration of MIPs with SPME showed improved selectivity compared with in-tube stationary-phase materials, overcoming the limitations of the SPME coating materials available. The sample pre-concentration enabled by the MIP adsorbents increase sensitivity, yielding lower LODs as we will see later in this review [27]. This approach was reported for the first time by Koster et al. [28], that developed a MIP-coated silica fiber SPME for the sensitive quantification of clenbuterol and five structural analogues (beta-adrenoceptor agonists used in the treatment of asthma) from human urine.

J. Pereira et al.

Extraction time and extraction temperature Extraction time and temperature are two of the most important parameters affecting the SPME extraction efficiency. Extraction time is mainly determined by the agitation rate and the partition coefficient of the analyte between the fiber coating and sample matrix, and is generally shorter for extractions from the headspace. SPME has a maximum sensitivity at the equilibrium point, when the level of analytes extracted remains constant. At this point, small extraction time variations do not affect the level of analyte extracted by the fiber. Moreover, full equilibration is not necessary for accurate and precise analysis by SPME because of the linear relationship between the amount of analyte adsorbed by the SPME fiber and its initial concentration in the sample matrix in nonequilibrium conditions [29]. SPME extraction is an exothermic equilibration process. Therefore when the extraction temperature increases, the extraction rate also rises and simultaneously the distribution constant decrease [13]. On the other hand, the headspace– analyte partition coefficient increases with higher sampling temperature, resulting in a higher analyte concentration in the headspace; consequently the extraction time is shorter [30, 31]. Ionic strength The salt addition can influence the extraction efficiency in two ways: changing the properties of the boundary phase and decreasing the solubility of hydrophilic compounds in aqueous phase (salting-out effect) [32]. However, the addition of salts is preferred for HS-SPME because fiber coatings are prone to damage during agitation by DI-SPME. For this purpose, sodium chloride, sodium hydrogencarbonate, potassium carbonate, and ammonium sulphate are generally used [29]. pH A strong dependence of the extraction efficiency on the pH value is observed for acidic and basic analytes; therefore, the SPME extraction yield can be improved by adjustment of the pH of the samples. By adjusting the pH, weak acids and bases can be converted to their neutral forms, in which they can be extracted by the SPME fiber. The sample pH should be at least two units lower than the pKa of the analyte in order to ensure that the acidic compound is in the neutral form. Similarly, for basic analytes, the pH must be two units higher than the pKb [16]. Different approaches on SPME In recent years, several formats of SPME have been (reviewed in [33]). Among them, in-tube solid-phase microextraction (IT-SPME), also called “coated capillary

microextraction,” was introduced in 1997 by Eisert and Pawliszyn [34]. IT-SPME consists in an open-tubular capillary column as an SPME device and has also been combined with HPLC and/or HPLC/MS as an efficient and simple preparation method [35]. Compared with the conventional SPME fiber, IT-SPME is a fully automated analytical technique that provides higher analytical efficiency. It was also developed to overcome some problems related to the use of conventional fiber SPME, such as fragility, low sorption capacity, and bleeding from thick-film fiber coatings [36, 37]. The main advantage of IT-SPME technique is that it enables automation of the SPME/HPLC process, allowing extraction, desorption, and injection to be performed continuously using a standard autosampler, and can be used with all GC commercial columns, thus increasing the number of stationary phases, allowing a wide field of application range of the method [37]. In addition, IT-SPME requires lower sample volumes and is versatile, according a wide range of available coatings [38]. Several extraction conditions could be manipulated to improve the IT-SPME extraction and separation efficiency, such as capillary column types, pH values, the numbers of draw/eject cycles, desorption solvent type, among others [36]. Moreover, automated on-line in-tube SPME-assisted derivatization technique improves detection through increasing selectivity and sensitivity, and enhances the separation of analytes with poor chromatographic behavior. The main disadvantage of the technique is the requirement for very clean samples because the capillary can be easily blocked [37]. In-tip solid phase microextraction (In-tip SPME) is another recent approach of SPME. In this SPME variant, the sorbent is used in tips that are totally similar to the conventional ones, allowing a simple and fast utilization, with lower cost per sample, and also a small sample volume. The requirement of sample pretreatment (filtration and/or dilution of complex samples) is one of the few relevant disadvantages that can be appointed to in-tip SPME [39]. Full automation of this SPME approach has been reported by Xie et al. [40], that used a pipette tip-based SPME in a 96-well plate format in order to accomplish SPME automation, while maintaining the technique simplicity. Particularly in-tip SPME has been improved for biomedical applications (BioSPME tips, reviewed in [41]) and its utilization will certainly be widespread in the near future as its integration in the clinical workflow will be much easier, given its familiar tip-format. These new geometries also broadened SPME application to non-volatile and thermally unstable compounds by allowing liquid chromatography coupling. In terms of stationary phases, their number and properties are continuously growing, although many of them are customized for certain applications and are not readily available commercially. Thin-film microextraction (TFME) is another sampling SPME device that has been developed to improve the

Re-exploring the high-throughput potential of METs Table 1 Characterization of the most common formats and sorbents available for SPME and MEPS MEPS SPME

In-tip SPME

IT-SPMEa

TFMEb Hamilton syringe

eVOL®

Online

Extraction Device

Living systems, foods, natural products, Biological matrices, pharmaceuticals, Sample

Food, biological matrices, Biological

biological matrices, matrix

environmental

Food and flavours, pharmaceutical, biological matrices, toxicology, forensic

samples, forensic

and environmental samples

forensic and environmental matrices

toxicology, forensic

samples applications

and environmental samples

Solvent Solvent-free

Low

Low

Low

Low

10 – 90

< 10

10 – 15

1 – 2 min/sampled

2 – 10

Yes

Yes

Yes

Yes

Good

Good

Medium

Good

usage

Extraction time (min)

Adsorption/ Multi-step

desorption

Selectivityc

Good

PDMS; CAR/PDMS; PDMS/DVB; C18;

PDMS; PDMS/DVB;

PAN; C18-PAN; PS-

PA; PEG;

DVB-PAN; PBA-

CAR/PDMS; CW-

PAN; C18-SCX;

DVB;

(a) Commercial GC Others: MWCNT-

DVB/CAR/PDMS; Similar to SPME CW/TPR fibres.

Available sorbents

stationary phases (e.g. PDMS; 1methylsiloxane, PEG, octadecylimidazolium porous DVB polymer, etc.)

Otherse: MIPs [50]; Others: Polymer PPY and PPPY [51]; monoliths [33]

(b) Tailor-made coatings parafilm [57]; MIPs (e.g. RAM, MIPs,

ADS [53]; sol-gel

monolithic sorbents, etc.)

gel deposition on titanium wire [55]

CX; PEP;

bromide [56];

immunoaffinity [52];

porous silica [54]; sol-

C2; C8; C18; C8/SCX; SIL; APS; SCX; SAX; SDVB; HDVB; PGC; R-AX; R-

[58]; PDMS/βcyclodextrin [59]; Polyaniline-nylon-6 nanofibers [60]; Ethylene vinyl acetate [61]

Otherse: RAM; MIPs; carbon [49].

J. Pereira et al. According to Kataoka et al. [37], this technique is called “Open-tubular Trapping (OTT)” when coupled to GC and “in-tube SPME” (IT-SPME) when coupled to LC

a

b

According to Jiang and Pawliszyn [43] nomenclature. However, some authors call this technique as planar solid-phase microextraction (PSPME) [44, 45]

c

Selectivity can be very variable, depending of matrix nature and the sorbent type

d

Extraction time obtained with the application of the robotic 96-blade (thin-film) SPME system [62–64]

e

Not yet available commercially

ADS – alkyl-diol-silica; APS – amino-propyl silane; C2 – ethyl; C8 – octyl; C18 – octadecyl; CAR/PDMS – carboxen/polydimethylsiloxane; CW-DVB – carbowax-divinylbenzene; CW/TPR – carbowax/templated resin; DVB/CAR/PDMS – divinylbenzene/carboxen/polydimethylsiloxane; HDVB – hydrophobic polystyrene-divinylbenzene copolymer; In-tip SPME – In-tip solid-phase microextraction; IT-SPME – In-tube solid-phase microextraction; MEPS – Microextraction by packed sorbent; MIPs – molecular imprinted polymers; MWCNT – carboxylic acid functionalized multi-walled carbon nanotube; PA – polyacrylate; PAN – polyacrylonitrile; PBA-PAN – phenylboronic acid – polyacrylonitrile; PDMS – polydimethylsiloxane; PDMS/DVB – polydimethylsiloxane/divinylbenzene; PEG – polyethylene glycol; PEP – polar enhanced polymer; PGC – porous graphitic carbon; PPPY – poly-Nphenylpyrrole; PPY – polypyrrole; PS-DVB-PAN – polystyrene-divinylbenzene polyacrylonitrile; SAX – strong anion exchange; SCX – strong cation exchange; SDVB – styrene-divinylbenzene; SIL – silica; SPME – Solid-phase microextraction; RAM – restricted access material; R-AX – retain anion exchange; R-CX – retain cation exchange; TFME – Thin-film microextraction; V-AX – verify anion exchange; V-CX – verify cation exchange

sensitivity within a short time of analysis [42–45]. TFME combines advantages of both SPE and SPME as the high surface area of porous SPE sorbents, the robustness, and easy handling of organic adsorbents [43, 46]. TFME compared to conventional methods results in significant time and cost savings. Moreover, the extraction selectivity in TFME/SPME provides a high degree of clean-up of unwanted interferences from samples, which minimizes the possibility of the matrix effects (a critical concern in LC-MS applications) [47]. In addition, TFME/SPME can be applied to in vivo sampling from a living without the need to isolate a defined sample volume, which is peculiar from the metabolomic point of view, as it decreases the overall number of experimental steps [47, 48]. The most common SPME fibers and coatings and respective generic applications can be appreciated in Table 1. A similar report is presented for MEPS extraction that we will discuss in the following paragraphs. MEPS overview MEPS is a relatively recent MET that we could described in a simplistic way as a solid phase extraction (SPE) technique scaled down to the microliter level. This includes both the sample volume and the solvent usage and, therefore, there are obvious advantages of MEPS in terms of cost and environment protection and applications. This is mainly because much less expensive solvents and wastes are necessary and produced. Furthermore, low volume samples, as most of the biological fluids samples (blood, plasma, etc.), which are difficult to obtain, can be easily processed with the same or higher analytical performance than SPE (reviewed in [7]). MEPS was first introduced by Abdel-Rehim [65] and, as indicated by its name, uses a stationary phase tightly packed inside a cylinder, which is crossed by a syringe (BIN). Using a syringe device, samples are then loaded through the BIN and the analytes are retained in the sorbent according to their chemical properties, particularly their polarity, which will

modulate the efficiency of their extraction from the whole sample [7, 66]. This is also the reason that MEPS can be considered a form of in-needle SPME, as shown in Fig. 1 and in others reviews [4, 5]. As there are nowadays many different sorbents available for MEPS, its range of applications is also very broad, covering the extraction of hydrophobic analytes from aqueous matrices (reversed phase extraction), polar analytes from non-polar organic solvents (normal phase extraction), and charged analytes from aqueous or nonpolar organic samples (mixed mode and ion exchange extraction) (reviewed in Pereira et al. [7]). Therefore, over the years, MEPS has successfully been used to extract a wide range of analytes in different biological matrices, such as blood, plasma, urine, exhaled breath, food, natural products, water, etc. (reviewed in [7, 23, 49]. There are, however, other important advantages in using MEPS that should be highlighted. Unlike regular SPE, MEPS fibers are reusable many times, up to 100 times or more depending on the complexity of the matrix being processed [49, 65, 66], making MEPS far less expensive than its SPE alternatives. Moreover, MEPS experimental layout is very simple and although MEPS can be operated manually, the semiautomatic format using the electronic pipette eVol or the online versions (shown in Table 1) are becoming more popular, allowing a tightly accurate control of the flow during the whole methodology. This minimizes user intervention, increases the analytical performance, and introduces new automation and high-throughput possibilities to the methodology. This extraction technique, introduced by Abdel-Rehim [67] is a miniaturization of the conventional solid phase extraction (SPE) process. The sorbent materials are packed in the MEPS BIN (the equivalent to the SPE cartridges) that is integrated into different syringe options able to process sample volumes from 10 µL up to 1000 µL). There are several MEPS sorbent materials, including reversed phase (C18, C8, and C2), normal phase (silica), restricted access material (RAM), hilic, carbon, polystyrene-divinylbenzene copolymer (PS-DVB), molecular imprinted polymers (MIPs), mixed mode (C8/SCX), or cation exchange (SCX) chemistries [66].

Re-exploring the high-throughput potential of METs

In MEPS, when the sample passes through the solid support, the analytes are adsorbed in the solid phase packed into a barrel insert and needle (BIN). The analytes are then eluted with an organic solvent such as methanol or other mobile phase. These sorbents can be used several times after a conditioning and washing procedure to avoid possible carryover and prolong lifetime of stationary phase, and this significantly increases for cleaner samples. MEPS can be used as a fully automated and miniaturized sample preparation technique to be connected online with GC-MS and LC-MS/MS assays without any modification of the chromatographic apparatus. Also, MEPS uses 100 times less sample and solvent than conventional SPE technique. So, not only less sample volume is required for the extraction, which is critical in some analytical environments, but significant cost savings can also be realized because of the reduction in solvent usage [65]. Experimental parameters affecting MEPS extraction efficiency There are some experimental variables, such as sample volume, composition of washing solution and elution solution, sorbent amount, and sorbent type that can affect the performance of MEPS. Regarding biological fluids like urine, blood, and plasma, which are more complex than other samples, there are some factors to take into account, such as dilution of sample (reducing viscosity of samples), pH adjustment, deproteination, speed of sample loading (ranging from 10 to 20 μLs–1) allowing a better interaction between sample analytes and sorbent. Furthermore, sample loading is important once the increase of sample loading cycles (draw-eject) leads to a higher recovery level. The washing step should be also considered to remove matrix interferents while in elution step, organic solvents are normally used pure or mixed with acid or basic solutions in order to elute all analytes from the sorbent using small volumes as possible (20–50 μL). The last step consists of washing the sorbent to prepare it for a new extraction. According to samples, there are several solvents that can be used in this step that include methanol and acetonitrile in order to eliminate carryover and to permit the reconditioning of MEPS sorbent before reuse [49, 68]. The key points of MEPS experimental layout can be appreciated in Fig. 2.

The powerful ability of SPME and MEPS for medical diagnosis purposes SPME and MEPS have very simple experimental designs that can be easily automated; in fact, there are available several robotized solutions capable of delivering high-throughput analysis that have been successfully used off-line and on-line with LC and GC systems. Their range of applications covers

almost all fields in which analytical chemistry may be used, from food and environment to forensics and bioanalyis (reviewed in [3, 7, 49, 69, 70]). Here we will focus in the biomedical applications, as the ones reported in references [65, 67, 71–78] and many others described with more detail in Table 2. This automation is clearly less expensive and also more accurate as user intervention is minimized. Therefore, higher analytical performances can be attained in the development of more efficient treatments with adjusted drug concentrations. As a consequence, a lower dosage may be enough to obtain the same therapeutic results. Additionally, the solvent usage is very limited and both sorbents are reusable. SPME and MEPS also present a wide range of commercially sorbents with different affinities that enable both the processing of different biological matrices currently used in the clinical environment (blood, serum, plasma, urine, faeces, etc.), with minimum pretreatment, as well as the simultaneous quantification of different compounds [41]. This is particularly relevant in patients receiving complex therapeutic treatments in which several drugs need to be simultaneously monitored. SPME was often pointed out as hardly suitable to highthroughput applications due to the long time necessary to establish equilibrium and low absolute recoveries [79]. However, as discussed by Vuckovic et al. [16], this seems not to be a real handicap as SPME is not an exhaustive sample preparation method and can be performed in optimized, controlled conditions before equilibrium is fully reached as long as the final methodology meets the necessary analytical performance. Moreover, new SPME fibers capable of reaching faster equilibrium were recently developed and optimized to use particularly in the bioanalytical field, as will be discuss in detail in the next section. Traditionally, SPME has been used in its headspace format (HS-SPME) coupled to GC to provide insights into several diseases through the characterization of the differential generation of volatile organic compounds (VOCs) under health and disease. Exhaled human breath (EHB), saliva, sweat, skin, urine, blood, faeces, and vaginal secretions have been used in order to establish diagnostic biomarkers of several diseases, particularly infectious and oncologic diseases. More recently, MEPS is also being applied in the bioanalytical field with success, particularly in the quantification of drugs with different properties (antibiotics, analgesics, antidepressants, etc.). Said et al. [71], for instance, monitored drug concentration during treatment with immunosuppressive drugs (cyclosporine, everolimus, sirolimus, and tacrolimus) using whole blood. MEPS allowed the enrichment of the analytes and depletion of non-polar matrix constituents compared with other traditional techniques (SPE and protein precipitation) used in the quantification of these drugs. Several other drugs belonging to different classes were studied, namely cotinine [80], verapamil [81], and risperidone [82], with the goal of quantifying these compounds so as to avoid some side effects on the human metabolism attributable to a high dosage [82].

J. Pereira et al. Fig. 2 MEPS experimental layout

An exhaustive description of the most relevant reports using SPME and MEPS with great high-throughput biomedical potential is presented in Table 2. As can be observed, the range of applications is very broad, covering biomarkers from very distinct diseases, such as oncologic, cardiovascular, respiratory, infectious, and gastrointestinal, among others. A whole pharmacopeia of drugs can be also analyzed by MEPS and SPME, including anti-cancer, antibiotics, antidepressants, analgesic and anti-inflammatory, antiepileptic, psychoactive and stimulants, anaesthetic, immunomodulatory, steroids, and neurotransmitters drugs. Finally, the biological matrices used are also quite different, including blood, plasma, serum, saliva, urine, faeces, and tissue samples. It is evident that the early detection of a disease is a hallmark for a successful clinical treatment. In most cases, particularly for some oncologic diseases, an early diagnosis is crucial for a patient’s survival without suffering severe impacts in health and life quality; the examples presented in Table 2 show the potentialities of MEPS and SPME to monitor both pathology and therapy intervention and evolution. Moreover, these analytical approaches can be easily adapted and incorporated in current automatic platforms, allowing to obtain more experiments in a shorter time frame with increased data quality [83]. A significant number of these applications involves the characterization of VOCs present in the exhaled breath of the patients, which can be a valuable tool in clinical practice because of its superior analytical performance compared with the conventional methodologies [84]. Overall, the bioavailability, bioequivalence, and pharmacokinetic data obtained in these reports using MEPS and SPME is extremely valuable and will contribute to define higher standards in health care and diagnosis [15, 23, 28, 63, 64, 67, 71–78, 80–82, 85–209].

Cancer Early diagnosis of cancer with effective screening methods is crucial for successful therapy. Ultimately, diagnosis of cancer disease can be made only on the basis of biopsy and histopathological examination of the tissue or cells, whereas cancer screening, which is based on different imaging methods [e.g., X-ray, ultrasound, computed tomography (CT), positron emission tomography (PET), mammography, or detection of cancer markers) can only select patients suspected of having cancer, who need to be examined further by biopsy and histopathology. A desirable screening method should have certain characteristics as being non-invasive, painless, inexpensive, and easily accessible to a large number of patients, reliable, and a tool to facilitate cancer diagnosis in its early stage, making unnecessary the use of additional invasive methods. Cancer cells present metabolic rates much higher than normal cells, thus producing metabolites that vary in their presence and amount compared with normal cell metabolism. A part of these metabolites are volatile and can be measured in the exhaled breath, as their presence is correlated with its arterial concentration. This constitutes a metabolic fingerprint that can be used to diagnose patients with cancer [210–213]. Therefore, in recent years breath analysis for the routine monitoring of several forms of cancer has attracted a considerable amount of scientific interest, as breath sampling is a non-invasive technique, totally painless, and agreeable to patients. VOCs are usually present in breath at very low concentrations, so it is necessary to enrich them before analysis [214]. SPME revealed to be very efficient in this task and, in fact, it was the successfully used in several studies showing that human breath VOCs as well as VOCs present in other

Matrix

Irritable Bowel Syndrome Ulcerative colitis, Campylobacter jejuni, and Clostridium difficile

Helicobacter pylori Cholera Infectious diarrhoea

Allergic asthma Nasal sinuses infections Neonatal phenylkenuria (PKU) and maple syrup urine disease (MSUD) Diabetic ketones

Prostate cancer Neuroendocrine tumor markers (HVA, VMA, 5-HIAA) Fungating cancer wounds Skin, naevi and melanoma Unrelated cancer forms Chemotherapy monitoring

HS-SPME/GC-MS

SPME/GC-MS DI-SPME/LC-MS SPME/GC HS-SPME/GC-MS

— — — — — — — — — — —

0.049 0.0032-43.46 0.30-0.60 — — — — —

EHB Urine EHB Faeces

PDMS/CAR

PDMS/DVB PA Activated carbon/zeolite CAR/PDMS

CAR/PDMS

[85–90] [91] [92]

Ref.

SPME/GC-MS

[119] [120]

[114] [76] [115] [116] [117] [118]

[111] [112] [113]

[93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] [105] SPME/GC-QqQ-MS/MS [106] SPME/GC-MS-O [107] SPME/GC-MS [108] [109] [110] — — —

— Biopsy tissue Urine EHB

DVB/CAR/PDMS PA

PDMS/DVB

CAR/PDMS PDMS/DVB PDMS/DVB PDMS PDMS/DVB

PDMS

EHB DVB/CAR/PDMS Human sinus mucus samples DVB/CAR/PDMS Blood PDMS/DVB

Other aldehydes (lung cancer) Lung cancer cells Urine Cancer tissues EHB Blood Urine

Urine EHB EHB

Hexanal and heptanal (lung cancer)

Apoptosis and necrosis (lung cancer) Breast cancer Stomach cancer Liver cancer

Lung cancer cells Blood

— — — 0.21-0.23 0.04 – 4.20 — 3×10-2 M 0.13-1.20 — 1.8-42.0 — — 0.16 0.063-49.6 — — — —

SPME/GC-MS

Analytical method

— 0.026-0.032 nM 0.005-0.006 nM 0.10-0.11 0.012 – 1.26 — 1×10-2 M 0.04-0.40 — 0.6-14.0 — — 0.10 0.046- 24.3 — — — —

LOD LOQ (ng mL-1 by default)

— — —

CAR/PDMS

Stationary phase

— — —

VOCs biomarkers profile for different cancers and diseases Lung cancer Exhaled human breath (EHB)

Target diseases or analytes

Table 2 Representative examples of bioanalytical applications of SPME and MEPS

Re-exploring the high-throughput potential of METs

Culture Saliva Urine

Malaria diagnostic Saliva VOCs metabolome profile Urine VOCs metabolome profile

Linezolid, daptomycin, amoxicillin Chloramphenicol Ofloxacin, marbofloxacin, enrofloxacin, danofloxacin, and difloxacin (Fluoroquinolones) Rifampicin (tuberculosis treatment)

Linezolid and daptomycin Linezolid and amoxicillin

Antibiotics Linezolid

Busulphan Camptothecin and 10-hydroxycamptothecin

Faecal VOCs metabolome profile Cardiovascular diseases (CVDs) related Angiotensin 1 and 2 Pravastatin and pravastatin lactone (lipid-lowering drugs) Atorvastatin and its metabolites (lipid-lowering drugs) Desmosterol, lathosterol and lanosterol (cholesterol biosynthesis precursors) Amiodarone and desethylamiodarone (antiarrhythmic drugs) Drug-eluting coronary stents Anti-cancer drugs Cyclophosphamide

Carbon-tape

Urine

C18 PDMS/DVB C2 C2

Plasma Sterilized stents Blood Plasma

MIPSPME CW/TPR C18 Different GC phases

Plasma

C8 and C18

PPY PPY derivatives PTH and PPY

Urine and water Urine

Blood Plasma

Blood Plasma

XDS–SPME C8 C8 PA, DVB/CAR/PDMS

Blood Plasma and urine Serum Serum

PS poly(MAA-EGDMA) monolithic column

Several fibers (8)

Faeces

PDMS, DVB/CAR/PDMS C18, mixed-mode DVB/CAR/PDMS

— 0.13-3

— C8

Plasma

Necrotising enterocolitis Therapeutic opioids monitoring in heroin addicted treatment Ochratoxin A

1×102 5×102 5-10 5.96-8.73

— 0.05×102 — 1.79-2.62

0.1 μg mL-1

—

—

—

—

(2.0-10.0)×10

500 — 79.0 82.5-151.8 (8.6-9.2)×10 0.381-0.425 0.086 μg mL-1 0.3-95 21.1-35.4

[131]

— SPME/LC-MS 5 nM 0.08-0.66 nM 0.3-1.30 μg mL-1 SPME/GC-FID

— 1.5 nM 0.03-0.33 nM 0.25-1.10 μg mL-1

— — 25.3 25-46 (2.9-3.9)×10 0.141-0.134 0.029 μg mL-1 0.1-37 6.3-10.6

[127] [128] [129] [130]

—

—

IT-SPME/LC

SPME/LC-MS SPME/LC-UV MEPS/LC-UV MEPS/LC-MS/MS IT-SPME/LC-MS SPME/LC-UV MEPS/NACE-MS

SPME/LC-UV

IT-SPME/LC-UV

MEPS/LC-MS/MS

SPME/GC-MS

MEPS/LC-DAD

SPME/GC×GCToFMS HS-SPME/GC-MS

[143]

[23] [141] [142]

[136] [137] [138] [139] [140]

[73] [135]

[133] [134]

[132]

[126]

[123] [124] [125]

— — —

[78]

— — —

multi-fiber-SPME/ LC-MS/MS —

[121] [122]

Ref.

0.2

MEPS/LC-CD

Analytical method

0.05

— 0.04-0.90

LOD LOQ (ng mL-1 by default)

Matrix

Stationary phase

Target diseases or analytes

Table 2 (continued)

J. Pereira et al.

Pregabalin Psychoactive and stimulant drugs Amphetamine type stimulants (ATS)

Valproic acid

Stimulants and β-blockers Agonist BAM8-22 and antagonist BAM22-8 Cotinine (nicotine metabolite) Analgesic drugs Fentanyl (narcotic analgesic) Ibuprofen Acetylsalicylic acid, acetaminophen, atenolol, diclofenac, and ibuprofen Anti-inflammatory drugs Naproxen Naproxen, ibuprofen and mefenamic acid Naproxen, ibuprofen and diclofenac Naproxen, ibuprofen, diclofenac, ketoprofen and acetylsalicylic acid Antiepileptic drugs Oxcarbazepine, carbamazepine, phenytoin, primidone and alprazolam

Antidepressants Citalopram, mirtazapine, paroxetine, sertraline, fluoxetine, and duloxetine Mirtazapine, citalopram, paroxetine, sertraline and fluoxetine Mirtazapine, citalopram, paroxetine, sertraline, fluoxetine and duloxetine Citalopram, fluoxetine and their main metabolites Citalopram, fluoxetine, venlafaxine, fluvoxamine, mirtazapine, and sertraline Citalopram Fluoxetine and norfluoxetine Imipramine, desipramine and clomipramine Imipramine, amitriptyline, nortriptyline and desipramine

Target diseases or analytes

Table 2 (continued)

16-25

—

[166] [167]

—

0.20×10-3 — 0.08-0.15 —

(13.2-195.6)×10-2 (5.6-10.8)×10-3 — 0.63×102 —

2-5 ppb

0.03×10-3 0.07-0.37 0.03-0.07 1.07-16.2

(4.0-59.3)×10-2 (1.8-3.6)×10-3 70 g L-1 0.19×102

Fe3O4 −SiO2 capillary columns

C18 C18 LDH-TiO2 composite

Plasma and urine

PDMS/DVB PDMS-DVB

Urine Water and urine

Serum

CW/TPR CMIP sol–gel SPME fiber C18

Urine Serum Urine

0.4-2

SPME/LC-MS/MS

MEPS/LC-UV MEPS/GC-MS SPME/GC-MS

SPME/LC-UV EC-SPME/IMS SPME/GC-FID MEPS/LC-UV

SPME/GC-MS SPME/LC-UV magnetic-IT-SPME/ LC-UV

— 0.25 μg mL-1 0.01 —

PDMS/DVB

EHB Urine Pure standards

Urine

SPME-UV SPME/LC-UV EC-SPME/GC-MS IT-SPME/μLC Fiber-IT-SPME/CE SPME/LC-ESI-MS IT-SPME/LC-MS MEPS/LC-MS/MS MEPS/GC-MS

0.15 μg mL-1 25 — — — 50 – — 2.7

0.046 μg mL-1 5-10 0.15–0.45 — 44-153 0.1 0.1-1.2 20 nM 0.8

PPY-coated steel fiber PDMS-DVB, CW-TPR Custom platinum wire PDMS Zylon fiber-packed capillary PDMS/DVB Several capillary phases C8 C8

[163] [164] [165]

[159] [160] [161] [162]

[156] [157] [158]

[149] [150] [151] [152] [153] [77] [154] [155] [80]

[147] [148]

Serum Plasma Urine Urine Urine Plasma Urine and serum

SPME/LC-DAD SPME/GC-MS

(0.010-0.014)×103 – 0.11-0.38 —

CW/TPR PDMS-DVB

Urine

[146]

[145]

[144]

Ref.

PPY

SPME/LC-UV

MEPS/LC-UV

—

10-25

C8-SCX

IT-SPME/LC-UV

Analytical method

20-50

5-20

LOD LOQ (ng mL-1 by default)

Custom fused-silica

Stationary phase

Plasma

Plasma

Matrix

Re-exploring the high-throughput potential of METs

MIPs C18 C8+SCX C2 C8 MIPS Methylcyanopropyl– Silarylene

Plasma and urine Plasma Plasma Plasma or urine Saliva Plasma Urine and plasma

Lidocaine, ropivacaine, mepivacaine and bupivacaine Lidocaine, mepivacaine hydrochloride and bupivacaine hydrochloride Lidocaine, bupivacaine, ropicavine and its metabolites Ropivacaine, mepivacaine, lidocaine, prilocaine Lidocaine Ropicavaine

PS C18

PDMS custom capillary phase MIPs CAR/PDMS/DVB C2, C8, C18, M1 and SIL

Plasma and urine Blood

Plasma EHB and blood Urine

Urine

PAN–PS–DVB, PAN-PBA

PAN-over C18−PAN, MPCmodified C18−PAN

Blood Extracted blood spot Plasma

C18 C8 C18

Plasma and saliva Blood Plasma

Plasma

PDMS C8-SCX Several fibers C8

Oral fluid Serum Plasma Plasma, urine and saliva Plasma and saliva Urine immobilized-antibody SPME probe C8-SCX DB-17 capillary column

Stationary phase

Matrix

Acebutolol and Metoprolol Lidocaine, ropivacaine and bupivacaine

Anaesthetic Anaesthetic and analgesic drugs (7) Propranolol propranolol and pindolol Propofol Verapamil, propranolol and metoprolol

Diazepam, Oxazepam Caffeine, riboflavin,

Antipsychotic drugs (7) Butyrophenone derivatives (6, potential neuroleptic drugs) Oxcarbazepine and its metabolites Clozapine and metabolites Benzodiazepines (Oxazepam, diazepam, nordiazepam, lorazepam) Diazepam

7-aminoflunitrazepam (sedative and hypnotic drug)

Tricyclic antidepressants (TADs, 6) Anticonvulsants (7) and TADs (4) Risperidone and 9-hydroxyrisperidone (antipsychotic drugs)

Target diseases or analytes

Table 2 (continued)

5.0 2.1-50.8 2 nM 10 nM — 2 nM —

— — — — —

1.0 10 nM

— — 1.0 0.6-15.2

— 13-20 — 0.009-75.89 nM — 0.01-1.5 4-7 3.8-6.9 0.006-72.20 nM —

0.5 0.2 1 μg mL-1 0.5-100

— — — 0.1-25

MEPS/LC-MS/MS MEPS/GC-MS

MEPS/GC-MS

Microdialysis-MEPS/ NACE–MS MEPS/LC-MS/MS

MEPS/μPESI-MS/ MS MEPS/LC-MS/MS

SPME/GC-MS IT-SPME/CEC SPME/GC-MS

DART−MS/MS 96-blade SPME/LC– MS/MS

MEPS/GC-MS/MS IT-SPME/LC-MS/ MS MEPS/LC-DAD MEPS/LC-CD 96-blade SPME/LCMS/MS SPME/LC−MS/MS

— — 0.050-0.125 0.25 1.5–2.5

SPME/GC-MS MEPS/LC-DAD SPME/LC-UV MEPS/LC-UV MEPS/LC-CD SPME/LC-MS/MS

Analytical method

— (8.0-17.0)×10 0.50-20.0 2-6 0.5 nM 0.060

0.015-0.037 0.08 0.4-0.7

0.2-1 0.03-0.2

0.5-2 (2.0-5.0)×10 — 0.7-2.0 0.17 nM 0.02

LOD LOQ (ng mL-1 by default)

[72] [67] [185] [186] [187]

[184] [74]

[182] [183]

[177] [178] [179] [180] [181]

[64]

[176]

[174] [175] [63]

[172] [173]

[168] [169] [170] [81] [82] [171]

Ref.

J. Pereira et al.

C18 DVB polymer

Urine Saliva

PA

—

Olomoucine (cyclin-dependent kinase selective inhibitor) Plasma Roscovitine (cyclin-dependent kinase selective inhibitor) Urine and plasma Others Alcohol abuse (fatty acid ethyl esters) Hair Ethyl glucuronide (heavy prenatal alcohol exposure) Placental tissue (PT) and perfusate (PF) 5-HMUra and 8-oxodG (Oxidatively damaged DNA) Urine

C8

[207]

[205] [206]

0.01-0.04 ng mg-1 0.04-0.12 ng mg-1 SPME/GC-MS 1.6-ng/mL (PF ) 4.8 ng/mL (PF ) HS-SPME/GC-MS 13.7 ng/g (PT) 41.6 ng/g (PT) 0.05-4.0 0.23-130.0 MEPS/LC-PDA PDMS/DVB PDMS

PS PS

[15] [203] [204] [75]

[28]

[202]

[201]

[200]

[199]

[198]

SPME/LC-MS SPME/LC MEPS/LC-MS/MS

— 0.39-0.54 37.0-189.0 1.0 1.0

SPME/GC-FID SPME/GC-SIMMS SPME/LC-ECD

SPME/LC-UV

SPME/LC-MS/MS

SPME/GC–MS

SPME/LC–MS/MS

[196] [197]

[193] [194] [195]

[192]

[190] [191]

[71] [189]

[188]

Ref.

0.11-0.18 12.0-63 0.5 0.5

10

0.20-1.1 ppb

—

urine: < 1 plasma: 5-10 24-32

— 5-9

0.05-20

1.2

— 5.5-18.7

MEPS/LC-ECD MEPS/LC-MS/MS IT-BA-SPME/LCESI-MS/MS SPME/GC-MS MEPS/CLC-FLD

MEPS/GC-MS IT-SPME/ LC–MS/ MS SPME/GC–MS

— —

5-50 50 4.0

MEPS/LC-MS/MS IT-SPME/LC-FD

Analytical method

0.5-3.0 0.006 MIU mL-1

0.05

0.01-0.05

0.5

0.5-4.6 ppb 1.6-5.6

2-20 1 1.2

5-15 0.3-8.9 pg mL-1

0.15-0.9 —

0.02

LOD LOQ (ng mL-1 by default)

PPY, PTH

MIPs

PDMS/DVB

Urine and serum

Urine

C18

Urine and plasma

Plasma

DVB/CAR/PDMS

Blood and urine

Metoprolol, oxprenolol, mexiletine, propranolol and propaphenon (adrenolytic drugs)

Aptamer sol–gel

PDMS/DVB C18

Plasma

EHB Urine

Clenbuterol and 5 structural analogues

Adenosine Others drugs Selegiline and desmethylselegiline (Parkinson’s disease treatment) Diazepan, pseudoephedrine, ranitidine, propranolol, carbazepines Clenbuterol (β-adrenergic drug to treat breathing disorders)

Dimethylamine and trimethylamine (biogenic amines) Several non-polar heterocyclic amines

C18 C8 poly(VPBA-co-EDMA)

Custom C18

C8 Immunosorbent

C8-SCX

Stationary phase

Blood Plasma

Matrix

17β-estradiol and 2-methoxyestradiol (potential angiogenesis modulators) Neurotransmissors Serotonin, dopamine and noradrenaline (biogenic amines) Urine Dopamine and Serotonin Dopamine

Remifentanil Immunomodulatory drugs Cyclosporine A, Tacrolimus, Sirolimus, and Everolimus Interferon α2a Steroids Steroids Testosterone, cortisol, and dehydroepiandrosterone

Target diseases or analytes

Table 2 (continued)

Re-exploring the high-throughput potential of METs

8-hydroxy-2’-deoxyguanosine (Oxidatively damaged DNA)

5-HIAA – 5-hydroxyindoleacetic acid, 5-HMUra – 5-hydroxymethyluracil, 8-oxodG – 8-oxo-7,8-dihydro-2′-deoxyguanosine, ATS – Amphetamine-type stimulants, BA-SPME – Boronate affinity SPME, CE-ECD – capillary electrophoresis with electrochemical detection, CMIP – conducting molecularly imprinted polymer, C2 – ethyl, C8 – octyl, C8-SCX – octyl-strong cation exchange, C18 – octadecyl, CLC-FLD – capillary liquid chromatography-fluorimetric detection, CW/TPR – Carbowax/Templated resin, DART - direct analysis in real time, DI-SPME – direct immersion solid-phase microextraction, DVB – divinylbenzene, EC-SPME – electrochemically controlled SPME, EHB – Exhaled human breath, GC-MS – gas chromatography–mass spectrometry, GC-MIP-AED – gas chromatography coupled to microwave-induced plasma atomic emission detector, GC-qMS – gas chromatography coupled to a quadrupole mass spectrometer, GC-QqQ-MS/MS- gas chromatography- triple quadrupole mass spectrometry, GC-TOF-MS – gas chromatography coupled to a time of flight mass spectrometer, HPLC-ED – high performance liquid chromatography coupled to electrochemical detection, HPLC-DAD – high performance liquid chromatography with diode array detection, HS-SPME – headspace SPME, HVA – Homovanillic acid, IT-SPME – in-tube SPME, LC-ESI/MS – liquid chromatography coupled to electrospray ionization mass spectrometer, LC-CD – liquid chromatography with coulorimetric detection, LC-FD – liquid chromatography with fluorescence detection, LC-MS/MS – liquid chromatography-tandem mass spectrometry, LC-UV – liquid chromatography coupled to ultraviolet detector, LDH – Zinc/Aluminum layered double hydroxide–titanium, MIPSPME – MIP-coated SPME fiber, MSUD - maple syrup urine disease, PA – polyacrylate, PAN – polyacrylonitrile, PDMS – polydimethylsiloxane, PDMS/DVB – polydimethylsiloxane-divinylbenzene, PKU – phenylkenuria, PPY – polypyrrole, PS – polystyrene polymer, PTH – polythiophene, SPME – solid-phase microextraction UHPLC-MS/MS – ultra-high pressure liquid chromatography-tandem mass spectrometry, UHPLC-PDA – UHPLC-photodiode array, μPESI-MS/MS – micropillar array electrospray ionization mass spectrometry, VMA – vanylmandelic acid, XDS – ion exchange diol silica

[208] [209] IT-SPME/LC-UV IT-SPME/CE-ECD 2.04 nmol L-1 2.61 nmol L-1 MIP monolithic column

7.12 nmol L-1 8.63 nmol L-1

Analytical method Target diseases or analytes

Table 2 (continued)

Matrix

Stationary phase

LOD LOQ (ng mL-1 by default)

Ref.

J. Pereira et al.

matrices such as urine and blood are useful biomarkers for the diagnosis of lung [85–100], breast [101], gastric [102], liver [103, 104], prostate [105] cancers, among others [106, 108, 109]. In Fig. 3 are presented some examples of molecules described as potential biomarkers of different forms of cancer successfully extracted by SPME. In lung cancer, aldehydes are often used as key VOCs for patients screening [94–99]. These aliphatic hydrocarbons compounds, such as ethane and pentane, are main byproducts of lipid peroxidation that is increased under oxidative stress. In fact, epidemiologic evidence pointed to oxidative damage as one of the major sources of carcinogenesis [215]. Moreover, as these compounds present low solubility in blood, they are exhaled within a few lung passages, and therefore easily detected in the exhaled air [84]. Oxidative damage is, however, a hallmark in many other diseases and for this reason these VOCs can also occur in other inflammatory diseases beyond lung cancer. This includes other forms of cancer, such as breast cancer; other inflammatory lung diseases, such as chronic obstructive pulmonary disease (COPD), usually associated to tobacco smoke exposure, acute respiratory distress syndrome (ARDS), cystic fibrosis (CF), asthma and obstructive sleep apnoea; different CVDs and related injuries, including acute myocardial infarction; and other conditions as rheumatoid disease; sepsis; schizophrenia; and notably the allograft rejection following organ transplantation (reviewed in [84] and [216]). All these clinical conditions could benefit from a fast SPME/GC-MS screening. It should be noted, however, that the presence of elevated levels of exhaled ethane and pentane is not a definitive diagnosis because of its presence in such diverse health complications, but an important tool to complement other biochemical analysis and avoid further invasive and expensive diagnostic examinations for patients with negative results in the exhaled breath profile. Specifically with the organ rejection after lung transplantation and schizophrenia, their early diagnose using exhaled breath VOCs could be further improved with the detection of carbon disulfide. This by-product of methionine metabolism is not present in the exhaled breath of healthy individuals, but is found elevated in cases of organ rejection after lung transplantation [217], as well as in patients with schizophrenia [218]. Particularly remarkable and still related with the cancer etiology is the report from Ulanowska et al. [110], showing the utility of SPME to study the chemotherapy effects in lung cancer patients metabolism. This is a very important tool to follow up the outcome of the treatment and eventually adjust its dosage and duration, increasing thereby its efficiency. Shirasu et al. [107] were able to show that the characteristic odor associated to fungating cancer wounds is due to dimethyl trisulfide production by bacteria that infected those fungi. This infection is highly prevalent in many advanced cancer patients, and the data obtained provides new insights toward the treatment of malodor in cancer patients.

Re-exploring the high-throughput potential of METs

colitis, Campylobacter jejuni and Clostridium difficile infections [120]. Garner et al. [121] also identified specific VOCs profiles in children with cholera, and neonates with necrotizing enterocolitis. These examples clearly show the suitability of SPME in the fast diagnosis of different gastrointestinal infections, many of them eventually lethal if not properly diagnosed and treated. Diabetes and CVDs

Fig. 3 Potential biomarkers in different cancer types analyzed by SPME: (a) lung cancer, (b) breast cancer, (c) gastric cancer, (d) liver cancer

Another interesting application related to the diagnosis of a specific type of cancer is the catecholamines quantification using both MEPS and SPME ([193–198]). Catecholamines are hormones produced by the adrenal gland and nerve tissues, neurotransmitters, and metabolic regulators, and their plasmatic and urinary levels are independent of dietary influences. Therefore, the catecholamine-secreting tumors, such as phaeochromocytoma, can be easily detected by the plasma or urinary catecholamines levels [219].

Diabetes is an interesting example of an SPME application. Its global incidence is growing very fast (it was estimated to affect 285 million people, which is more than 6 % of the adult population, in 2010, rising to 430 million people in 2020 [221]). Moreover, the complications associated to diabetes are highly disabling and systemic, including impairment of immune system, periodontal disease, retinopathy, nephropathy, somatic and autonomic neuropathy, cardiovascular diseases and diabetic foot [221]. Several ketones, namely acetone, which is formed during dextrose metabolism, lipolysis or lipid peroxidation, can be used as biomarkers of diabetes mellitus as their levels are increased in the breath of patients with this metabolic disease (reviewed in [84]). In this sense, different reports show the suitability of SPME in extraction of diabetic ketones, allowing a fast diagnosis of diabetes [76, 114, 115]. CVDs present a similar trend to diabetes, but with much higher disability and mortality rates and therefore efficient methodologies to follow its etiology are needed. The examples shown in Table 2 ([127–132], selected chemical structures presented in Fig. 4a) show that both SPME and MEPS have great potential in this subject. Particularly relevant is the possibility that these techniques allow to monitor both the disease progression as well as the drug concentration, enabling an important therapeutic drug monitoring (TDM). Therapeutic drug monitoring

Gastrointestinal Infections Gastrointestinal tract infections are still highly prevalent, particularly in underdeveloped countries. One of these infections is caused by Helicobacter pylori, a bacterium that has been classified as a first group gastric carcinogen [220]. This infection has been associated with gastric cancer, the second most common cancer in the world, by using SPME/GC-MS in the determination of the VOCs excreted by stomach tissue and bacteria culture [102, 116]. Particularly, the authors have shown that 1-propanol and carbon disulfide composition was higher in cancer tissue than in normal tissue. This simple analysis can be routinely used to screen patients who eventually need further diagnosis and treatment [102]. In a similar way, SPME/GC-MS methodology was used in the detection of cholera [117], infectious diarrhoea [118], and ulcerative

TDM is a crucial part of the clinical intervention process, being commonly used to maintain the ‘therapeutic’ drug concentrations. TDM is also useful to identify the causes of unwanted or unexpected responses, to prevent unnecessary diagnostic testing, to improve clinical outcomes, and ultimately, to save lives [222]. Therefore, it is necessary to have an excellent sample preparation procedure for the determination of drug concentrations in different matrices, including blood, urine, saliva, and plasma, as well as totally diverse drugs. The exhaustive collection of reports using SPME and MEPS as extraction methodologies, presented in Table 2, show the potential of these METs in TDM of a broad range of drugs with very different properties, including anti-cancer drugs (Fig. 4b) [73, 133–135], antibiotics [23, 136–143] and antidepressants [77, 80, 144–155] (Fig. 5a and b, respectively),

J. Pereira et al.

analgesic and anti-inflammatory drugs [156–162] and antiepileptic drugs [163–166] (Fig. 6a and b, respectively), psychoactive and stimulant drugs [63, 64, 81, 82, 167–176] and anaesthetic [67, 72, 74, 177–188] (Fig. 7a and b, respectively), immunomodulatory drugs [71, 189] and steroids [190–192] (Fig. 8a and b, respectively), and finally neurotransmitters [193–198] and other unrelated drugs [15, 75, 199–204] (Fig 9a and b, respectively). It would be very exhaustive to describe in detail all these applications and so we will highlight the most notable in terms of TDM importance or technical novelty. This is the case of Said et al. [134], reporting a simple, online, and fast MEPS/LC-MS-MS methodology for the determination of cyclophosphamide (Fig. 4b), one of the most widely used anti-cancer drugs, in human plasma samples. Regarding this, other relevant work was performed with the anti-cancer drug busulphan (Fig. 4b). This drug is used in high doses as preparative regimen before stem cell transplantation, but it has a narrow therapeutic window and under- or overdosing may have a fatal outcome for the patient. Therefore, TDM using on-line MEPS/LC-MS/MS to allow a dose correction is currently used to adjust the exposure to busulphan [73]. Kuriki et al. [199] present a very interesting application of HSSPME/GC-MS for the simultaneous determination of selegiline (Fig. 9b) and desmethylselegiline, two drugs used in Parkinson’s disease treatment, in human body fluids. Cantú et al. [170] reported a simple methodology using SPME/LC, not requiring any special interface, to determine the plasma concentrations of seven anticonvulsants (including phenobarbital and carbamazepines) and four tricyclic antidepressants (TADs), both widely used in psychiatry and epilepsy-related

Fig. 4 Potential biomarkers of CVDs (a) and anti-cancer drugs (b) analyzed by SPME and MEPS, respectively

TDM. Chaves et al. [189] proposed an automated method using a monoclonal anti-interferon α2a (IFN α) antibody ITSPME with adequate analytical performance for the determination of IFN α in plasma samples from patients receiving TDM. This natural therapeutic protein is produced by the immune system against viruses, parasites, and tumor cells and therefore very important in the treatment of immunocompromized patients. Related to the CVD TDM, El-Beqqali et al. [182] reported a MEPS/LC-MS/MS procedure for quantifying acebutolol and metoprolol, two βadrenoceptor-blocking drugs widely used as effective antihypertensive and anti-anginal agents, in plasma and urine samples. In the same direction, Hu et al. [179] used MIPscoated SPME fibers coupled to LC for the determination of propranolol and pindolol, two β-blockers widely applied to hypertension, angina pectoris, and arrhythmia treatment.

Innovative approaches and recent applications using SPME and MEPS A major breakthrough in SPME was certainly the introduction of biocompatible coatings. This has broadened its application to semi-volatile and non-volatile species, usually using the

Fig. 5 Examples of antibiotics (a) and antidepressants (b) analyzed by SPME and MEPS

Re-exploring the high-throughput potential of METs

Fig. 6 Analgesic and anti-inflammatory (a) and anti-epileptic drugs (b) analyzed by SPME and MEPS

direct immersion-mode (DI-SPME) to determine drug concentrations in biological fluids [41]. Using this approach, it was possible to identify several drugs often conjugated in the clinical environment as antidepressants and anticonvulsants in

Fig. 7 Psychoactive and stimulant drugs (a) and local anaesthetics (b) analyzed by SPME and MEPS

Fig. 8 Immunomodulatory drugs (a) and steroids (b) analyzed SPME and MEPS

plasma and urine [77, 146, 148, 170]. Particularly remarkable is the report from Raikos et al. [177], in which he was able to simultaneously identify four anaesthetic and three analgesic

Fig. 9 Neurotransmitters (a) and other unrelated drugs (b) analyzed SPME and MEPS

J. Pereira et al.

drugs in human urine of patients undergoing open heart surgery. Another important feature that is made available for MEPS and SPME applications, although not exclusive to the biomedical field, is the utilization of molecularly-imprinted polymers (MIPs)-coated sorbents, which improves the analytical performance of certain applications. The therapeutic monitoring of trace levels of propranolol and pindolol in serum and plasma [179] and the simultaneous quantification of five anaesthetics (lidocaine, ropivacaine, mepivacaine, and bupivacaine) [184] in plasma and urine using MEPS extraction are only two examples of an exponential increase in the utilization of MIP sorbents in SPME and MEPS, which were already named MISPME [27] and MIMEPS [7]. These two particular sets of SPME and MEPS are still not commercially available, thereby limiting their widespread application in sample extraction. IT-SPME has been gaining popularity and a particular modification, magnetic-IT-SPME, in which a controlled magnetic field is applied, improved the extraction efficiencies of the target analytes acetylsalicylic acid, acetaminophen, atenolol, diclofenac, and ibuprofen over 70 %. This result suggests that the application of a magnetic field may solve the low extraction efficiency of IT-SPME systems [158]. Very promising too is the TFME, also known as 96-blade SPME because of its 96 wellplate format, for high-throughput analysis of biological fluids and ligand– receptor binding studies [56, 62–64, 70, 176], which will improve significantly the automated high-throughput analytical capacity as shown for the benzodiazepines analysis in Table 2 and [63, 64]. Finally, we should refer to the targeted and untargeted metabolomics capacities of METs, particularly those of SPME, which allow a deeper knowledge of human metabolism. In this field, Vuckovic and Pawliszyn [223] presented extensive studies evaluating SPME coatings for untargeted metabolomic profiling, following the saliva VOCs metabolome [124], the urine VOCs metabolome [125], and the Faecal VOCs metabolome [126].

Future trends In vivo SPME in humans will certainly be an exciting new development in the near future. Up to now, there are only in vivo studies performed with dogs (beagles), rats, and fish, first using a SPME fiber inserted in the vein of the animal (in-dwelling catheters) and later a disposable device, which had a hypodermic needle housing the fiber (reviewed in [1, 16, 41]). However, the idea of getting a real-time snapshot of the metabolic fingerprint on a fully functional living organism, including humans, is highly attractive and opens a brand new world in biomarker discovery and therapeutic drug monitoring. This in vivo

pharmacokinetics reflects the real condition at the time of sampling, which is particularly important for some unstable or fast turnover drugs and metabolites [69]. There are also a few other advantages of using in vivo SPME as the reduction of the manipulation of biological samples because there is no blood collection, only a short and direct exposure of the SPME fiber to the blood stream, combining sampling, sample preparation, and sample clean-up into a single and automated procedure [16]. Unfortunately, the invasiveness of the blood sampling associated with in vivo SPME, although is minimized when compared with traditional blood withdrawing, continues to be a major drawback that is preventing its generalization to humans. Although several studies are required before SPME may be applied to routine clinical measurement, namely biocompatibility requirements on the materials used to make the device in order to avoid any toxic reactions [41], the recent commercialization of the in vivo SPME devices by Supelco will facilitate a wider adoption of in vivo SPME in the near future [4], while the anticipated addition of new coating chemistries [13] can further increase the scope and type of applications accessible by in vivo SPME [16]. The second trend in METs that we would like to highlight is the volatile organic compounds (VOCs) profiling. Although currently it is still confined to the research laboratory, in part due to lack of normalization and standardization that results in large variations between studies [84], it is envisaged that in the near future, bedside VOC profiling methodologies will enable rapid characterization of health and diseases, providing crucial information to healthcare practitioners [224]. This is certainly supported by the promising results obtained with several applications as the characterization of putative VOCs biomarkers for different forms of cancer (lung, breast, stomach, liver, prostate, endocrine system, skin, etc.), diseases (diabetes and irritable bowel syndrome), and clinical conditions (chemotherapy monitoring, nasal sinus infections, fungating cancer wounds, Helicobacter pylori, cholera, and infectious diarrhoea detection, described in Table 2). Moreover, VOC profiling through breath analysis is potentially inexpensive, rapid and non-invasive, and its utilization can be envisaged in the detection and monitoring of other clinical conditions, such as chronic lung diseases (COPD, asthma, and cystic fibrosis), haemolysis, and different organ transplant rejection, or even bacterial detection (as in sepsis cases) and determination of antibiotic susceptibility (reviewed in [225, 226]).

Acknowledgments The authors acknowledge the Portuguese Foundation for Science and Technology (FCT) through the Pluriannual base funding (Project PEst-OE/QUI/UI0674/2011) and MS Portuguese Network (REDE/1508/RNEM/2005) for their support.

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