Dried blood spots for monitoring and individualization of antiepileptic drug treatment

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Contents lists available at ScienceDirect

European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps

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Review

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Dried blood spots for monitoring and individualization of antiepileptic drug treatment

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Daniela Milosheska, Iztok Grabnar, Tomazˇ Vovk ⇑

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University of Ljubljana, Faculty of Pharmacy, Aškercˇeva 7, 1000 Ljubljana, Slovenia

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a r t i c l e

i n f o

Article history: Received 30 January 2015 Received in revised form 8 April 2015 Accepted 9 April 2015 Available online xxxx Keywords: Therapeutic drug monitoring Dried blood spots Antiepileptic drugs Clinical validation

a b s t r a c t Therapeutic drug monitoring (TDM) is a multi-disciplinary clinical specialty used for optimization and individualization of drug therapy in the general and special populations. Since most antiepileptic drugs (AEDs) are characterized by pronounced intra- and inter-individual variability, it can be especially valuable as an aid for dosing adjustments in patients with epilepsy. Dried blood spots (DBS) sampling technique is recognized as a suitable alternative for conventional sampling methods as TDM interventions should be applied in the most cost-effective, rational and clinically useful manner. In the present review we summarize the latest trends and applications of DBS in TDM of epilepsy. Quantification of AEDs in DBS was employed in various clinical settings and has been already reported for phenobarbital, phenytoin, valproic acid, clonazepam, clobazam, carbamazepine, topiramate, rufinamide, lamotrigine, 10-hydroxycarbazepine and levetiracetam. The major limitation of the published studies are restricted evaluation of critical parameters such as the impact of spotted blood volume, spot homogeneity and haematocrit effect, limited clinical validation and non-established correlations between the DBS and plasma concentrations of AEDs. Standardization of critical technical aspects for appropriate sampling, sample preparation and validation of the analytical procedures for quantification of the drugs, as well as appropriate interpretation of the results are the fields which should get more attention in upcoming studies. Limited data on clinical validation and the fact that this technique has been used in practice only for a few AEDs makes the routine implementation of TDM of AEDs using DBS method a big challenge that should be faced by the pharmaceutical scientists in the future. Ó 2015 Published by Elsevier B.V.

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Contents 1. 2. 3.

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4.

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5. 6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Therapeutic drug monitoring in epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DBS as an alternative matrix for TDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. DBS specimen collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Comparison of DBS and plasma samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1. Blood to plasma ratio and haematocrit effect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. DBS as an alternative for saliva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Advantages and disadvantages of DBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of AEDs in DBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Sample processing and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Analytical methods for TDM of AEDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regulatory aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Clinical benefits of TDM of carbamazepine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Adjustment of lamotrigine dosing in pregnancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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⇑ Corresponding author at: Faculty of Pharmacy, University of Ljubljana, Aškercˇeva 7, 1000 Ljubljana, Slovenia. Tel.: +386 14769550; fax: +386 1 4258 031. E-mail address: [email protected] (T. Vovk). http://dx.doi.org/10.1016/j.ejps.2015.04.008 0928-0987/Ó 2015 Published by Elsevier B.V.

Please cite this article in press as: Milosheska, D., et al. Dried blood spots for monitoring and individualization of antiepileptic drug treatment. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.04.008

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Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

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1. Introduction

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Therapeutic drug monitoring (TDM) is a multi-disciplinary clinical specialty aimed at improving patient care by individually adjusting the dose of drugs for which clinical experience or clinical trials have shown it improved outcome in the general or special populations. It can be based on a priori demographic, clinical and pharmacogenetic information, and/or on the a posteriori measurements of drug blood concentration (pharmacokinetic monitoring) and/or biomarkers (pharmacodynamic monitoring) (IATDMCT, 2013). Antiepileptic drugs (AEDs) are a group of drugs used to decrease the frequency and/or severity of seizures in people with epilepsy and are characterized with extensive pharmacological and structural diversity (Bromfield et al., 2006). The rationale of employment of TDM in everyday clinical practice depends on their pharmacokinetic properties, especially on their intra- or interindividual variability. Thus, TDM was initiated for a number of AEDs and used to establish optimal therapy regimens for individual patients (Patsalos et al., 2008). First generation of AEDs including phenytoin, phenobarbital, carbamazepine and valproic acid is characterized by pronounced inter-individual variation in pharmacokinetics and a narrow therapeutic range (Johannessen and Tomson, 2002). For these AEDs TDM has been a common practice to guide dosage adjustment for a particular patient to achieve a serum drug concentration within the reference range at which most patients are expected to exhibit an optimal clinical response (Patsalos et al., 2008). On the other hand, there is some uncertainty about the utility of TDM regarding the second and third generation of AEDs which entered the market between 1990 and 2012. These drugs are characterized by more predictable pharmacokinetics and a substantial lack of documented correlation between drug concentration and drug effects (Johannessen and Tomson, 2006; Patsalos and Berry, 2012). Despite these characteristics TDM is a tool that can guide clinicians to provide effective and safe antiepileptic therapy in individual patient, to verify the drug compliance and to prevent and manage drug interactions, overdoses and toxicity (Patsalos et al., 2008). Collecting biological samples for drug concentration measurements is the key component for effective TDM (Gross, 2001). In clinical practice, AED concentration measurements are usually performed in serum or plasma. Additionally, samples of whole blood, saliva, dried blood spots, tears, hair, sweat, cerebrospinal fluid and breast milk have been investigated (Johannessen and Landmark, 2008). Nowadays the idea of using an alternative specimen employing non-invasive and patient friendly techniques is becoming even more attractive with the development of sensitive analytical methods. Alternative specimens, appropriate for TDM, that are simple for collection from a patient perspective are saliva and DBS (Krasowski and McMillin, 2014). DBS sampling, where blood is obtained via a finger-prick by the patients themselves or by medical personnel could be a convenient replacement for venous blood sampling for most AEDs. The use of DBS has been extensively discussed in the literature (Edelbroek et al., 2009; Li and Tse, 2010; Spooner et al., 2009). Feasibility of using DBS as a sampling technique has been thoroughly reviewed recently. However, so far none of these reviews has focused specifically on the use of DBS sampling for TDM in epilepsy. In this paper we review the application of DBS as an alternative for venous and saliva samples for TDM of AEDs. We discuss the

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advantages, restrictions and key technical aspects that are relevant for practical employment of DBS method in everyday clinical practice compared to the conventional sampling techniques. We also review the published literature on existing DBS analytical methods for AEDs and discuss clinical implications and future perspectives of the implementation in TDM of epilepsy.

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2. Therapeutic drug monitoring in epilepsy

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TDM in epilepsy is very complex. Due to the episodic nature of the condition assessment of the clinical efficacy of AEDs is especially challenging as it is difficult to assess if the patient is responding to the therapy or is just free of seizures. Additionally, many times there are difficulties to differentiate clinical symptoms and signs of toxicity. The main assumption in TDM is that clinical effects correlate better with the drug concentration than with the dose (Patsalos et al., 2008). Initially TDM was employed in clinical practice for the first generation of AEDs due to the complex and variable pharmacokinetics (Neels et al., 2004). The main pharmacokinetic parameters of AEDs are given in Table 1. Phenytoin is one of the earliest examples of drugs for which TDM is essential because of its narrow therapeutic window, high degree of protein binding, and nonlinear pharmacokinetics (Richens, 1979). Moreover, valproic acid is among AEDs most frequently reported for intoxications and monitoring of free valproic acid concentration can be helpful in identifying concentration related adverse effects (Bronstein et al., 2011). Carbamazepine, ethosuximide, phenytoin, and primidone are also considered good candidates for TDM since, in general, the first generation AEDs have significant inter-individual variability in pharmacokinetics and a narrow therapeutic window with toxicity and neurological side effects being a common problem (Perucca, 2005). Inter-individual and intra-individual variability in pharmacokinetics is a result of genetic factors, patient age, specific physiological conditions, associated diseases and drug–drug interactions. Genetic polymorphisms in drug metabolizing enzymes are of particular relevance for the first generation of AEDs. Genetic polymorphisms have been identified for cytochrome P450 (CYP) 2C9 and CYP 2C19, enzymes that are crucial to explain the variability in drug response (Johannessen and Landmark, 2010; Perucca, 2005; Shastry, 2006). Additionally, carbamazepine, phenobarbital, primidone and phenytoin are strong inducers of liver drug-metabolizing enzymes, whereas valproic acid is an inhibitor of multiple CYP enzymes. As a result of pharmacokinetic interaction, serum concentrations of concomitant drugs can be decreased or increased (Johannessen and Landmark, 2010; Perucca, 2005). Most of these drugs are highly bound to plasma proteins. Consequently, monitoring of free plasma drug concentration is preferred, especially in clinical situations where protein binding is disturbed (Dasgupta, 2007; Jansen et al., 2012). Generally, the second generation of AEDs has more favourable pharmacokinetics, wider therapeutic range, reduced interaction profile, better tolerability and fewer adverse effects than the first generation. The strongest evidence for routine TDM is for lamotrigine, oxcarbazepine (10-hydroxycarbazepine metabolite), stiripentol, tiagabine, and zonisamide, mainly due to inter-individual variation in clearance. Pharmacokinetic interactions involving second generation of AEDs include the enzyme inhibitors felbamate, rufinamide, and stiripentol and the weak enzyme inducers oxcarbazepine and topiramate (Johannessen and Landmark, 2010).

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Table 1 Pharmacokinetic data and reference ranges of antiepileptic drugs. Antiepileptic drug

Reference range (mg/L)

Oral bioavalibility (%)

Time to ss (h)

tmax (h)

t1/2 (h)

Vd (L/kg)

Protein binding (%)

Main route of elimination

10–40

100

12–24

2–4

70–140

0.5–1

50

Phenytoin

10–20

>80

5–17

1–12

30–100

0.5–1

90

Primidonea,g

5–10

>90

2–4

2–4

7–22

0.6–0.8

10

Ethosuximidea,h

40–100

>90

7–10

1–4

40–60

0.62–0.72

0

Carbamazepinea,i,j

4–12

75–85

2–4

2–9

8–20

0.8–2

75

Valproic acida,k,l

50–100

>90

2–4

1–7

12–16

0.14–0.23

90

Clonazepama,m,n

0.02–0.07

>80

3–10

1–4

17–56

1.5–4.4

85

Clobazama,o,p

0.03–0.3

>95

7–10

1–3

10–30

1.4–1.8

85

Extensively hepatic. Oxidation (CYP2C9, CYP2C19 and CYP2E1) Extensively hepatic. Oxidation (CYP2C9 and CYP2C19) Extensively hepatic. Oxidation (CYP2C9 and CYP2C19). Phenobarbital and phenylethylmalonamide are major active metabolites Extensively hepatic. Oxidation (CYP3A, CYP2E and CYP2B/C) Extensively hepatic. Oxidation (CYP3A4 and CYP2C8). Carbamazepine-epoxide is pharmacologically active metabolite Extensively hepatic. Glucuronidation, beta oxidation and cytochrome P450 (CYP) mediated oxidation Extensively hepatic. Oxidation (CYP3A4). 7-amino metabolite retains some pharmacological activity Extensively hepatic. Oxidation (CYP3A4 and CYP2C19). N-desmethyl metabolite contributes to activity

Second generation Vigabatrinea,q,r,s Lamotriginea,t,u

0.8–36 2.5–15

P60 P95

1–2 3–6

1–2 1–3

5–8 15–35

0.8 0.9–1.3

0 55

Gabapentina,v,w Felbamatea,x,y

2–20 30–60

<60 >90

1–2 3–4

2–3 2–6

5–9 16–22

0.65–1.4 0.7–1

0 25

Topiramatea,z,aa,ab

5–20

P80

4–5

2–4

20–30

0.6–0.8

15

Tiagabine Oxcarbazepinea,ad,ae,af

0.02–0.2 3–35

P90 90

1–2 2–3

0.5–2 3–6

5–9 8–15

0.75–0.85 0.7

96 40

Levetiracetama,ag,ah,ai Pregabalina,aj Zonisamidea,ak,al

12–46 0.9–14.2 10–40

P95 P90 665

1–2 1–2 9–12

1 1–2 2–5

6–8 5–7 50–70

0.7 0.5 1.45

10 0 40–60

Rufinamideam,an,ao

30–40

80

1–2

4–6

6–10

0.7–1.1

26–35

Stiripentolam,ap,aq,ar

4–22

ND

1–3

0.5–2

4.5–13

1–1.3

99

Third generation Lacosamideam,as,at Eslicarbazepineam,as Retigabineam,as,au

10–20 5–35 NE

100 >90 60

3–4 2–4 2–3

1–2 2–3 0.5–2

12–16 9–10 6–10

0.6–0.7 2.7 2–3

<15 <40 80

Perampanelav,aw,ax,ay

NE

100

14

0.5–2.5

66–90

1.1

95

First generation Phenobarbitala,bc a,d,e,f

a,ac

⁄

Renal excretion Extensively hepatic. Glucuronide conjugation (UGT1A4 and UGT1A9) Renal excretion Renal excretion and oxidation (CYP3A4 and CYP2E1) Renal excretion and oxidation (CYP isoenzymes) Extensively hepatic. Oxidation (CYP3A4) Hepatic. Keto reduction followed by glucuronidation of the active monohydroxy derivative Renal excretion and hydrolysis Renal excretion Extensively hepatic. Acetylation, reduction (CYP3A4) and glucuronidation Extensively hepatic. Hydrolysis and glucuronidation Extensively hepatic. Oxidation (CYP1A2, CYP2C19 and CYP3A4), hydroxylation, Omethylation and glucuronidation 60% hepatic. Demethylation (CYP2C19) Hepatic. Glucuronidation 60% Hepatic. Acetylation and glucuronidation Extensively hepatic. Oxidation (CYP3A)

ND – not determined. ⁄⁄ NE – not established. a Patsalos et al. (2008). b Nelson et al. (1982). c Wilensky et al. (1982). d Neuvonen (1979). e Richens (1979). f Nakajima et al. (2002). g Gallagher et al. (1972). h Buchanan et al. (1973). i Rawlins et al. (1975). j Eichelbaum et al. (1985). k Cramer et al. (1986). l Perucca et al. (1978). m Kaplan et al. (1974). n Berlin and Dahlstrom (1975). o Rupp et al. (1979). p Aucamp (1982). q Rey et al. (1992). r Richens (1991). s Haegele et al. (1988). t Rambeck and Wolf (1993). u Biton (2006).

Please cite this article in press as: Milosheska, D., et al. Dried blood spots for monitoring and individualization of antiepileptic drug treatment. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.04.008

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Gidal et al. (2000). Vollmer et al. (1986). Shumaker et al. (1990). Thompson et al. (1999). Patsalos (1999). Sachdeo et al. (1996). Britzi et al. (2005). Gustavson and Mengel (1995). Patsalos et al. (1990). McKee et al. (1994). May et al. (2003). Radtke (2001). Patsalos (2004a). Patsalos (2004b). Ben-Menachem (2004). Buchanan et al. (1996). Mimaki (1998). Patsalos and Berry (2013). Perucca et al. (2008). Wheless and Vazquez (2010). Chiron (2007). Levy et al. (1983). Moreland et al. (1986). Patsalos and Berry (2012). Doty et al. (2007). Ferron et al. (2002). Shih et al. (2013). Krauss (2013). Gidal et al. (2013). Summary of product characteristics, FycompaÒ, 2012.

Moreover, lamotrigine concentrations are also affected by the first generation of AEDs and other drug classes, such as oral contraceptives. The second generation of AEDs has less potential for pharmacokinetic interactions than the first generation. However, they are susceptible to interactions and are often used as adjunctive therapy with the first generation of AEDs (Johannessen and Landmark, 2010). For the remaining drugs from the second generation TDM may be clinically useful to assess compliance or to adjust dosing in hepatic or renal failure. Future research is needed to better define the reference ranges and to better document the value of TDM in clinical practice (Krasowski, 2010). The third generation of AEDs was recently introduced into the market and their overall impact on epilepsy treatment has yet to be determined. Lacosamide, retigabine and perampanel have novel mechanisms of action, while esclicarbazepine acetate is a pro-drug and is closely related to carbamazepine and oxcarbazepine. Generally, AEDs from this generation are characterized with linear pharmacokinetics, rapid absorption, undergo hepatic metabolism and have low to moderate drug–drug interaction profile (Brown and El-Mallakh, 2010; Patsalos and Berry, 2012; Shih et al., 2013). Indications for monitoring these drugs include patients with renal and/or hepatic impairment, pregnancy, suspected toxicity and non-compliance (Asconape, 2014; Perucca, 2000; Tomson et al., 2013). Moreover, when enzyme-inducing AEDs are introduced or withdrawn from patients on perampanel and eslicarbazepine acetate, patients should be closely monitored for their clinical response and tolerability and dose adjustment may be necessary (Clinical pharmacology review FycompaÒ, 2012; Patsalos et al., 2008). The value of TDM in management of patients prescribed lacosamide, retigabine, eslicarbazepine acetate or perampanel is not known and needs to be investigated in future (Krasowski and McMillin, 2014; Patsalos and Berry, 2013). Even though there are no controlled randomized studies demonstrating the positive impact of TDM on clinical outcome in epilepsy, experiences from nonrandomized investigations and clinical practice demonstrate clearly that TDM of all generations of AEDs may be beneficial, if used properly (Johannessen and Landmark, 2008; Tomson et al., 2007). In practice, optimization of AEDs therapy can be best guided by identification of the

individual specific therapeutic concentration range (Perucca, 2005). This concept can be established by determining, preferably on at least two separate occasions at steady state, the drug concentration in a body fluid, once a patient has been stabilized. Furthermore, reported reference range for a particular drug is a range of drug concentrations quoted by the laboratory and can be used by clinicians as a guide for therapeutic response or toxicity. It must be emphasized that this is not necessarily a therapeutic range which is associated with the best achievable response in a specific patient. Establishing reference ranges, especially for the newer AEDs is challenging due to the wide range of drug concentrations that are associated with the successful clinical management of seizures. Determination of individual therapeutic concentrations based on good seizure control is the best option for the patient, in particular when dosing requirements can change due to physiological alterations or changes in the therapy which can alter AEDs pharmacokinetics (Patsalos et al., 2008). Precise and accurate laboratory measurement of the drug concentration in biological fluid is an essential part of the TDM. Conventionally, determination of drug concentration for TDM is performed in blood plasma or serum, obtained by venipuncture by phlebotomists. Frequently, conducted studies pointed that pre- and post-analytical errors related to TDM such as incorrect timing of sample collection and misguided interpretation of concentration measurements are common, thus reducing the clinical benefit of TDM in epilepsy (Krasowski and McMillin, 2014). Nevertheless, this does not minimise the importance of the analytical method used. The quantitative TDM testing is performed using both, commercially available tests and analytical methods developed in the laboratory for specific purpose. The most commonly used are immunoassays and chromatographic techniques (Johannessen and Landmark, 2008; Neels et al., 2004; Warner et al., 1998). Published analytical methods for TDM of AEDs employing plasma or serum are extensively reviewed elsewhere (Chollet, 2002; Kang et al., 2011). DBS and saliva are recognized as possible substitutes for widely used blood samples in TDM (Krasowski and McMillin, 2014). TDM of AEDs by the use of saliva has been already reviewed elsewhere (Liu and Delgado, 1999; Patsalos and Berry, 2013). Other

Please cite this article in press as: Milosheska, D., et al. Dried blood spots for monitoring and individualization of antiepileptic drug treatment. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.04.008

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specimens such as tears, hair, sweat, cerebrospinal fluid and breast milk have also been investigated for AEDs determinations but from a practical point of view are not appropriate for routine TDM (Chen et al., 2010; Johannessen and Landmark, 2010; Patsalos and Berry, 2013). TDM interventions should be applied in the most cost-effective, rational and clinically useful manner (Touw et al., 2005). Therefore, DBS sampling seems to be a useful technique in comparison with the conventional methods.

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3. DBS as an alternative matrix for TDM

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3.1. DBS specimen collection

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DBS sampling is a relatively simple sample collection technique where a small volume of capillary blood obtained by finger or heel prick is applied on a special paper card. Various DBS cards composed of non-cellulose or cellulose (filter paper) matrix of specific pore size and thickness are commercially available such as Whatman 903, Ahlstrom 226, Whatman FTA DMPK-A,B,C, Whatman 31 ET CHR, Whatman 3, and Agilent Bond Elut DMS. Selection of the DBS card is based on the type of analytical requirements (Sharma et al., 2014; Wilhelm et al., 2014). There are some practical recommendations in order to provide representative DBS samples for quantitative analysis. At first, skin area for sampling should be warmed or massaged in order to stimulate circulation and then cleaned with 70% isopropyl alcohol. After drying, skin is pricked with a sterile, disposable lancet designed to deliver a controlled, uniform puncture that stimulates sufficient capillary blood flow with minimal injury. First blood drop should be wiped away because it contains higher proportion of interstitial fluid. Subsequent drop is then absorbed into the filter paper without any direct contact with the paper. The entire circle should be uniformly saturated with blood (Mei et al., 2001). Alternatively, capillary blood can be collected into a capillary tube containing anticoagulant. Blood is drawn up into the capillary via capillary action and then dispensed onto a filter paper using a suction bulb (Spooner et al., 2009). Optionally DBS can be obtained also from a venous blood collection tube by pipetting venous blood onto the DBS card. It is important to slowly expel blood from the pipette tip and touch the drop to the paper, allowing the blood to absorb. Following sample collection, DBS cards are usually dried at room temperature for a minimum of two hours and then stored until analysis or sent to the laboratory. Collected samples must be thoroughly dried prior to the storage as moisture may harm the sample by encouraging bacterial growth or altering its elution time (Mei et al., 2001; Shipping Guidelines for Dried-Blood Spot Specimens, 2012). Cards are usually sealed in a gas-impermeable zip-lock bag containing a desiccant pack and humidity indicator. In general, one card in a properly labelled bag is stored under previously defined storage conditions based on the analyte, the assay, and the paper used for collection. However, for long term storage (>90 days), temperature under 20 °C is recommended. Although DBS sample collection is relatively simple, adequate training is necessary to minimize potential sources of error. Potential errors with DBS sample handling are improper placement of the whole blood drop on the DBS card, variation in blood spot size, unsettled stability and shipment issues (McDade et al., 2007). Collected DBS samples must be of adequate quality and must meet some appearance criteria in order to be considered suitable for analysis. Clotting, layering or supersaturating should be prevented. The marked circle on the DBS card must be homogenously and symmetrically filled and both sides of the card must show the same colour. Samples indicating contamination, haemolysis, or insufficient volume are not considered for further

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5

analysis (Edelbroek et al., 2009; Li and Tse, 2010; Mei et al., 2001). DBS sample should be homogeneous in terms of both the volume of spotted blood and the analyte contained within it, and the blood spot area is expected to be directly proportional to the volume of blood spotted on the card with no change in the density of the blood or analyte per unit area (Cobb et al., 2013). A guideline for blood collection techniques and specifications for Food and Drug Administration (FDA) approved filter paper for new-borns screening programs was recently published (Clinical Laboratory and Standard Institute, 2013).

325

3.2. Comparison of DBS and plasma samples

335

The goal of any drug concentration measurement in a whole blood, plasma, or serum sample is to acquire the value which will precisely reflect the drug concentration under in vivo conditions at the time of sampling. Hence, the choice of the appropriate assay matrix should be rationally based (Hinderling, 1997). Obtained capillary blood in DBS samples is a mixture of venule, arteriole and capillary blood, as well as intracellular and interstitial fluids (Blumenfeld et al., 1977). Because of the arterial component, generally, there may be differences in the measured drug concentrations when comparing venous and DBS samples (Chiou, 1989). Whole blood and DBS samples contain a considerably different subset of proteins than plasma or serum samples due to the presence of the erythrocyte proteome (Chambers et al., 2013). Additionally, when a drop is applied directly onto the filter paper, sample does not contain anticoagulant while standards and quality controls used for method development and validation, usually do since blood is initially collected into a blood tube containing anticoagulant and then spotted onto the card with a pipette. In such cases use of anticoagulant is necessary for appropriate spotting. It is suggested to use ethylenediaminetetraacetic acid (EDTA) because of its advantages over heparin in its mixing and drying abilities along with calcium dependent phospholipases and ester hydrolases inhibition. EDTA may cause interference in quantitative analysis but matrix-related irreproducibility appeared to be more pronounced with heparin than with EDTA (Li et al., 2011b; Sadagopan et al., 2003; Sharma et al., 2014).

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3.2.1. Blood to plasma ratio and haematocrit effect TDM uses reference ranges that are traditionally defined for plasma or serum samples. Concentrations obtained by DBS technique represent drug concentrations in the whole blood. Therefore, the relationship between the plasma and DBS concentration has to be established for appropriate clinical interpretation of the DBS concentrations (McDade et al., 2007). The drug is in the blood distributed between the plasma and blood cells (Fig. 1).

362

Fig. 1. Drug partitioning in plasma and blood cell. Unbound drug in plasma is in equilibrium with the unbound drug in blood cells. Additionally, equilibrium occurs between bound and unbound drug concentration in plasma and blood cells. CB – blood concentration, CuPL – unbound plasma concentration, CbPL – bound plasma concentration, CuBC – unboud blood cell concentration, CbBC – bound blood cell concentration.

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The concentration ratio between these two compartments is determined by the blood cell to plasma partition coefficient (Eq. (1))

K BC=PL ¼

C BC C PL

ð1Þ

where KBC/PL is the blood cell to plasma partitioning and CBC and CPL are the concentrations in blood cells and plasma, respectively. The KBC/PL depends on cell permeability for the drug and binding of the drug to the plasma proteins and cell constituents (Fig. 1). Besides the partition process volumes of the plasma and blood cells can significantly influence drug concentration in plasma and whole blood. Haematocrit (Hct) which is the volume percentage of the blood cells in the blood varies among the population and is normally from 0.41 to 0.51 in men and 0.37 to 0.47 in women (Li and Tse, 2010). However, Hct values can be significantly different in specific populations, e.g. neonates, children, and people living at high altitudes. Additionally, Hct is changed in specific conditions such as polycythaemia and chronic obstructive pulmonary disease and in patients receiving cytotoxic drug therapy, i.e. chemotherapy. According to De Kessel et al. 99.5% of the hospital population will have Hct between 0.19 and 0.63 (De Kesel et al., 2013). This large variation of Hct values makes it one of the key elements for appropriate interpretation of DBS concentrations. Drug concentration in the blood (CB) can be generally expressed with Eq. (2) which shows that the concentration in blood and plasma will be in most cases different except if concentration in blood cell is comparable with concentration in plasma.

CB ¼

C PL  V PL þ C BC  V BC VB

ð2Þ

If we introduce Eq. (3) describing the relationship between the volume of plasma (VPL), volume of blood (VB) and Hct into the Eq. (2), we get the relation between plasma and blood concentration (Eq. (4)) which enables the conversion of whole blood concentration (DBS) to plasma concentration.

V PL ¼ V B  ð1  HctÞ CB C PL ¼ ð1  HctÞ þ K BC=PL  Hct

ð3Þ ð4Þ

For drugs with very low values of blood cell to plasma partitioning (KBC/PL < 0.1), the product KBC/PL  Hct can be neglected and the association between plasma and blood concentration is dependent only on the Hct value. By insertion of the relationship between the unbound and total drug concentration in plasma (Eq. (5)), where f uPL is the unbound drug fraction in plasma u

C uPL ¼ f PL  C PL

ð5Þ

one can express KBC/PL as Eq. (6), where q is the ratio of the drug blood cell concentration to unbound drug concentration in plasma.

K BC=PL

C BC u u ¼ u  f PL ¼ q  f PL C PL

ð6Þ

Ratio between the whole blood (DBS) and plasma drug concentration (R) can be then expressed by rearrangement of Eq. (4) taking into account the influence of drug binding in plasma (Eq. (7)).

R¼

CB u ¼ 1 þ Htc  ð f PL  q  1Þ C PL

ð7Þ

Although, only unbound or free drug is pharmacologically active, usually in practice, the total plasma concentration is measured because it is directly proportional to free drug concentration when the free fraction in plasma is constant. Likewise, total blood drug concentration is proportional to free drug concentration when free fraction in plasma, Hct and blood cell partitioning are

constant. Both plasma/serum and whole blood samples represent total drug concentration. Thus, in such situations either plasma or DBS can be used for drug quantification. However, for those drugs that enter blood cells and bind to plasma proteins and blood cell constituents, it is useful to know which of the parameters (f uPL or q) has a more pronounced influence on R. In situations where there is minimal variability in free fraction and blood cell partitioning is low, than either plasma or DBS can be used. The blood to plasma concentration ratio (Eq. (7)) is a useful metric to evaluate the situation. For many drugs with high affinity for plasma proteins, which not or minimally partition into erythrocytes, R tends towards the lower limit of 0.55–0.60 (1  Hct). In this case blood cells dilute the plasma drug concentration and Hct value is important, if DBS is used. Therefore, in these situations the variability in Hct should be taken into account and understanding of variation in plasma protein binding is advised. In cases where R is much larger, DBS sampling is preferable over plasma because of the haemolysis concerns with preparation of plasma samples (Emmons and Rowland, 2010). When R is larger than 2.0, measuring concentrations in whole blood or erythrocytes rather than in plasma (or serum) increases the sensitivity of an assay with a given lower limit of quantification. Assaying concentrations in whole blood, rather than in plasma or serum, should be also considered with drugs showing significant temperature or pH dependent red blood cells partitioning and for drugs which are metabolized by enzymes in red blood cells. In this case drug plasma concentration depends on the conditions during sample handling and specific protocols should be provided with specified time to centrifugation, temperature, and pH conditions, as well as suitable enzyme inhibitors (Hinderling, 1997). Overall, DBS is more suitable sampling method in comparison to plasma for drugs with high affinity for erythrocytes (Sharma et al., 2014). This can be of particular relevance for zonisamide that has the erythrocyte to plasma ratio of 15 at low concentrations and about 3 at higher concentrations (European public assessment reports ZonegranÒ, 2008; Nishiguchi et al., 1992). Similarly, the blood to plasma ratio for topiramate decreased from 8 to 2 as its concentration increased, which indicates a considerable and saturable binding of topiramate to erythrocytes (Shank et al., 2005). Erythrocytes to plasma concentration ratio of carbamazepine is 1.09 (Bonneton et al., 1992), consequently whole blood (DBS) and plasma drug concentrations are similar. Also the 10-hydroxycarbazepine which is the main pharmacologically active form of oxcarbazepine has blood to plasma and erythrocyte to plasma concentration ratio 1.25 and 1.1, respectively (Jung et al., 1997; Theisohn and Heimann, 1982). On the other hand, valproic acid, tiagabine and perampanel are strongly bound to plasma proteins and have minimal permeability into blood cells. Consequently, their value of R is low, 0.64, 0.65 and 0.55–0.59, respectively (Bioequivalence Review Tiagabine, 1997; Clinical pharmacology review FycompaÒ, 2012; Han et al., 2012).

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3.3. DBS as an alternative for saliva

486

Usually, total drug concentration is measured for TDM. Due to the equilibrium between bound and free fraction of the drug, free drug concentration can be predicted from total drug concentration measurement. However, in conditions like uraemia, liver disease and hypoalbuminaemia where this equilibrium is disturbed, the measured free drug concentration can be significantly higher than the one estimated from the measurement of total drug concentration. This can lead to drug toxicity even if the measured total concentration fits the therapeutic range, particularly for strongly protein bound drugs (Dasgupta, 2002). In such clinical situations saliva can be the matrix of choice. Theoretically, saliva is an ultrafiltrate of plasma. Drug concentration measurement in saliva can

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be therefore considered as a substitute for the free drug concentration in plasma (Patsalos and Berry, 2013). Saliva is an attractive matrix of increasing utility, but it is suitable for TDM only for drugs that accumulate in saliva and are unionized within the salivary pH range (Ruiz et al., 2010). Additional advantage of saliva over DBS is the non-invasive sample collection. Many patients are resistant to finger prick. In comparison with DBS, saliva sampling is not as cost effective because of the need of commercial kits for collection of the saliva. Some of them include buffer and preservatives in order to provide better stability of the drug sample. Moreover, buffer dilutes already low concentration of the drug and higher volume of the collected sample or more sensitive analytical methods are needed for TDM (Patsalos and Berry, 2013). Additionally, TDM based on measurements in saliva can be less reliable due to the presence of drug residues in the mouth or leakage of drug rich exudate, particularly in patients with gingivitis. To minimize contamination from drug residues it is recommended to collect saliva before, or a few hours after dosing (Patsalos et al., 2008). The salivary flow rate, pH, sampling conditions and other pathophysiological factors may influence the concentration of drug in saliva (Dasgupta, 2007). Other factors that affect saliva drug concentrations include molecular weight of the drug, its pKa, lipid solubility, and salivary metabolism (Liu and Delgado, 1999; Patsalos and Berry, 2013; Ruiz et al., 2010). Due to complex sample preparation, stability of the salivary samples is questionable, particularly when collected and mailed to the laboratory by the patient. Even though, the stability of salivary samples of lamotrigine, levetiracetam, oxcarbazepine, topiramate and zonisamide when mailed was demonstrated, further studies are needed to assess the ability of obtaining adequate samples (Jones et al., 2005). AEDs that are highly bound to plasma proteins such as phenytoin, carbamazepine and valproic acid require monitoring of free plasma drug concentration especially in clinical situations where protein binding is disturbed (Dasgupta, 2007). Free drug concentration measurement is relevant also for some of the new AEDs, including perampanel, stiripentol and tiagabine (Krasowski, 2010). The literature data show that salivary TDM can be applied to optimize treatment of epilepsy with carbamazepine, clobazam, ethosuximide, gabapentin, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, primidone, topiramate, and zonisamide. On the other hand, salivary TDM of valproic acid is probably not helpful, whereas for clonazepam, eslicarbazepine acetate, felbamate, pregabalin, retigabine, rufinamide, stiripentol, tiagabine, and vigabatrin, the data are sparse or nonexistent (Patsalos and Berry, 2013). It should also be noted that free drug concentration monitoring of AEDs is not a routine procedure in clinical settings and that there is a lack of established reference concentration ranges for free drugs. 3.4. Advantages and disadvantages of DBS Compared to conventional liquid blood samples DBS samples have several advantages including small sample volume (10– 50 ll), no need of phlebotomist, simpler sample handling (no need for centrifuges and homogenizers), sample storage and cost-effective shipment by regular mail (no requirements of cold chain during transportation and storage) (Amsterdam and Waldrop, 2010). Advantages and disadvantages of DBS samples over traditional venous samples are summarized in Table 2. The major drawback of venipuncture in comparison to DBS is that of being an invasive and painful technique. Although, pricking is somehow an invasive procedure, it was shown to be more ‘‘patient-friendly’’, better tolerated and relatively less painful than venipuncture. The DBS method as a minimally invasive alternative to venous blood sampling facilitates the collection of blood

Table 2 The advantages and disadvantages of DBS sampling for TDM of AEDs. Advantages

Comment

Simple and minimally invasive sampling

Finger or heel prick is generally more ‘‘patient-friendly’’, better tolerated and relatively less painful than venipuncture. No need of phlebotomist, allows collection of blood samples in home-based settings Allows providing blood samples for more frequent monitoring and dose adjustments. Applicable in specific clinical situations involving newborns, pregnant women or critically ill patients Adsorption and the solid nature of DBS make analytes typically less reactive than in liquid state. Chemically coated sample collection cards are available for compounds susceptible to enzymatic degradation No need for centrifuges and homogenizers. Sample storage and cost-effective shipment by regular mail without requirements of cold chain

Small sample volume (10–50 ll)

Potential increases in sample stability

Simple sample preparation and low cost of transportation

Disadvantages Need for highly sensitive analytical techniques Complex method development

Lack of specific regulatory guidelines for the quantitative determination of drugs in DBS Need for clinical validation

Comment Limited amounts of sample material and matrix complexity Evaluation of the selection of the DBS card, internal standard addition, spotting and punching device, site of punching and haematocrit, volume spotted, spot homogeneity, spot to spot carry over, recovery, as well as stability of the samples and storage conditions Difference in matrix type between DBS and conventional samples makes FDA and EMA guidelines insufficient for complete validation Non-established correlations between DBS and plasma concentrations of AEDs

samples in home-based settings, by non-medically trained personnel. This is especially important for TDM and population studies because it allows providing blood samples more frequently and at various time points following dosing (Ostler et al., 2014). Additionally, DBS is especially valuable in specific clinical situations involving new-borns, pregnant women or critically ill patients, where from the ethical view point small volume blood samples are recommended. DBS technique provides a totally different dimension in stabilizing unstable compounds because when the blood spot is dried, the enzymes responsible for instability lose their activity. Adsorption and the solid nature of DBS make analytes typically less reactive than in liquid state (D’Arienzo et al., 2010). In addition, chemically coated sample collection cards, such as Whatman FTA-DMPK A and B cards are available to aid analyte stability for compounds susceptible to enzymatic degradation. These cards contain chemical additives that lyse bacterial cells, inactivate pathogens, denature enzymes and proteins, and reduce health hazards. Analyte stability can be additionally influenced by drying time and drying conditions. Recently, a novel technique for sample stabilization by rapid high-temperature heating was proposed (Blessborn et al., 2013). Moreover, in-house modified DBS card was developed and demonstrated to be able to stabilize compounds which require stabilization at a low pH by rapidly lowering the pH of the spotted blood sample during the drying process (Liu et al., 2011). Generally,

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when samples are packed in sealed bags with desiccant packets, DBS provides prolonged stability at ambient conditions for most of the drugs (Li et al., 2011b). Therefore, the stability of many unstable molecules can be improved. Additionally, it has been reported that DBS technique is suitable for drugs which are susceptible for photo-degradation. However, they require special handling. DBS technique is not suitable for volatile and air sensitive compounds and some unstable metabolites (Bowen et al., 2012). From a safety point of view, DBS cards are less bio-hazardous sample than plasma or whole blood (Bond et al., 1981; Evengard et al., 1988). However, when collecting and preparing DBS spot specimens for shipment, standard precautions must be taken (Guide to infection prevention, 2014). Proper packaging and labelling of the packaging contents is a must, especially when shipping specimens known to contain an infectious agent (Shipping Guidelines for Dried-Blood Spot Specimens, 2012). The decision to employ DBS technology for a given study needs to be considered on a case by case basis with an understanding of compound specific metabolism characteristics and clinical study design because there is a variety of compound specific stabilization techniques and approaches (Bowen et al., 2012). Most quantitative assays in DBS rely on the assumption that ‘‘a filter paper disc punched from a blood-filled circle provides a volumetric measurement that is similar to liquid measuring devices’’ (Mei et al., 2001). Although, DBS technique offers many advantages over plasma or serum samples, the use of DBS is associated with some practical issues in the pre-analytical phase including selection of the DBS card, spotting and punching device, evaluation of the influence of different factors such as spotting temperature, anticoagulant, volume spotted, site of punching and haematocrit, as well as stability of the samples, effect of drying and storage conditions (Spooner et al., 2009; Timmerman et al., 2013, 2011). From the analytical point of view, one of the disadvantages of the DBS method is that it requires highly sensitive analytical techniques such as liquid chromatography tandem mass spectrometry (LC–MS/MS) because of the limited amounts of sample material and matrix complexity. Thus DBS samples are not as compatible as plasma or serum samples with the most commonly used high performance liquid chromatography with ultraviolet detection (HPLC-UV) (Sharma et al., 2014). However, nowadays LC–MS/MS technique is more available and may become a standard analytical technique for most of the analyses in the future, especially for concentrations in the low ng/ml range (Adaway and Keevil, 2012; Deglon et al., 2012a; Demirev, 2013; Saint-Marcoux et al., 2007; Wagner et al., 2014). Analyses of AED concentrations in DBS have been reported for phenobarbital (la Marca et al., 2009; Shah et al., 2013b; Villanelli et al., 2015), phenytoin (Coombes et al., 1984; Kong et al., 2014b), valproic acid (Kong et al., 2014b; Pohanka et al., 2014; Shah et al., 2013a; Wegner et al., 2014), clonazepam and clobazam (Deglon et al., 2012b), carbamazepine (Kong et al., 2014a,b; Shah et al., 2013a,b; Wegner et al., 2014), topiramate (Filippi et al., 2009; la Marca et al., 2008; Philippi et al., 2002; Popov et al., 2013; Shah et al., 2013a), rufinamide (la Marca et al., 2011), lamotrigine (AbuRuz et al., 2010; Shah et al., 2013a,b; Wegner et al., 2010, 2014), 10-hydroxycarbazepine (Wegner et al., 2010, 2014), and levetiracetam (Shah et al., 2013a,b). Quantification of AEDs in DBS was employed in various clinical settings. Most of the analytical methods were developed for paediatric applications and TDM (Coombes et al., 1984; la Marca et al., 2008, 2011, 2009; Pohanka et al., 2014; Popov et al., 2013; Shah et al., 2013b; Villanelli et al., 2015). Some of them are routinely applied to assess adherence to medications in adult and paediatric patients (AbuRuz et al., 2010; Deglon et al., 2012a; Shah et al., 2013a) or for TDM in pregnant women (Wegner et al., 2010).

4. Measurement of AEDs in DBS

653

4.1. Sample processing and analysis

654

Generally, in the laboratory a disc is punched from the blood spot representing a fixed volume of absorbed blood. Punching can be performed manually with simple low cost punches, using semi-automated instruments or fully automated robotic punching systems. Subsequently, sample pre-treatment is performed in order to obtain a clean sample for analysis. Protein and other interfering substances are removed by solvent extraction, protein precipitation, liquid–liquid extraction or solid-phase extraction (Li and Lee, 2014). Mostly, analytes are extracted with methanol, acetonitrile, or a mixture of both. On the other hand, water addition in the extraction solvent increases the interference with the endogenous components and should be avoided (Edelbroek et al., 2009). The current common practice involves adding of the internal standard (IS) in the extraction solvent (Abu-Rabie et al., 2011). There are several other options to apply the IS, like synchronous spotting of the analyte and IS in separate spots, spiking of IS in blood with the analyte and spotting, IS addition in the extraction solvent, spotting of IS solution on top of a previously spotted sample and spotting of sample on top of a previously spotted IS solution. Spraying of the IS solution has been also proposed as a convenient and easily automatable technique suitable for routine analysis of DBS by offline and online extraction (Zimmer et al., 2013). However, there is no general recommendation on IS addition in DBS bioanalysis and the most widely employed approach of IS incorporation in the extraction solvent does not compensate for recovery in many cases (van Baar et al., 2013). Feasibility of various IS addition procedures was investigated by Meesters et al. The recommendation is that the addition procedure should be a compromise between an acceptable recovery rate for the analyte of interest and feasibility for clinical practice (Meesters et al., 2011). Furthermore, the results from recently published study that investigated the merits of the various ways of IS addition prior to LC–MS/MS analysis on a set of 22 pharmaceutical compounds with log P in the range of 0–10 clearly showed that there is no ‘one-size-fits-all’ approach to IS addition in DBS bioanalysis. Therefore, validation of DBS methods must be carefully considered for each individual compound. It appears difficult to predict feasibility on the basis of compound structure and intrinsic molecular properties (van Baar et al., 2013). The ease of use and the benefits derived from the DBS analysis have generated a rapid growth in the development and application of analytical technologies (Demirev, 2013; Tanna and Lawson, 2011). A variety of analytical methods, including immunoassay, HPLC, gas chromatography with mass spectrometry (GC–MS), liquid chromatography with mass spectrometry (LC–MS), LC–MS/ MS, desorption electrospray ionization mass spectrometry (DESI-MS), matrix assisted laser desorption mass spectrometry (MALDI-MS) etc., are reported for the analysis of DBS. Availability of suitable analytical methods is especially valuable for TDM of AEDs, since the target analytes have to be quantified at the levels anticipated in a few microliters of dried blood. Widely applied liquid chromatography methods with UV or fluorescence detection, in general are more selective than immunoassays by separating the compounds of interest from the matrix before detection. However, those methods are limited to the quantification of drugs at high concentrations (mg/L range) because of the poor sensitivity. LC–MS is the technique of choice for quantitative bioanalysis. Employment of a new generation of mass spectrometers such as hybrid quadrupole time-of-flight resulted in improved sensitivity, which enables analysis of pharmaceuticals in the low therapeutic range (Wagner et al., 2014). Moreover, direct MS allows automated handling of DBS without any treatment prior to MS analysis

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Sampling method

LOD/LOQ (mg/L)

Paper type

DBS sample preparation/ extraction technique

Sample volume (lL)

Conversion factor DBS to plasma

Clinical validation

Topiramatea

LC–MS/ MS

0.5–50

20 lL heparinized venous blood was pipetted on paper

0.1 (LOD) 0.5 (LOQ)

903Ò Whatman

Two 3.2 mm discs were punched. Extraction with water:acetonitrile (30:70) in 0.05% formic acid containing D12-topiramate as IS

7

Comparison of the method with fluorescence polarization immunoassay. No differences between measured concentrations in plasma and DBS from 21 patients with confirmed epilepsy

Phenobarbitalb

LC–MS/ MS

1–100

20 ll EDTA venous blood was pipetted on paper

0.5 (LOD) 1 (LOQ)

903Ò Whatman

Two 3.2 mm discs were punched. Extraction with water:methanol (20:80) containing D5-phenobarbital as IS

6

Lamotriginec

HPLC-UV

0.5–20

10 lL EDTA venous blood was pipetted on paper

0.12 (LOD) 0.2 (LOQ)

903Ò Whatman

One 6 mm disc was punched. Extraction with ethyl acetate:1 M NaOH (24:1) containing metformin as IS

10

No Hct effect was evaluated. Assuming HCT range 50–60% (newborns) and 36–52% (adults) the TPM measurements from DBS to plasma concentration were converted multiplying by 2.22 (newborns) and by 1.79 (adults) Significant inverse correlation between levels of phenobarbital in plasma and in whole blood was observed. The Hct influence was considered by including a correction factor = 100/ (100  Hct) No influence of Hct in the range of 25–58% on lamotrigine concentrations

Rufinamided

LC–MS/ MS

0.008–0.8

20 ll venous blood was pipetted on paper

0.01 (LOQ)

903Ò Whatman

One 3.2 mm disc was punched. Extraction with water:acetonitrile (30:70) in 0.05% formic acid solution

3.5

Clobazame

LC–MS/ MS

0.02–5

5 lL EDTA venous blood was pipetted on paper

0.001 (LOD) 0.001 (LOD) 0.01 (LOQ)

903Ò Whatman

5

0.38 (LOD) 1.15 (LOQ) 0.223 (LOD) 0.7 (LOQ) 0.318 (LOD) 1 (LOQ) 0.3 (LOD) 0.9 (LOQ) 0.258 (LOD) 0.8 (LOQ) 0.07 (LOD)

903Ò Schleicher & Schuell

One 6 mm disc was punched. Extraction with methanol containing nordiazepam -D5 as IS One 6 mm disc was punched. Extraction with methanol:water (90:10) containing D12topiramate as IS One 6 mm disc was punched. Extraction with methanol:acetonitrile (3:1) containing hexobarbital as IS. The extract undergo SPE using OasisÒ HLB cartridges

Clonazepame f

Topiramate

Levetiracetamg

0.002–0.5 LC–MS/ MS

0.01–2

30 lL EDTA venous blood was pipetted on paper

HPLC-UV

2–50

30 lL venous blood was pipetted on paper

g

Lamotrigine

1–20

Phenobarbitalg

2–50

Carbamazepineg

1–20

Carbamazepineepoxideg

0.5–10

Carbamazepineh

GC–MS

0.5–120

30 lL EDTA venous blood was pipetted on paper

FTATM DMPK-C DBS

903Ò Whatman

One 6-mm disc was punched. Extraction with acetonitrile:1 M NaOH (24:1) containing 5-(pmethylphenyl)-5phenylhydantoin as IS

10

Not reported

15

Good correlation between rufinamide plasma and DBS levels was observed. The Hct influence was considered by including a correction factor = 100/(100  Hct) No Hct effect evaluation

Comparison of the method with particle enhanced turbidimetric inhibition immunoassay. No differences between measured concentrations in plasma and DBS from 50 patients with confirmed epilepsy Limited validation. No differences between measured concentrations in plasma and DBS from 3 volunteers given a single dose of 100 mg Limited validation. No differences between measured concentrations in plasma and DBS from 14 patients with confirmed epilepsy No clinical validation

The results indicate that human whole blood samples with Hct values 34–45% gave acceptable quantitation results No reported effect of the Hct in range of 30–55%

No clinical validation

Calculations take into account the individual Hct values and red blood cell-to-plasma partition (RBC/plasma) ratio. Conversion formulas from CDBS to plasma concentrations were:Cplasma

Limited validation. Comparison of the method with immunoturbimetric assays. Good agreement was observed between the theoretical plasma concentrations estimated from

No clinical validation

9

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Table 3 Overview of recently reported analytical methods for TDM of AEDs, using DBS as sampling technique.

Analytical method

Analytical range (mg/ L)

Sampling method

Phenytoinh

DBS sample preparation/ extraction technique

Sample volume (lL)

i

0.05 (LOD) LC–MS

1.4–173

20 lL venous blood was pipetted on paper

Not reported

903Ò Whatman

Oversized punch (8–9 mm) was used. Extraction with methanol containing IS. IS not reported

20

LC–MS/ MS

0–100

20 ll EDTA venous blood was pipetted on paper

0.3 (LOD)1 (LOQ)

903Ò Whatman

One 3.2 mm disc was punched. Extraction with methanol:water (80/20) + formic acid (0.1%)

3

Conversion factor DBS to plasma

Clinical validation

CBZ = 0.896xCDBSCBZ + 1.00 mg/ L CplasmaPHT = [1.116  (CDBSPHT/ (1  0.71  Hct))]  1.00 g/mL CplasmaVPA = [0.926  (CDBSVPA/ (1  0:96  Hct)] + 12.48]mg/mL No reported effect of the Hct in range of 30–60%

the DBS concentrations and the observed plasma concentrations for PHT, VPA and CBZ in 165 patients with confirmed epilepsy

No Hct effect evaluation. However, spot homogenecity was. No influence of the punch location in the dried blood spot were observed

Limited validation. No significant differences between measured concentrations in plasma and DBS from 34 patients with confirmed epilepsy Limited validation. Correlation analysis found a good agreement between plasma and DBS from 17 pediatric patients when a correction is performed

LC–MS/MS – liquid chromatography tandem mass spectrometry, HPLC-UV – high performance liquid chromatography with ultraviolet detection, GC–MS – gas chromatography with mass spectrometry, LC–MS – liquid chromatography with mass spectrometry, EDTA – ethylenediaminetetraacetic acid, LOD – limit od detection, LOQ – limit of quantification, NaOH – sodium hydroxide, IS – internal standard, DBS – dried blood spots, Hct – haematocrit, RBC – red blood cell, CDBS – drug concentration in dried blood spot samples, CBZ – carbamazepine, PHT – phenytoin, VPA – valproic acid. a la Marca et al. (2008). b la Marca et al. (2009). c AbuRuz et al. (2010). d la Marca et al. (2011). e Deglon et al. (2012a,b). f Popov et al. (2013). g Shah et al. (2013a,b). h Kong et al. (2014b). i Pohanka et al. (2014). j Villanelli et al. (2015).

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Phenytoinj

Paper type

0.07 (LOD)

Valproic acidh Valproic acid

LOD/LOQ (mg/L)

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Table 3 (continued)

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(Deglon et al., 2012a; Demirev, 2013). Currently, three categories of automation can be distinguished: on-line desorption of DBS sample, paper spray analysis of DBS sample, and fully automated extraction of DBS sample. Even though most of the automated methods are only tested in a controlled laboratory environment, development of automated DBS analysis can improve the overall efficiency regarding to the high throughput demands of the routine testing (Wilhelm et al., 2014). Fully-automated on-line DBS extraction is certainly of greater interest as it is less prone to errors compared to the off-line punching approach. Recently published studies showed interest in coupling of online automated extraction of DBS with LC–MS/MS for measurement of therapeutic agents in various clinical and research settings (Ganz et al., 2012; Oliveira et al., 2014a,b). Analytical methods used for DBS analysis are described in details elsewhere (Demirev, 2013; Tanna and Lawson, 2011; Wagner et al., 2014).

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4.2. Analytical methods for TDM of AEDs

733

Published validated analytical methods for quantification of AEDs in DBS are presented in details in Table 3. Analytical technique, in addition to sample preparation method (sampling, card type, and extraction methods), analytical range, calculated conversion factor and method of clinical validation is presented. Drug concentrations in DBS are measured by various analytical techniques such as HPLC-UV (AbuRuz et al., 2010; Shah et al., 2013b), gas chromatography with mass spectrometry (GC–MS) (Kong et al., 2014b), liquid chromatography with mass spectrometry (LC–MS) (Pohanka et al., 2014) and LC–MS/MS (Deglon et al., 2012b; la Marca et al., 2008, 2011, 2009; Popov et al., 2013; Villanelli et al., 2015). However, liquid chromatography coupled with mass spectrometry is the key technique used for DBS analysis of AEDs because of its high sensitivity and selectivity. The most often used DBS cards are Whatman 903 which are approved by FDA as Class II medical devices for blood collection. Almost all of the methods are developed using DBS samples provided by pipetting whole blood from venipuncture. Patient samples used for clinical validation were usually obtained directly by applying blood drops from a finger prick onto the DBS cards. Discs are punched manually and IS is usually added into the extraction solvent (Table 3). The effect of Hct on the measured DBS concentration of AEDs was generally assessed (AbuRuz et al., 2010; Kong et al., 2014b; la Marca et al., 2011, 2009; Pohanka et al., 2014; Popov et al., 2013; Shah et al., 2013b). For most of the methods the impact of spotted blood volume, spot homogeneity and calculation of conversion factor were not evaluated. The effect of blood volume on homogeneity of DBS was performed in recently published articles (Pohanka et al., 2014; Popov et al., 2013; Shah et al., 2013b). There are only three publications that reported simultaneous determination of the concentration of multiple drugs in a single DBS sample (Deglon et al., 2012b; Kong et al., 2014b; Shah et al., 2013b). In nearly all papers, method validation procedure was in accordance with the internationally used acceptance criteria described by Food and Drug Administration or European Medicines Agency (Bioanalytical Guidance EMA, 2012; Bioanalytical Guidance FDA, 2001). Additionally, Popov et al. validated the method according to the European Bioanalysis Forum (EBF) recommendations (Popov et al., 2013; Timmerman et al., 2011). The data on clinical validation of DBS by comparison of AEDs concentrations in DBS samples with concentrations in venous samples is very scarce. Limited validation by producing DBS samples in the laboratory with venous whole blood samples was performed by some of the authors (Table 3). Concentrations, measured in these DBS samples, were compared with concentrations found in the original whole blood samples. In some studies, however, no comparison with plasma samples was made or only a few concentration

716 717 718 719 720 721 722 723 724 725 726 727 728 729 730

734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779

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time curves constructed from DBS samples were presented (AbuRuz et al., 2010; la Marca et al., 2008, 2011, 2009; Popov et al., 2013). Clinical validation with evaluation of the effect of haematocrit and compound specific red blood cell to plasma binding was performed by Kong et al. (2014b). The authors proposed DBS as an alternative matrix to the conventional plasma samples for TDM of phenytoin, carbamazepine and valproic acid in the adult population since good agreement between the theoretical plasma concentration estimated from the DBS concentration levels and the observed plasma concentration was demonstrated. Conversion formulas including the blood cells to plasma partition ratio (KBC/PL) and the individual Hct levels for the estimation of the theoretical plasma concentrations from DBS concentrations were provided. For phenytoin and valproic acid conversion factors of 0.29 and 0.04, respectively, were recommended for calculation of plasma concentration. However, DBS samples were obtained from venous blood (Kong et al., 2014b). Almost all authors reported appropriateness of the developed DBS methods for application in the paediatric population.

780

5. Regulatory aspects

798

Up to now, there are no specific regulatory based guidelines for the development and validation of the quantitative determination of drugs in DBS (Jager et al., 2014). Generally, method validation employing DBS matrix is performed in accordance with the current guidelines on bioanalytical method validation (Bioanalytical Guidance EMA, 2012; Bioanalytical Guidance FDA, 2001). However, in recent years several review and recommendation papers have been published. Lately, Jager et al. proposed recommendations on how to perform validation of bioanalytical methods using DBS and acceptance criteria for all relevant parameters were discussed. DBS specific validation parameters are summarized in Table 4. Detailed description of specific validation procedures and validation criteria are provided elsewhere (Jager et al., 2014). Previously, EBF has made several recommendations which are intended to provide guidance for the analysis of DBS samples. Moreover, EBF has introduced the term good blood-spotting practices as a part of the other good practice procedures in order to avoid dealing with poor quality samples and unreliable results (Timmerman et al., 2011). The effect of Hct, stability (of the blood prior to spiking and stability of the cards), application of IS to the samples, sample dilution procedures, and spot homogeneity were identified as major areas of focus related to the use of DBS in regulated bioanalysis. It was shown that Hct changes remain the single most important parameter defining compound behaviour and DBS assay performance in terms of spot formation, spot homogeneity, accuracy, precision and recovery. DBS sample homogeneity is affected by a number of factors like card substrate type, compound and Hct (Cobb et al., 2013). The time between spiking the analyte in blood and application of the spiked sample onto the card (i.e. stability aspects and equilibration after spiking during method development), vigorous shaking of blood prior to spiking on card (i.e. introducing haemolysis) or application of multiple sample

799

Table 4 DBS specific validation parameters according to Jager et al. (2014).         

Spot volume Haematocrit Spot homogeneity Spot to spot carry over Extraction recovery Matrix effect Stability under transport and storage conditions Sample dilution above the upper limit of quantification Incurred sample reanalysis

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800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830

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aliquots from one capillary versus from multiple capillaries are some of the examples of parameters that can negatively impact homogeneity of spiked samples (Timmerman et al., 2011). Spot homogeneity as a parameter mostly influenced by Hct will impact the overall bioanalytical strategy for DBS method development including spotting, selection of card-type, size of a punch and punching place (Timmerman et al., 2013). As spot nonhomogeneity can significantly affect the performance of a DBS method, the sampling approach taken for analysis of DBS samples should be assessed during method establishment to ensure reliable and reproducible concentration measurements. Since DBS samples are generally nonhomogeneous, it is important to investigate the homogeneity, for example using a range of Hct levels, to ensure that these issues do not exist (Cobb et al., 2013). However, when nonhomogeneity is confirmed, there is an expectation that the associated inaccuracy due to nonhomogeneity, in addition to the other sources of inaccuracy in the method, should not be beyond the acceptance criteria of regulated bioanalytical methods (Timmerman et al., 2013). The effect of non-homogeneity can be overcome or limited by taking the whole spot through the extraction procedure. On the other hand, in this approach the most critical parameter is the accuracy of the volumetric application. Other proposed approaches are taking one punch per spot at the same location in every sample and punching a larger diameter of the spot. The latter compromise spotting of an approximate volume of blood onto a card in a clinical setting without the need for accurate application of the sample (Cobb et al., 2013). As it can be difficult to train people not accustomed to the laboratory technique, whole spot DBS analysis has evolved from cutting the entire spot from DBS paper to the use of two new techniques, such as perforated DBS and precut DBS (Fan and Lee, 2012). Perforated DBS discs predefine areas on a DBS card which are later removed from the card by pushing with single-use pipette tips into 96-well plates (Li et al., 2011a). In precut DBS methods, discs of a particular size are precut from a filter paper and blood sample is then spotted directly onto the disc (Youhnovski et al., 2011). Similar concept with dried matrix on paper discs has been also proposed by other authors (Meesters et al., 2012). However, these methods are not suitable with patient self-sampling because of the requirement for volumetric application. A novel approach called volumetric absorptive microsampling for quantitative bioanalysis of DBS was proposed recently. This device has the potential to overcome some of the issues associated with conventional sampling (Denniff and Spooner, 2014; Spooner et al., 2014). However, the correction method by standardization of the Hct in the calibration standards close to the expected Hct of the samples is still needed to minimize the Hct effect (Wilhelm et al., 2014). Haematocrit is identified as the most important parameter that can affect DBS assay. Determination of Hct directly in DBS is difficult, although recently published studies showed that calculation of Hct based on measurement of potassium concentration seems promising and is a potential tool for Hct correction (Capiau et al., 2013; den Burger et al., 2015). Moreover, a novel on-card approach for assessment of Hct directly on DBS spots using diffuse reflectance which allows for correction of sample volume was reported (Miller et al., 2013). Spot homogeneity is another parameter which is difficult to control. Improvements on this matter are expected with the development of substrates which will enable the formation of a homogeneous DBS (Timmerman et al., 2013). There are no proficiency testing programs for TDM by DBS because of the different types of card matrices that complicate the design of such a program (Wilhelm et al., 2014). However, a pilot program for the external quality control for TDM of tacrolimus by DBS is an ongoing project carried out by the Dutch Association for Quality Assessment in Therapeutic Drug Monitoring and Clinical Toxicology (Robijns et al., 2014).

Limited guidelines for validation of DBS analytical methods and the fact that plasma and serum are golden standards in TDM are the main obstacles for a more widespread implementation of DBS (Stove et al., 2012). Additional experiments are required to validate DBS assays towards current acceptance criteria for regulated bioanalyses (Timmerman et al., 2013).

897

6. Clinical validation

903

Apart from the limitations of the current guidelines on bioanalytical method validation, one of the major obstacles for widespread utilization of DBS technique in TDM is current limited clinical validation data, required to ensure consistency of the results with traditional plasma samples. Complete clinical validation has to be performed through the comparisons of concentrations found in plasma/serum samples and DBS samples taken at the same time, followed by an appropriate statistical analysis. However, in most studies a limited clinical validation or a form of in vitro validation is performed with DBS samples produced in the laboratory from the withdrawn venous blood. Concentrations measured in these DBS samples are then compared with the concentrations found in the original whole blood (Edelbroek et al., 2009). Considering that real DBS samples are typically small volumes of capillary blood, there are plausible chances that capillary blood analyte concentration may vary from venous blood. Consequently, DBS results may differ from those obtained with traditional venous sampling. In this case a venipuncture is used which deviates from the original goal of DBS and obliterates one of the major benefits from DBS sampling, namely, a more convenient sample collection. In addition, the calibration samples (prepared from venous blood) and study samples of capillary blood do not have the exact same matrix composition and therefore biological variability gives rise to potential discrepancies in quantification, especially because of the matrix effects (Wagner et al., 2014). Therefore, it is recommended that important patient-related factors like blood spot volume, Hct value, and difference between capillary and venous blood are validated during the development of a bioanalytical method for DBS intended for TDM of AEDs. However, at steady state drug concentrations the differences between capillary and venous concentrations should be negligible and blood concentrations from either source could be used interchangeably for estimating the AEDs concentration (Kong et al., 2014b). Briefly, comparison of DBS assay results with those from simultaneously collected serum or plasma samples via venipuncture using a previously established, gold-standard method is a satisfactory validation tool. Ideally, when establishing the individual therapeutic concentration, two separate venous samples and capillary DBS samples in triplicate should be obtained, approximating trough (immediately before dosing) and peak (after dosing) steady-state concentrations. Calibration standards can be prepared from blank venous whole blood as it is not possible to have blank capillary blood available in large quantities. Although our knowledge about the impact of matrix composition (capillary vs. venous blood) on analyte determination is currently very limited, it is usually considered insignificant (Wagner et al., 2014). Moreover, whereas spiking plasma with an analyte only requires mixing to obtain properly homogenised samples, with DBS analytes are susceptible to partition between the plasma and the blood cells (principally erythrocytes). Therefore a certain delay is expected before the distribution equilibrium is reached. In most cases, distribution equilibrium is expected to be achieved relatively rapidly and is of no practical concern (Emmons and Rowland, 2010). Generally, statistical evaluation of the relationship between DBS and plasma drug concentration can be performed with linear

904

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898 899 900 901 902

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regression and by inspection of the residual plots for evidence of bias or inconsistent variability across the range of measurements. Furthermore, analysis of DBS and plasma/serum samples can be used to generate a conversion formula to derive plasma-equivalent values from the results of DBS analysis. However, plasma equivalents should be used with caution as the relationship may vary across different analytical methods and across populations. Occasionally, whole blood samples instead of serum/plasma samples can be used for comparison with DBS samples (McDade et al., 2007). Venous DBS can be useful for patients who are already in clinical settings for other routine analyses, as they may provide advantages related to handling, storage, shipment, and possible stability improvement. Conversion factors, calculated from geometric mean DBS/plasma concentration ratios should be used to calculate converted DBS or venous DBS concentrations. Theoretical plasma concentration can be calculated using Eq. (4). When the effect of RBC dilution on DBS concentration of AED is negligible, direct comparison of DBS and plasma concentration is possible (Kong et al., 2014b). One of the more recently proposed methods intended to establish the use of DBS samples as a surrogate to plasma for TDM of AEDs employed a comparison of the population pharmacokinetic estimates from concurrent DBS and plasma samples. Using this approach it was confirmed that DBS sampling is a valid alternative to conventional venous sampling for TDM of carbamazepine (Kong et al., 2014a).

985

7. Clinical implications

986

TDM is one of the first concepts of precision medicine. It aims at individualisation of drug therapy based on drug concentration or biomarker level utilizing pharmacokinetic and pharmacodynamic principles. Therefore it is not solely the process of measuring drug concentration levels in biological fluids, but putting these into service of an optimal therapeutic drug response with minimal adverse drug effects i.e. better pharmaceutical care of patients (Bauer, 2014). AEDs are the most common group of medications for which TDM is performed (Krasowski and McMillin, 2014). TDM has been introduced in the sixties to the first-generation of antiepileptic drugs, including carbamazepine, phenytoin, phenobarbital, primidone and valproic acid, which have narrow therapeutic windows and pronounced variability in pharmacokinetics. Despite the fact that TDM is widely used in routine care of patients with epilepsy, it is not evidence based and its usefulness is still somewhat questionable as its outcome has not been systematically evaluated. There were only two randomized controlled studies (Froscher et al., 1981; Jannuzzi et al., 2000) and neither of them has shown clear clinical benefit of using TDM in epilepsy patients. There was no difference in the seizures-free rates, nor in the frequency of adverse events between the tested group of patients on TDM and the control group of patients without TDM. However, it has been also identified that pre-analytic errors such as timing of blood sample drawing and incorrect recording of dosing in addition to misinterpretation of TDM data are frequent (Patsalos et al., 2008). Despite the fact that TDM in epilepsy is in this view anecdotal, this does not argue against its clinical benefits in specific situations; during polytherapy for example, or in patients with renal or liver disease, or to detect a noncompliant patient, or during pregnancy. The primary aim of TDM in epilepsy has been comprehensively discussed (Patsalos et al., 2008). It can be used after initiation of treatment or after dose adjustment to establish individual therapeutic range. It is also useful to confirm a presumed AED toxicity, or in patients with presumed drug–drug interactions, or when seizures persist despite adequate dosage. It is particularly useful for AEDs showing dose-dependent (e.g.

960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983

987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022

13

phenytoin) or time-dependent (e.g. carbamazepine) pharmacokinetics. The rationale for TDM in special populations (e.g. children, elderly, and pregnant women with epilepsy) is to elucidate alterations in pharmacokinetics during physiologic changes.

1023

7.1. Clinical benefits of TDM of carbamazepine

1027

Carbamazepine is an established first-line AED to treat focal seizures. It undergoes extensive hepatic metabolism, the major metabolite being carbamazepine-10-11-epoxide, which contributes to the therapeutic and toxic effects of carbamazepine. Carbamazepine induces various enzyme systems, including cytochrome P450 1A2, 2C and 3A and uridine diphosphate glucuronyl transferases. Consequently it increases metabolism of various drugs, including its own. Owing to autoinduction, which usually begins after 3–5 days after initiation of treatment and completes in 20–35 days, its clearance changes. This results in time-dependent pharmacokinetics with a half-life of 25–65 h after a single dose and 12–17 h in the post-induction steady-state and a progressive decrease in its blood concentration (Vucicevic et al., 2007). TDM can be a useful tool to monitor these changes. Kong et al. have used plasma and DBS carbamazepine concentration data from 97 patients with epilepsy to build population pharmacokinetic models and demonstrated that for carbamazepine the use of DBS could be comparable to plasma (Kong et al., 2014b).

1028

7.2. Adjustment of lamotrigine dosing in pregnancy

1046

Another interesting example of the use of DBS for TDM in epilepsy is the study of lamotrigine clearance changes during pregnancy (Polepally et al., 2014). Owing to low risk of teratogenicity and adverse neurodevelopmental effects lamotrigine is an attractive option to treat epilepsy in pregnant women. Lamotrigine is increasingly prescribed in women of childbearing potential and during pregnancy. However, lamotrigine dosing during pregnancy usually needs adjustment due to 100% (range: 65–230%) increase in clearance. This is presumably related to changes in glomerular filtration rate and estradiol induced increase in glucuronidation (Reimers et al., 2011). These changes are subject to marked interindividual variability and TDM seems warranted. Polepally et al. have characterized changes in oral clearance of lamotrigine over the course of pregnancy and postpartum and used population pharmacokinetic modelling approach to support the development of clinical guidelines (Polepally et al., 2014). Six hundred DBS samples from 60 pregnant women (64 pregnancies) were collected. Based on these data a pharmacokinetic model was developed. A mixture of two subpopulations of patients has been identified with a ten-times higher gestational age-associated increase in oral clearance of lamotrigine from 2.16 to 6.88 L/h at the end of pregnancy (0.118 L/h per week) in 77% of women, compared to 0.0115 L/h per week and minimal change in 23% of women. Postpartum clearance declined to baseline values with a half-life of 0.55 weeks. The reason for the immense difference in the rate of change in clearance is unknown, but TDM appears as a useful tool to adjust these changes. Frequent monitoring and dose adjustments may be necessary which favours the advantages of DBS.

1047

8. Conclusions

1075

TDM is used for a number of AEDs to establish optimal therapy in individual patients. It seems that DBS method is a viable alternative for a conventional TDM of AEDs based on plasma samples. As a minimally invasive sampling technique it allows manipulation with limited quantity of blood at various time points and facilitates sample handling and storage. Without a doubt, considering ethical

1076

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1024 1025 1026

1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045

1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074

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and economic issues, DBS method is especially valuable in specific clinical situations involving new-borns, pregnant women, or critically ill patients. However, there are many obstacles that should be overcome. First, standardization of critical technical aspects for appropriate sampling, sample preparation and validation of the analytical procedures for quantification of the drugs, as well as interpretation of the results is needed. Furthermore, limited data on clinical validation and the fact that this technique has been used in practice only for a few AEDs makes the usefulness and practical value of DBS application in TDM of AEDs debatable. Development of specific assay protocols for TDM using DBS samples and studies that will correlate concentrations of the drugs between plasma/ serum and DBS samples are necessary. Routine implementation of TDM of AEDs using DBS method is a challenge that should be faced by the pharmaceutical scientists in the future.

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Acknowledgements

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This work was financially supported by the Slovenian Research Agency (ARRS Grant P1-0189). DM receives doctoral funding from Ad Futura Scientific and Educational Foundation of Slovenia.

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Please cite this article in press as: Milosheska, D., et al. Dried blood spots for monitoring and individualization of antiepileptic drug treatment. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.04.008

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