The value of drug and metabolite concentration in blood as a biomarker of psychopharmacological therapy.

International Review of Psychiatry, October 2013; 25(5): 494–508

The value of drug and metabolite concentration in blood as a biomarker of psychopharmacological therapy

GUDRUN HEFNER, A. KATHRIN LAIB, HILMAR SIGURDSSON, MATTHIAS HOHNER & CHRISTOPH HIEMKE

Int Rev Psychiatry Downloaded from informahealthcare.com by Chulalongkorn University on 01/03/15 For personal use only.

Department of Psychiatry and Psychotherapy, University Medical Centre Mainz, Germany

Abstract Desirable and undesirable effects of a drug are related to its concentration at various sites of actions. For many psychotropic drugs, it has been shown that drug concentration in brain correlates with concentration in blood. The latter is also an available estimate of clearance and bioavailability. Its monitoring enables identification of multiple factors that have an impact on clinical outcomes, especially uncertain compliance and pharmacokinetic peculiarities. For this review we analysed for antidepressants if drug concentration in blood can be used as biomarker for psychopharmacological treatment. Systematic review of the literature revealed for new and old antidepressant drugs that drug and metabolite concentrations in blood are measures of the pharmacokinetic phenotype and related differentially to occupancy of primary target structures, therapeutic effects and unwanted anticholinergic, cardiac and other side effects. Drug concentration in blood can therefore be used as biomarker in clinical practice to guide psychopharmacological treatment with established antidepressant drugs. Monitoring of drug concentration is suitable to improve efficacy and safety of the pharmacotherapy, especially in elderly patients who require complex pharmacological therapies.

Introduction The aims of psychopharmacological treatment are minimization of psychopathological symptoms in the acute phase and stabilization and relapse prevention in the chronic phase. These goals should be attained with lowest possible effective doses of the medication to minimize risks of unacceptable side effects. To achieve these goals, the dose is the major steering instrument. Rational dosing is based on knowledge of bioavailability (F), clearance (CL), volume of distribution (Vd), and elimination halflife (t1/2). This information is generally obtained from healthy subjects. In patients, however, multiple factors such as uncertain compliance, polymorphic expression of enzymes involved in drug metabolism, co-medication, age, body weight, or co-morbid diseases affect the pharmacokinetic parameters considerably, especially hepatic and renal clearance (Fig. 1). As a consequence, drug concentrations in the body and resulting clinical outcomes differ markedly from patient to patient. Dose adaptation that relies on drug concentration in blood as a biomarker of pharmacokinetic variability can be helpful to decrease this variability.

This review summarizes the state of the art for drug concentrations of antidepressant drugs in blood as biomarkers for psychopharmacological treatment. Antidepressants were considered as model drugs aiming to show that drug concentrations in blood are differentially related to desired and undesired drug effects and that measuring drug and metabolite concentrations in serum or plasma enables personalized psychopharmacological therapy. Special attention was taken concerning elderly patients, as these patients exhibit the highest pharmacokinetic variability, and complex care is needed with advancing age. Published articles that have examined the relationship between blood concentration of antidepressants/ lithium and therapeutic/adverse effects, respectively, were analysed. We also reviewed literature on the link between drug and metabolite concentration in blood and the pharmacokinetic phenotype and compliance. Concerning adverse effects, we concentrated on studies which analysed the dependency between blood concentration and distinct adverse drug reactions. We identified studies published in English and German, searching in PubMed database using the

Correspondence: Professor Dr Christoph Hiemke, Department of Psychiatry and Psychotherapy, University Medical Centre Mainz, Untere Zahlbacher Strasse 8, 55131 Mainz, Germany. Tel: ⫹ 49-6131-17-7131. Fax: ⫹ 49-6131-17-6789. E-mail: [email protected] (Received 24 May 2013 ; accepted 16 August 2013) ISSN 0954–0261 print/ISSN 1369–1627 online © 2013 Institute of Psychiatry DOI: 10.3109/09540261.2013.836475


Drug concentration in blood to guide psychopharmacotherapy

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Fig. 1. From drug prescription to clinical effects. Multiple individual variables lead to marked pharmacokinetic and pharmacodynamic variability of drug response. CL, clearance; F, bioavailability; Vd, volume of distribution; Cmax, peak concentration; Cmin, trough level; Css, steady-state concentration.

following terms: ‘serum concentration’, ‘plasma concentration’, ‘serum level’, ‘plasma level’, ‘therapeutic drug monitoring’, ‘dosage’, ‘dose’ and ‘TDM’ (therapeutic drug monitoring) in combination with ‘tricyclic antidepressants’(TCA), ‘TCA’, ‘antidepressants’, ‘SSRI’ (selective serotonin reuptake inhibitors), ‘lithium’ and ‘tolerability’, ‘side effects’, ‘adverse drug-reactions’ (ADR), ‘ADR’, ‘anticholinergic’, ‘cardiac’, ‘QTc prolongation’, ‘Torsades de pointes’ (TdP), ‘TdP’, ‘seizures’, ‘delirium’, ‘hypertension’, ‘response’, ‘therapy effect’, ‘efficacy’, ‘effectiveness’, ‘compliance’, ‘adherence’, ‘biomarker’. No limitation was made concerning study design and patient numbers included in the studies.

case of co-medication with an inhibitor of CYP2D6 due to reduced clearance. In contrast, serum levels will be low if the patient is non-compliant or an ultrarapid metabolizer (UM) of CYP2D6 due to enhanced clearance. Since drug concentrations in blood are usually related to drug concentrations at the target structures, it makes sense to measure drug levels in blood for dose individualization, i.e. to use TDM.

Drug concentrations in blood, biomarkers for hepatic and renal drug clearance Pharmacokinetic variability can be easily visualized by measuring drug concentrations in blood. Abnormally high or low drug levels are informative for clarification of compliance problems or pharmacokinetic peculiarities. As an example, Fig. 2 shows steady state concentrations of paroxetine in patients under a prescribed dose of 40 mg/day. Paroxetine is primarily metabolized via cytochrome P450 (CYP) 2D6. Paroxetine serum levels will be abnormally high if a patient is a poor CYP2D6 metabolizer or in

Fig. 2. Steady-state concentrations of paroxetine in patients under a prescribed dose of 40 mg/day (computed from Eggart et al., 2011). The vertical bar indicates the dose-related reference range of paroxetine for 40 mg/day (Hiemke et al., 2011).

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TDM refers to the individualization of drug dosage by adjusting drug concentrations to a distinct concentration range (trough level) (Hiemke et al., 2011), the so called ‘therapeutic reference range’ (Hiemke et al., 2011). For this population-based target range a therapeutic response and a minimum of unwanted side effects are expected with highest probability. For many antidepressant drugs TDM is well established (Hiemke et al., 2011). Under steady-state conditions, the drug concentration in blood (Css) is a function of the dose (D), the dosing interval (τ), the clearance (CL), and the bioavailability (F) of the drug: Css ⫽ D/τ ⫻ 1/CL/F The drug concentration (Css) is an available estimate for CL and F. For most antidepressant drugs the variability of blood levels depends predominantly on variable CL. Css of an individual patient can therefore be regarded as a biomarker for the CL of an individual patient in accordance with the Biomarkers Definitions Working Group (Altar, 2008 p. 362, Table 1) who defined a biomarker as ‘a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic response to therapeutic interventions.’ With few exceptions, e.g. milnacipran, the total CL of most antidepressant drugs depends primarily on the hepatic clearance. A number of functional gene variants of the cytochrome P450 (CYP) family and many inhibitors and inducers of drug metabolizing enzymes were detected in the past by measuring drug concentrations in blood. For lithium, renal clearance is the major determinant for its excretion. It is an alkali metal that can be easily detected (Grandjean & Aubry, 2009; Soares et al., 2001). Lithium does not undergo any metabolic fate in the liver. In compliant patients, unexpectedly abnormal serum concentrations of lithium point to disturbed renal function which requires not only dose correction but also clarification of reasons underlying the disturbance. Typical reasons for abnormalities are medications or other medical conditions that cause alterations in sodium absorption by the kidney.

Drug and metabolite concentration in blood and metabolic ratios as biomarkers for compliance Adherence to antidepressant medication is assumed as one of the major contributors to optimal therapeutic outcome (Akerblad et al., 2008; Frank et al., 1992; Gopinath et al., 2007; Melfi et al., 1998). It is estimated that less than half of patients receiving antidepressants take their medication as prescribed

for six months or longer (Akerblad et al., 2008; Demyttenaere et al., 2001; Olfson et al., 2006). There are different types of non-compliance: total and partial non-compliance (Reis et al., 2004). Premature or temporary discontinuation, taking lower or higher doses than prescribed (Akerblad et al., 2008) or taking the drug directly before the appointment and collection of the blood resulting in non-trough serum levels may pretend compliance. These types of non-compliance may occur deliberately or nondeliberately in different patients. Different approaches have been developed to control compliance. An established objective and valid method is measuring drug concentration in blood (ng/mL). Non-detectable concentrations of the drug in one or more samples indicate total noncompliance (Hiemke et al., 2011). When a detectable but abnormally low drug concentration is observed, one should first suspect partial non-compliance, and second, rapid metabolism. Inconsistency with prior measures is another signal for partial noncompliance. Then drug level measurement should be repeated. The serum level can be below or above the therapeutic reference range (Table 1) because of a low or high dose. Most antidepressant drugs follow linear kinetics (de Leon et al., 2005). Linear kinetics means that concentration (C) and dose (D) follow a linear relationship with a stable C/D ratio (ng/mL/mg). When the dosage is doubled, the concentration will also increase two-fold. Fluoxetine, paroxetine, and fluvoxamine are exceptions. They follow non-linear kinetics (Hiemke & Härtter, 2000). These three drugs are inhibitors of their own metabolism. Their clearance decreases with increasing dose. Considering such exceptions, C/D is an even better measure for compliance control than the absolute drug concentration in blood. C/D should be used in cases of suspected compliance problems. An abnormally low C/D indicates noncompliance independent of the dose that was prescribed (Hiemke et al., 2011). Moreover, the metabolic ratio (MR), which is the serum level of the drug metabolite divided by the serum level of the parent compound, is another highly informative measure to clarify patients´ compliance problems, especially in the course of time (Hiemke et al., 2011). Reis and co-workers (2004) stated that by measuring metabolites, the time window to view non-compliance is opening up. Especially in case of partial non-compliance (omitting one single or several doses) which could have occurred some days before taking the blood sample, poor adherence can only be detected by including the measurement of metabolites and analysing the MR. Metabolites should be measured even if they do not contribute to therapeutic effects. It is thus possible to reveal any intake of medication directly before blood sampling


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Table 1. Therapeutic reference ranges and laboratory alert levels of antidepressant drugs reported in the guidelines for TDM in psychiatry (Hiemke et al., 2011) and associated adverse effects that may occur at therapeutic and supratherapeutic concentrations.

Drugs plus active metabolites Amitriptyline plus nortriptyline

Bupropion plus hydroxybupropion


Citalopram

Therapeutic reference range (ng/mL) 80–200

225–1500

50–110

Side effects at therapeutic concentrations

Laboratory alert level (ng/mL)

Anticholinergic side effects QTc prolongation Cardiovascular side effects Somnolence Gastrointestinal disturbances Cardiovascular side effects

300

2000

Gastrointestinal disturbances

220

Clomipramine plus norclomipramine

230–450

Anticholinergic side effects Cardiovascular side effects Somnolence

450

Desipramine

100–300

Anticholinergic side effects QTc prolongation Cardiovascular side effects Somnolence

300

Doxepin plus nordoxepin

50–150


300

Duloxetine

30–120

Gastrointestinal disturbances Cardiovascular side effects

240

Escitalopram

15–80


160

Fluoxetine plus norfluoxetine

120–500


1000

Imipramine plus desipramine

175–300


300

Mirtazapine

30–80

Somnolence

160

Nortriptyline

70–170

300

Paroxetine

30–60

Anticholinergic side effects QTc prolongation Cardiovascular side effects Somnolence Anticholinergic side effects Gastrointestinal disturbances

120

Toxic effects at supratherapeutic concentrations Cognitive disturbances Delirium Sudden cardiac death/TdP Seizures Anticholinergic side effects QTc prolongation Seizures Anticholinergic side effects Cardiovascular side effects QTc prolongation Seizures Serotonin syndrome/SIADH Cognitive disturbances Delirium QTc prolongation Seizures SIADH Cognitive disturbances Delirium Sudden cardiac death/TdP Seizures SIADH Cognitive disturbances Delirium Sudden cardiac death/TdP Seizures SIADH Anticholinergic side effects Seizures Serotonin syndrome/ SIADH Anticholinergic side effects Cardiovascular side effects QTc prolongation Seizures Serotonin syndrome/SIADH Anticholinergic side effects Cardiovascular side effects QTc prolongation Seizures Serotonin syndrome/SIADH Cognitive disturbances Delirium Sudden cardiac death/TdP Seizures SIADH Anticholinergic side effects Cardiovascular side effects QTc prolongation SIADH Cognitive disturbances Delirium Sudden cardiac death/TdP Seizures Cognitive disturbances Delirium Cardiovascular side effects Seizures Serotonin syndrome/SIADH

(Continued)

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Table 1. (Continued). Drugs plus active metabolites


Sertraline

Therapeutic reference range (ng/mL)

Side effects at therapeutic concentrations

Laboratory alert level (ng/mL)

10–150


300

Trimipramine

150–300

Anticholinergic side effects Cardiovascular side effects Somnolence

600

Venlafaxine plus O-desmethyl-venlafaxine

100–400

Gastrointestinal disturbances Cardiovascular side effects

800

Toxic effects at supratherapeutic concentrations Anticholinergic side effects Cardiovascular side effects QTc prolongation Seizures Serotonin syndrome/SIADH Cognitive disturbances Delirium QTc prolongation Seizures SIADH QTc prolongation Seizures Serotonin syndrome/SIADH

Typical anticholinergic side effects are cognitive impairment, constipation, hyposalivation/dry mouth and urinary retention. Gastrointestinal disturbances associated with SSRIs and SNRIs are nausea, vomiting and diarrhoea. Cardiovascular side effects mean orthostatic dysregulation, tachycardia or cardiac arrhythmia. Other cardiac symptoms are QTc prolongation, torsades de pointes (TdP) or sudden cardiac death. Serotonin syndrome includes mental disorders (agitation, hallucination), autonomic hyperactivity (tachycardia, blood pressure deviations, fever) and neuromuscular disturbances (tremor). The syndrome of inadequate adiuretin secretion (SIADH) is characterized by the occurrence of hyponatraemia, hypokalaemia and hypophosphataemia, accompanied by nausea and confusion. Cognitive disturbances are reflected by dizziness, confusion, disorientation or cognitive decline according to the different types of delirium (ICD-10 F05).

in order to pretend compliance (Stieffenhofer & Hiemke, 2010). Inter-individual variations of serum levels and MRs have been measured for sertraline, escitalopram, venlafaxine and mirtazapine (Diaz et al., 2005; Kurz et al., 1998; Lundmark et al., 2000b; Reis et al., 2002, 2005, 2007; Skogh et al., 2002). Coefficients of variation (CV) were in the range of 20 to 166% depending on the study conditions. Under well controlled conditions that considered co-medication, food intake and stable smoking status intra-individual plasma levels were found to be rather stable ranging around 20% (Khalifa et al., 1992; Lundmark et al., 2000b; Reis et al., 2002, 2005, 2007; Skogh et al., 2002), since the clearance of individual drugs is a constant process over time. Performing consecutive measurements of drug serum levels, clinicians can find an MR for individual patients according to a supposed low within patient variability. Changes in the individually established ratio may indicate whether changes in drug disposition or compliance have occurred (Reis et al., 2002). Based on this concept, Bengtsson (2004) stated in a review that pharmacokinetic variance in the course of clinical studies with healthy volunteers will amount to a 5–10 fold variation of steady state drug concentrations in blood. Under naturalistic conditions this variance will increase to 50 to 100-fold fluctuation, and thereby compliance problems are most relevant. Perel (1988) proposed diagnosing non-compliance to antidepressant treatment with tricyclic antidepressants if two dose-corrected serum levels of the individual patient exceed the previously ascertained

mean value by more than two standard deviations (Perel, 1988). Measuring escitalopram and Ndemethylescitalopram serum levels, Reis et al. (2007) found stable metabolic capacities in the individual patient over time. Serum levels of escitalopram showed moderate oscillation around 30%, while the intra-individual ratio of N-demethylescitalopram to escitalopram proved to be more stable, fluctuating around 23%. Inter-individual serum levels at a given dose, however, showed a wide variation up to 71%. Based on minimal intra-individual fluctuations of sertraline and desmethylsertraline serum concentrations over time, Reis et al. (2004) revealed total nonadherence in cases of undetectable serum levels. Partial non-adherence was detected when plasma levels differed from the population mean by more than two standard deviations or if the ratio of desmethylsertraline to sertraline differed by more than 50% from the individually established mean. It is concluded that measurement of drug concentrations in blood with inclusion of active as well as inactive metabolites and taking into account the intra-individual course of drug levels in blood, dose corrected levels or metabolic ratios over time are most suitable to assess compliance at present. Drug and metabolite concentrations in blood, biomarkers for the pharmacokinetic phenotype and genotype For many psychoactive drugs, metabolites actively contribute to the overall clinical action of the parent drug, e.g. fluoxetine and the active metabolite


Drug concentration in blood to guide psychopharmacotherapy norfluoxetine (Hiemke & Hartter, 2000). For this reason, blood concentration measurement must include the quantification of active metabolites to depict the active fraction at the site of action. As explained above, the analysis of pharmacologically inactive and active metabolites can give useful information on the metabolic state of the patient in addition to the status of compliance (Caccia & Garattini, 1992) and to get information regarding the patients’ capacity to metabolize drugs. With regard to drug–drug interactions, C/D and MR are also most informative. They increase above the ‘normal’ ratio (Hiemke et al., 2011) when the metabolism of the parent drug is induced by co-medications or smoking, and decrease when an inhibitor is used as co-medication. Using this approach, it was demonstrated that melperone is an inhibitor of CYP2D6. This drug increased the C/D of venlafaxine and decreased the MR of O-desmethylvenlafaxine/ venlafaxine due to inhibition of the CYP2D6 catalysed formation of O-desmethylvenlafaxine (Grozinger et al., 2003). For oxybutynin, which decreased the C/D of clomipramine and its metabolite norclomipramine, it was shown to be an inducer of the clearance of this TCA. Further analysis revealed that oxybutynin is an inducer of CYP3A4, since it increased the ratio of concentrations of Hydroxymorphinan to dextrorphan (Grozinger et al., 1999). These observations taken together indicate that C/D and even more ratios of metabolite concentrations to parent drug reflect metabolic capacities (Hendset et al., 2006). The ‘normal range’ of C/D was reported in the consensus guidelines for TDM in psychiatry (Hiemke et al., 2011). It contains 68% of the drug concentrations determined under normal conditions in the blood of a ‘normal’ patient, i.e. individuals 18–65 years of age without relevant co-morbidity, co-medication, or genetic abnormalities in drug metabolism (Haen et al., 2008). Many patients, however, especially elderly patients, often do not fulfil the conditions of a ‘normal’ patient. Ageing involves progressive impairments of the functional reserve of multiple organs, especially renal and hepatic clearance, and significant changes in body composition are observed (Turnheim, 2003). Multi-morbidity or polypharmacy may lead to significant changes in drug absorption, distribution, biotransformation, and elimination (Turnheim, 2003) drug–drug interactions, or drug–disease interactions (Fulton & Allen, 2005; Mallet et al., 2007). Resulting alterations of the pharmacokinetic phenotype are reflected by ‘abnormal’ C/D or MR (Hiemke et al., 2011). ‘Abnormal’ values of C/D or MR for antidepressant drugs may also reflect pharmacogenetic abnormalities, especially of the cytochromal enzymes CYP2D6 and CYP2C19 (Garriock et al., 2010a,

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2010b). C/D ratios are higher in poor metabolizers (PM) than in extensive, intermediate or ultra-rapid metabolizers (EM, IM, UM) for CYP2D6 among patients taking for example nortriptyline, imipramine or venlafaxine and similarly for CYP2C19 among patients treated with citalopram or escitalopram (Kirchheiner et al., 2004). de Leon (2009) reports in his article about the future of personalized prescription in psychiatry that environmental and personal reasons or genetic factors may make the patient behave as a PM. This means that with a given dose the patient may have an unexpectedly high serum concentration because of a genetic reason, the use of an interacting drug (an environmental reason) or renal or hepatic insufficiency (a personal reason). Each of these factors decreases the elimination of the drug, which is reflected by alterations in blood concentrations and thus an objective indicator for a patient’s individual phenotype (Mallet et al., 2007). Deviations from expected reference ranges (Hiemke et al., 2011) can show what kinds of enzymes or genes are involved. Drug concentration in blood and site of action occupancy Sufficient occupancy of a molecular target is usually a necessary condition for drug response. Nowadays it is possible to study the serotonin (5-HT) transporter (SERT) function in living humans by means of either positron emission tomography (PET) or single photon emission computed tomography (SPECT) (Catafau et al., 2006; Grunder et al., 2011). Investigation of the clinical pharmacokinetics of psychotropic drugs with PET provides important information about the relationship between serum levels of the psychotropic drug and the proportion of target molecules (neuroreceptors, transporters) occupied over time. If the relationship between target occupancy and serum concentration of the drug is established, drug concentration is a biomarker concerning occupancy of target structures in an individual psychiatric patient. PET studies have brought about highly relevant information for the determination of optimal serum concentrations of multiple psychotropic drugs which is reviewed in a special issue by Grunder and co-workers (2011). To date, several imaging studies measured SERT occupancy with SSRI (Catafau et al., 2006; Parsey et al., 2006; Takano et al., 2006; Voineskos et al., 2007). Serum concentrations of, for example, citalopram, paroxetine, fluoxetine, and sertraline were shown to correlate well with serotonin transporter occupancy (Meyer et al., 2004). Particularly, Meyer and colleagues (2004) found that at least 80% occupancy should be attained for optimal


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clinical outcome. There is generally, with the exception of fluvoxamine, an excellent agreement between the data from PET studies of SERT occupancy and therapeutic reference ranges (Grunder et al., 2011; Hiemke et al., 2011). The binding of the selective serotonin and noradrenaline reuptake inhibitor (SSNRI) venlafaxine has been characterized in regard to the SERT and the norepinephrine transporter (NET) by Takano et al. (2013) in non-human primates. The active moiety (venlafaxine plus O-desmethylvenlafaxine) concentration of 58 ng/mL was associated with 80% SERT occupancy results. The calculated active moiety concentration associated with 80% NET occupancy was 108 ng/mL. The only TCA which has been studied using PET (Suhara et al., 2003), in regard to its binding to the SERT, is clomipramine. About 80% of striatal serotonin transporters are usually occupied with doses above 10 mg. Doses of 25 mg daily or serum concentrations of 25 ng/mL almost completely occupy the SERT. All PET studies on SERT or NAT occupancy reported so far in the literature that included serum concentration measurements of antidepressant drugs have shown that target structure occupancy correlates well with serum concentrations. Antidepressant drug concentrations in blood may therefore be regarded as valid measures for the amount of occupancy of structures that are considered to be the primary targets for antidepressant drug actions. Drug and metabolite concentrations in blood, biomarkers for clinical improvement The use of TDM is based on the assumption that there exists a relationship between serum concentrations and clinical effects (therapeutic improvement, side effects and adverse effects; Hiemke et al., 2011). Lithium is the best example for this assumption. The serum concentration of lithium is therefore a valid biomarker to guide treatment with lithium and attain highest possible efficacy and safety. It is well documented that serum concentrations correlate with clinical improvement and side effects (Collins et al., 2010; Eagles et al., 2000; N. Gupta, 2001; Sharma et al., 2009; Wilting et al., 2009). It has also been shown that the optimal serum concentration range of lithium depends on whether the patient is in an acute manic episode or in need of maintenance therapy (Sharma et al., 2009). Gelenberg et al. (1989) concluded in a comparison of standard and low serum levels of lithium for maintenance treatment of bipolar disorder that doses resulting in serum lithium levels from 0.8 to 1.0 mmol/L are more effective than those that result in lower serum lithium concentrations.

For TCAs, several clinical studies have demonstrated the usefulness of drug concentration-based dosing (Glotzbach & Preskorn, 1982; Hiemke et al., 2011; Preskorn & Fast, 1991; Ulrich & Lauter, 2002). Perry and co-workers (1994) discovered a significant curvilinear relationship between therapeutic response and the serum concentrations of nortriptyline, amitriptyline, and imipramine. For desipramine, a significant linear relationship between therapeutic response and desipramine concentration in serum was observed. In two studies, Preskorn (1986), and Preskorn and Fast (1991), the response rates within the therapeutic range of the TCAs (Table 1) were significantly higher compared to response rates outside the therapeutic reference range. Additionally, a retrospective study by Pfuhlmann and colleagues (2007) provided further evidence that there exists a relationship between amitriptyline and clomipramine serum levels, but not for drug doses, and clinical response in everyday clinical practice. This was also shown by Müller and colleagues (2003). Most TCAs are considered to be a potentially inappropriate medication in the elderly because of, for example, the anticholinergic activity and the cardiotoxic potential (Holt et al., 2010). Nevertheless, the use of TCAs in elderly patients is sometimes unavoidable. Because of the underrepresentation of elderly patients in clinical trials and consequently the lack of valid data, special recommendations concerning the antidepressant serum concentration in this sensitive subpopulation are lacking. Geriatric patients are supersensitive to antidepressant drugs, and the increased sensitivity may be associated with age-related pharmacodynamic and pharmacokinetic changes (Mangoni and Jackson, 2004; Mclean, 2004). Pharmacodynamic alterations in the elderly are less well elucidated than pharmacokinetic changes. Nevertheless, it has been proposed that age-related decrement in homeostatic mechanisms in addition to alteration in receptor properties or signal transduction and an increased permeability of the blood–brain barrier could lead to increased sensitivity (Bressler & Bahl, 2003; Turnheim, 2003). Compared with middle-aged people, age-related pharmacodynamic changes may have consequences for therapeutic response with low serum levels of the drug. Therefore, the optimal tolerable concentration could be below the therapeutic reference range (Hiemke et al., 2011). The reference ranges recommended in the guidelines of the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) (Hiemke et al., 2011) for antidepressants (Table 1) are based on studies in middle-aged people. Considering age-related peculiarities, blood concentration-guided dosing can help to find the optimal concentration for individual elderly patients.


Drug concentration in blood to guide psychopharmacotherapy A shortage of clinical data concerning the relationship between therapeutic effect and serum concentration (Hiemke, 2008) limits the use of drug concentration-based dosing of selective serotonin reuptake inhibitors (SSRIs). Lundmark and colleagues (2000a) revealed that serum concentrationbased dosing resulted in using minimum effective doses of SSRIs, making it especially cost-effective in elderly patients. A recent observational study from Ostad-Haji et al. (2011) revealed that citalopram serum concentrations on day 7 of treatment are predictive for later non-response. It was shown that patients with a serum citalopram concentration of 50 ng/mL or greater had a better treatment outcome and a shorter duration of hospitalization compared with patients with drug concentrations of less than 50 ng/mL. Similar findings were reported for paroxetine (Gex-Fabry et al., 2007). Serum level measurements of antidepressants can assist the physician to infer whether the lack of treatment response is due to non-compliance, or whether serum concentrations are below the therapeutic reference range, or simply because of a nonresponse for the individual patient concerning the drug. However, the value of drug concentration in blood as a biomarker for onset of action and therapy response depends strongly on the validity of the recommended therapeutic reference ranges (Hiemke et al., 2011) on which TDM is based. More studies are needed to establish that the findings for citalopram (Ostad Haji et al., 2011) and paroxetine (Gex-Fabry et al., 2007) may also be applied to other new antidepressant drugs. Drug concentration in blood, a biomarker for adverse effects A highly selective drug that specifically affects a distinct target structure in one particular pathway will theoretically achieve the optimal biological effect with the added benefit of minimal side effects. The use of ‘dirty drugs’ such as TCAs that affect more than one signalling pathway are often associated with multiple unwanted clinical effects. As an example, amitriptyline has a strong action on the serotonin transporter (SERT) and moderate effects on the norepinephrine transporter (NET), and this is what induces the wanted antidepressant clinical effect (Coppen & Wood, 1979; Fuxe et al., 1977; Ghose & Coppen, 1977). However, amitriptyline also binds to muscarinic acetylcholine receptors (Pascual & Woodhouse, 1994) which explains the strong anticholinergic activity of the drug, also in therapeutic doses. With ‘dirty drugs’ especially, but also with selective drugs, side effects and wanted clinical effects can occur at the same time when different target molecules (in the central and peripheral nervous

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system) are affected by one drug. In the elderly patients particularly, sensitivity to neuroleptic drugs is increasing, leading to adverse drug reactions such as anticholinergic side effects (Hilmer et al., 2007; Turnheim, 2003). Physicians should be aware of this fact. If unwanted side effects come along with wanted clinical effects, they should make a strong risk–utility analysis with the help of regular serum level measurement. A simple dose reduction, resulting in lower serum levels, can increase the tolerability without a decrease in therapeutic response. Thus, dosage is a poor predictor for side effects, because the serum concentration at the site of action that affects the wanted and unwanted effects is highly variable. Toxicity of SSRIs is low in comparison to most of the tricyclic antidepressants (Baumann, 1996; Degner et al., 2004; Hiemke & Hartter, 2000; Taylor, 2008; White et al., 2008). For selective, well tolerated drugs with a broad therapeutic index such as SSRIs, data concerning the tolerability of SSRIs in relationship to their serum concentration are lacking. However, Coupland et al. (2011) found no evidence that the use of SSRIs or drugs in the group of other antidepressants (no tricyclic and related antidepressants or SSRIs) was associated with a reduced risk of adverse outcomes compared with TCAs in elderly patients. Furthermore, the associations with the adverse outcomes were significantly different between the individual drugs. For that reason, it is necessary for future studies to analyse the concentration dependency of adverse drug reaction (ADR) for every single drug of the newer antidepressants. So far, only a few clinical studies have analysed a dependency between serum concentration and a certain kind of ADR. They deal with seizures, cardiac effects and anticholinergic effects. Seizures Among antidepressants, TCAs remain a common cause of drug-induced seizures. Preskorn and Fast (1992) identified eight patients who, during routine therapy with conventional doses of TCAs, experienced elevated serum concentrations and suffered a grand mal seizure. They correlated the incidence of TCA-induced seizures with TCA serum levels (mean amitriptyline serum concentration 731 ng/mL). The only risk factor that emerged for experiencing seizures was an elevated serum TCA concentration. The authors concluded that therapeutic drug monitoring can reduce the incidence of TCA-induced seizures by allowing for rational dose adjustment. Bupropion and venlafaxine have also emerged as common causes of drug-induced seizures (Thundiyil et al., 2007). So far, however, data on blood concentration dependency of seizures are lacking for these


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antidepressants. Additionally, in a cohort study of elderly people (aged 65 years and older), Coupland (2011) demonstrated that SSRIs and groups of other antidepressant drugs are associated with an increased risk of epilepsy/seizures. However, blood levels of the drugs were not measured. With older people taking multiple medications, the physician should be aware of the increased sensitivity concerning convulsive threshold-lowering drugs (Mangoni & Jackson, 2004), because of pharmacodynamic and pharmacokinetic interactions (Mallet et al., 2007; Turnheim, 2003). Data are so far lacking for elderly patients who have an increased risk for seizures under antidepressant drugs already at concentrations considered as therapeutically effective for patients aged younger than 65 years (Table 1). Therefore neither doses nor blood levels could be defined for those patients that are associated with an increased risk. Cardiac effects As a result of the high prevalence of concomitant psychiatric and cardiovascular disease in the elderly (Jeste et al., 1999; Lapid & Rummans, 2003), psychopharmacological treatment should consider cardiac risk factors, especially in elderly patients. Importantly, there is a significant correlation between cardiac disease and depression (Krishnan et al., 2002). Having either one of these diseases appears to increase the risk of acquiring the other (Glassman et al., 1998). TCAs have been associated with both cardiac arrhythmias and sudden cardiac death (Pacher & Kecskemeti, 2004), while the SSRIs are generally regarded safer concerning cardiovascular side effects. Nevertheless, certain SSRIs have demonstrated pro-arrhythmic and hypotensive effects (Pacher & Kecskemeti, 2004). Cardiac and cardiovascular side effects are important to regulate in clinical practice, especially in elderly patients with increased risk of falls and cardiac toxicity. By focusing on the challenges of treating elderly people with co-morbid cardiovascular disease, TDM can be an important component of their medical management. Nearly 30 years ago, Christensen and colleagues (1985) investigated cardiovascular effects of amitriptyline in the treatment of elderly depressed patients. Thirteen elderly patients were treated with a fixed 100 mg dose of amitriptyline. In all patients, the sum of concentrations of amitriptyline and nortriptyline exceeded 130 ng/mL (therapeutic reference range: 80–200 ng/mL; Hiemke et al., 2011). During treatment, a transient increase in the supine heart rate was observed along with orthostatic drop in blood pressure. In addition, PQ and QRS intervals were markedly increased during treatment. The authors of this study detected significant changes in

the systolic time intervals indicating impairment of myocardial conduction and contractility. Ten years later, Rechlin et al. (1995) demonstrated a significant serum concentration-dependent decrease in blood pressure variation in patients treated with amitriptyline (mean serum level 239 ng/mL). Meanwhile, this study showed that the heart rate of participants was significantly elevated compared to controls. Rodriguez de la Torre and colleagues (2001) were able to confirm these results for amitriptyline some years later. They observed a significant correlation between serum concentrations and heart rate, ECG intervals, and orthostatic hypotension. Also, a strong correlation between the decrease in systolic pressure and antidepressant serum concentration was detected, suggesting that antidepressant serum level is a better biomarker for cardiac effects than the dose. Rechlin et al. (1995) referred to a study by Low and Opfer-Gehrking (1992) who had studied the effects of amitriptyline on autonomic functions. Patients who received 75 mg amitriptyline (mean serum levels below 85 ng/mL) showed no change in heart rate variability during deep respiration, representing muscarinergic (M2) receptor function. Located at the heart, stimulation of the M2 receptor via acetylcholine leads to an activation of cardiac potassium channels, resulting, for example, in a decrease in heart rate. In a study of young and elderly volunteers receiving 50 mg amitriptyline, Swift and colleagues (1981) reported similar results concerning cardiovascular effects of the drug. No conclusive changes in either resting heart rate or blood pressure were observed. Thus, in the lower therapeutic reference range, amitriptyline appears to be safe, for risk patients as well, concerning cardiovascular and cardiac toxicity. Furthermore, in regard to cardiac toxicity, some antidepressants prolong the QT interval in the electrocardiogram (Holt et al., 2010) which is an established risk factor for polymorphic ventricular arrhythmias (TdP). There are several pharmacodynamic mechanisms leading to QT prolongation, most established being the blocking of the rapid component of the delayed rectifier potassium channel (IKr) (Roden, 2004). IKr is generally used as an anti-target in research and in the development of new active pharmaceutical agents, where the IC50 and pharmacokinetics are important factors. Therefore, it is clear that IKr binding is dependent on local concentration, and hence on serum levels. The best documented drug to be associated with QT prolongation is lithium, where TDM is obligatory during treatment. Concentrations above 1.2 mmol/L are prone to prolong the QT interval significantly

Drug concentration in blood to guide psychopharmacotherapy 560

antidepressants in some way, but for citalopram and fluoxetine there are reported cases of TdP following an overdose. This is easily explainable by looking at the IC50 values of these drugs, which exceed the therapeutic range more than three-fold. Therefore, in patients with a slowed metabolism, it is even more important to have a look at serum levels that may signalize a possible risk of TdP.

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Many commonly prescribed drugs have anticholinergic properties which can induce side effects (e.g. delirium), especially in an ageing brain with increased sensitivity (Carnahan et al., 2006). Central anticholinergic effects depend on the agent and the concentration in the blood (Chew et al., 2008). Studies conducted in older adults have shown that drugs which block muscarinic receptors cause, for example, impairments in various cognitive functions, tachycardia, urinary retention, agitation, and delirium (Cancelli et al., 2009; Luukkanen et al., 2011). Chew and colleagues (2008) measured the anticholinergic activity (AA) of medications commonly used by older adults. An in vitro radioreceptor assay was used to investigate the AA of 107 medications. For medications that showed detectable AA, average steady-state peak serum concentrations (Cmax) in older adults were used to estimate relationships between in vitro dose and AA. At typical doses of amitriptyline or doxepin (150 mg/day) administered to older adults demonstrated AA exceeding 15 pmol/mL. Nortriptyline and paroxetine had AA values of 5 to 15 pmol/mL. For citalopram, escitalopram, fluoxetine, lithium, and mirtazapine values were below 5 pmol/mL. Duloxetine demonstrated AA only at the highest concentrations tested.

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Fig. 3. Dependency of QTc time on serum levels of citalopram (computed from Unterecker et al., 2012). Vertical lines indicate the therapeutic reference range of 50 to 110 ng/mL and the laboratory alert level of 220 ng/mL (Hiemke et al., 2011).

(Hsu et al., 2005). Pedersen et al. (1982) established a good correlation between ECG changes and serum levels of tricyclic antidepressants. Other studies have demonstrated this correlation with, for example, citalopram (Isbister et al., 2004; Unterecker et al., 2012). In Fig. 3, data computed from a case report of Unterecker et al. (2012) are shown. There is a linear correlation between serum levels and the heart rate corrected QT time (QTc). Data have also demonstrated that overdose of sertraline causes TdP (de Boer et al., 2005). Furthermore, research has shown that drugs blocking the IKr in concentrations close to the therapeutic serum concentration (i.e. drugs with a low margin) have a higher risk of causing arrhythmias as opposed to drugs with a high margin (Redfern et al., 2003). A correlation with QT prolongation has been reported for basically all 20

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Serum concentration of amitriptyline(ng/mL) Fig. 4. Dependency of in vitro anticholinergic activity in serum (SAA) and in vivo consequences on salivary flow from concentrations of amitriptyline in serum (computed from Blackwell et al., 1978 and Chew et al., 2008). Vertical lines indicate the therapeutic reference range of 80–200 ng/mL, and the laboratory alert level of 300 ng/mL (Hiemke et al., 2011) for amitriptyline plus nortriptyline.

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Fig. 5. Dependency of in vitro anticholinergic activity (pmol/mL) on serum levels of nortriptyline (computed from data by Chew et al., 2008). Vertical lines indicate the upper and lower threshold levels of the therapeutic reference range of 70 to 170 ng/mL (Hiemke et al., 2011) for nortriptyline.

Fig. 4 shows the dependency of in vitro anticholinergic activity and in vivo consequences on salivary flow from concentrations of amitriptyline in serum. In addition, Fig. 5 shows the dependency of in vitro anticholinergic activity on serum levels of nortriptyline. The in vitro AA for a serum concentration of 117 ng/mL of nortriptyline (AGNP therapeutic reference range: 70–170 ng/mL) (Hiemke et al., 2011) is equivalent to 18 pmol/mL. Besides the AA, some studies have investigated the influence of dose or serum concentrations on peripheral and central anticholinergic side effects. Blackwell and co-workers (1978) determined anticholinergic effects with amitriptyline intake in young female volunteers. Results demonstrated that 5 h after administration of amitriptyline a significant reduction in salivation was observed. Decreases for 25, 50, and 100 mg of amitriptyline were 35.4%, 56.2%, and 70.4% (Fig. 5), respectively. Swift and colleagues (1981) reported similar findings. With single doses of 50 mg amitriptyline (expected concentration in blood: 68 ng/mL) (Hiemke et al., 2011) salivary flow was reduced by about 50% at 5.5 h after ingestion of the drug. Gupta et al. (1999) characterized pharmacokinetic and pharmacodynamic properties of OROS® and immediate-release (IR) amitriptyline formulations and observed that, of the anticholinergic effects, decreased saliva weight and dry mouth correlated well with amitriptyline concentrations. After the IR night-time dosing, saliva production showed most reduction (65%) from baseline when the amitriptyline concentration was about 25 ng/mL. The serum drug concentration required for 50% of the maximal dry mouth effect was estimated to be 30. Likewise, the authors correlated amitriptyline

serum concentrations and side effects. Anticholinergic effects were associated with peak serum drug concentrations of amitriptyline that are attained about 2 to 3 h after drug intake. At that time concentrations of the metabolite nortriptyline are rather low. Dampening of the peak level of amitriptyline by application of a slow-release formulation (OROS), the incidence of anticholinergic effects decreased in comparison with an intake of immediate release formulation (Gupta et al., 1999). Besides peripheral ADRs, anticholinergic central nervous system (CNS) toxicity of TCAs, for example cognitive impairment, is serious and difficult to diagnose early in its course, especially in demented, elderly multi-morbid patients (Luukkanen et al., 2011). In a study by Preskorn and Simpson (1982), 100 inpatients receiving amitriptyline were monitored for serum drug concentration. Fourteen patients receiving routine doses of amitriptyline developed serum drug concentrations above 300 ng/mL (therapeutic reference range: 80–200 ng/mL) (Hiemke et al., 2011). Of the seven patients with serum levels above 450 ng/mL, six developed a drug-induced delirium as opposed to none of the seven patients with levels below 450 ng/mL. These results were later confirmed by Preskorn and Jerkovich (1990). A systematic population study of TCA-induced CNS toxicity found in 976 TCA-treated patients, that 58 individuals (6%) developed TCA-induced CNS toxicity. The risk of this toxicity was positively correlated with TCA serum levels. For levels greater than 450 ng/mL, the risk increased by 67%. Concomitant illnesses can mask anticholinergic side effects, for example constipation or urinary retention (Mintzer & Burns, 2000). Discovering these unwanted effects and finding a causal relationship due to the drug can be difficult without serum level measurement. Late-life depression is often associated with other medical illnesses besides psychiatric symptoms (Alexopoulous, 2005). They include somatic complaints (Buchtemann et al., 2012) which are further aggravated by peripheral body changes and cognitive impairment. Physicians have to differentiate between reactions due to the drug and physical suffering due to underlying illnesses. Side effects can often not be attributed objectively to a distinct cause. With increasing AA, the peripheral and central blockade of muscarinic acetylcholine receptors is increasing, influencing different functions in the human body at the same time. In the psychiatric setting, measurement of drug concentrations in blood helps to find out whether anticholinergic side effects are due to high drug concentrations and whether lowering of the dose can be recommended to improve tolerability without loss of therapeutic potency.



Other side effects The prevalence and severity of ADRs increases with lithium concentrations in blood, also if the dose is adjusted in accordance with the mood state. Gelenberg et al. (1989) concluded in a comparison of standard and low serum levels of lithium that doses resulting in serum lithium levels from 0.8 to 1.0 mmol/L are associated with a higher incidence of overall side effects compared to lower serum levels. Additionally, Chen and co-workers (2004) determined that in patients with chronic lithium intoxication (ⱖ 1.2 mEq/L), the frequency of severe symptoms was higher than in those with acute intoxication (ⱖ 1.2 mEq/L). Coppen et al. (1983) detected that patients who underwent a dosage reduction with consequently lower serum lithium levels (0.45–0.79 mmol/L) had significantly decreased thyroid stimulating hormone levels. Total subjective side effects score and tremor was also reduced. Tellian and Rueda-Vasquez (1993) were able to confirm these results as they found a positive correlation between changes in serum lithium concentration and changes in thyroid stimulating hormone (TSH) level. These results suggest that lithium-induced changes in TSH are serum level dependent. The elderly have a higher risk of side effects under lithium than the general adult population and require more laboratory monitoring (Conney & Kaston, 1999). Based on pharmacokinetic differences, elderly patients require only 30 to 50% of the lithium dosage of young patients than younger patients (Hardy et al., 1987). Dehydration, comedication affecting urinary excretion, and renal diseases can increase lithium levels (Stoudemire et al., 1990). Cognitive side effects, weight gain, and lack of coordination have been reported as the most subjectively distressing side effects that often lead to non-compliance (Gitlin et al., 1989). Tremor affects up to 65% of patients treated with lithium, and a severe tremor may be a sign of toxicity (Gelenberg & Jefferson, 1995).

Conclusion Drug and metabolite concentrations in blood can be objectively measured with high precision and regarded as valid biomarkers related to pharmacokinetic phenotype, compliance, therapeutic effects, and side effects of psychotropic drugs. Established relations between drug concentration in blood and clinical effects are the basis for therapeutic drug monitoring (TDM). It uses drug and metabolite concentration measurements in blood to objectify whether lack of improvement may be due to suboptimal drug concentrations that are required

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for response, or whether complaints of the patient or visible symptoms are drug concentration related or a symptom of the psychiatric illness (Hiemke et al., 2011). Blood concentration measurement may reduce the risk of central nervous and cardiovascular toxicity which makes TDM particularly useful for prevention of adverse reactions. We have shown here the state of the art for antidepressant drugs and that good evidence is given for these suggestions. Data reported so far reflect an enormous interindividual variability in a population undergoing psychopharmacological therapy. Drug concentration is a measure for the individual pharmacokinetic phenotype, and this information is essential for personalized pharmacotherapy. It may give rise to the identification of factors influencing treatment response and the occurrence of adverse drug reactions, such as genetic abnormalities, co-medication, or adherence to the medication. The drug concentration in blood should be used in clinical practice as a biomarker to guide antidepressant treatment with established drugs in an era that is characterized by a lack of innovative drugs and an increasing number of older-aged patients who require complex pharmacological therapies. Declaration of interest: C. Hiemke has received speaker’s or consultancy fees from Bristol-Meyers Squibb, Janssen Cilag, Pfizer, Lilly and Servier. He is managing director of the psiac GmbH which provides an internet based drug-drug interaction program for psychopharmacotherapy. All authors report no conflict of interest with this publication. References Akerblad, A.C., Bengtsson, F., Holgersson, M., von Knorring, L. & Ekselius, L. (2008). Identification of primary care patients at risk of nonadherence to antidepressant treatment. Patient Preference and Adherence, 2, 379–386. Alexopoulos, G. S. (2005). Depression in the elderly. Lancet, 365, 1961–1970. Altar, C.A. (2008). The Biomarkers Consortium: On the critical path of drug discovery. Clinical Pharmacology and Therapeutics, 83, 361–364. Baumann, P. (1996). Pharmacokinetic-pharmacodynamic relationship of the selective serotonin reuptake inhibitors. Clinical Pharmacokinetics, 31, 444–469. Bengtsson, F. (2004).Therapeutic drug monitoring of psychotropic drugs. TDM ‘nouveau’. Therapeutic Drug Monitoring, 26, 145–151. Blackwell, B., Stefopoulos, A., Enders, P., Kuzma, R., & Adolphe, A. (1978). Anticholinergic activity of two tricyclic antidepressants. Am J Psychiatry, 135, 722–724. Bressler, R., Bahl, J. J. (2003). Principles of drug therapy for the elderly patient. Mayo Clin Proc, 78, 1564–1577. Buchtemann, D., Luppa, M., Bramesfeld, A., & Riedel-Heller, S. (2012). Incidence of late-life depression: a systematic review. J Affect Disord, 142, 172–179. Caccia, S. & Garattini, S. (1992). Pharmacokinetic and pharmacodynamic significance of antidepressant drug metabolites. Pharmacological Research, 26, 317–329.


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