Psychiatric drugs are among the most frequently applied drugs in Germany (Schwabe and Paffrath, 2016; Schwabe et al., 2017). Drug action is related to its serum concentrations. Serum concentrations are affected by genetic polymorphisms of cytochrome P-450 (CYP) enzymes (Hiemke et al., 2018), age (Unterecker et al., 2012; Hansen et al., 2017), sex (Hansen et al., 2017), and weight (Aichhorn et al., 2006), but also smoking (Desai et al., 2001; Unterecker et al., 2012; Hiemke et al., 2018) and comedications (Bergemann et al., 2006; Hiemke et al., 2018). To optimize the pharmacotherapy in psychiatry, therapeutic drug monitoring (TDM) is applied to quantify and interpret drug blood levels. Thereby, individualized dosages for patients may be tailored on the basis of TDM (Hiemke et al., 2018). Typical indications for TDM include unexpected side effects, uncertain drug adherence, or pharmacokinetic drug–drug interactions (Hiemke et al., 2018). Although it has been reported that serum levels of antidepressants show only incomplete correlation with clinical improvement (Preskorn, 2014), administration of TDM when administering tricyclic antidepressants was shown to improve the clinical response (Muller et al., 2003).
Specific attention has repeatedly been paid to the impact of sex, age, and smoking on psychopharmacological treatment. Patients with schizophrenia smoke more frequently than the general population (58–88 vs. 23%; Sacco et al., 2005). Among patients with depressive disorders, 40–50% are smokers (Oliveira et al., 2017). The polycyclic aromatic hydrocarbons (PAH) in cigarette smoke are known to induce CYP isoenzyme CYP1A2 (Kroon, 2007; Al-Arifi et al., 2012), but other ingredients in tobacco smoke have also been suggested to interact with metabolizing enzymes (Zevin and Benowitz, 1999). Several studies (Haring et al., 1989; Seppala et al., 1999; Haslemo et al., 2006) have observed decreased serum concentrations of drugs predominantly metabolized by CYP1A2, especially clozapine (CLZ), in smokers. However, data on other psychiatric drugs, which are not or not predominantly metabolized by CYP1A2 show inconsistent results (Perry et al., 1986; Zevin and Benowitz, 1999; Lind et al., 2009; Majcherczyk et al., 2012; Schoretsanitis et al., 2017). For example, only one out of three studies reported significantly lower serum concentrations of amitriptyline (AMI) in smokers compared with nonsmokers (Linnoila et al., 1981; Desai et al., 2001). Decreased serum concentrations were also reported among smokers for imipramine (Zevin and Benowitz, 1999), fluvoxamine (Zevin and Benowitz, 1999; Oliveira et al., 2017), duloxetine (Oliveira et al., 2017; Augustin et al., 2018), mirtazapine (MIRT; Oliveira et al., 2017), and trazodone (Oliveira et al., 2017), but the evidence for other antidepressants is rare. Among antipsychotic drugs, CLZ and olanzapine have mainly been studied so far (Sagud et al., 2009) and smoking 7–12 cigarettes daily appeared to strongly induce the metabolism of these drugs (Haslemo et al., 2006). In preclinical studies, quetiapine (QUET) metabolism was not affected by cigarette smoke (Nemeroff et al., 2002). Risperidone (RISP) was investigated in a naturalistic setting and the dose-corrected serum levels of 9-OH-risperidone (9-OH) were significantly lower in smokers compared with nonsmokers, but the clinical relevance remained unclear (Schoretsanitis et al., 2017).
Age and sex are known to affect serum concentrations of psychopharmacological drugs as well. The influence of age on serum levels of different psychopharmacological drugs was shown in a number of studies (Haring et al., 1989; Castberg et al., 2007, 2009; Reis et al., 2009; Unterecker et al., 2012, 2013), as was the influence of sex (Aichhorn et al., 2005; Sigurdsson et al., 2015; Castberg et al., 2017; Hansen et al., 2017), but only some studies have considered the confounding parameters smoking, age, and sex in combination.
We hypothesized that smoking might be an underestimated pharmacokinetic parameter in the psychopharmacological treatment of drugs not mainly metabolized by CYP1A2. The aim of the present analysis was therefore to quantify the impact of smoking on the serum concentrations of AMI, doxepin (DOX), MIRT, venlafaxine (VEN), QUET, and oral RISP. In addition, CLZ was included in the analysis to control for a plausible study sample.
Patients and methods
Serum concentrations of drugs and their metabolites were determined between January 2009 and December 2010 in the Department of Psychiatry, Psychosomatics, and Psychotherapy of the University Hospital of Würzburg during routine TDM. All procedures performed in the analysis involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The target drugs and their metabolites were the antidepressants AMI and nortriptyline (NOR), DOX and nordoxepin (N-DOX), MIRT, VEN, and O-desmethylvenlafaxine (ODV) and the antipsychotics CLZ and N-desmethylclozapine (NCLZ), QUET, and RISP and 9-OH. Inpatients and outpatients taking at least one of the target drugs for any psychiatric disorder were included in the analysis. TDM request forms were completed by the attending physician to provide information on the daily dose of the target drug, sex, age, and smoking habit of the patient. In case of missing data on these variables, patients were excluded. To avoid bias in case of multiple serum concentration determinations for one drug in the same patient, only the most recent measurement was included. TDM analyses were carried out according to the AGNP-TDM expert group consensus guideline (Baumann et al., 2004). Blood withdrawal was performed in the morning (trough level) at steady state.
Quantification of the drugs
Serum was obtained by centrifugation at 1850g for 10 min (Hettich Centrifuge Rotanta 460 R; Andreas Hettich GmbH & Co. KG, Tuttlingen, Germany). Before analysis, serum samples were again centrifuged at 21 380g for 6 min (Hettich Centrifuge Mikro 200; Andreas Hettich GmbH & Co. KG, Tuttlingen, Germany). Sample cleanup was performed by online solid-phase extraction. An aliquot of 100 µl plasma was subjected to an isocratic reversed-phase high-performance liquid chromatography method with ultraviolet-absorbance detection and fluorescence detection. Lower limits of quantification were 3 ng/ml for AMI and NOR, 2 ng/ml for DOX, N-DOX, MIRT, VEN and ODV, CLZ and norclozapine, 4 ng/ml for QUET, and 3 ng/ml for RISP and also for 9-OH. Further details on the analytical procedure are provided in Supplementary Material (Supplemental digital content 1, http://links.lww.com/ICP/A63). Internal quality control samples were performed in each analytical series. For each target drug, the precision and accuracy was less than 10%. The laboratory was certified by a quality control program, with external control samples being analyzed quarterly.
Data of request forms were transferred to a database including sex, age, smoking habit, applied daily dose, and serum concentrations of the target drugs. To normalize daily doses, dose-corrected serum concentrations (C/D) were calculated. Further, the metabolite to parent-compound ratio was calculated to enable the evaluation of the individual pharmacokinetic phenotype according to Hiemke et al. (2018). Arithmetic means with SDs were calculated for descriptive analyses. A multiple linear regression analysis (forced entry method) was carried out to analyze the influence of age, sex, and smoking on C/D and the ratio metabolite/parent drug. Primary outcomes were the influence of smoking on the active moiety in case of drugs with pharmacologically active metabolites otherwise on the parent compound and the influence on the ratio metabolite/parent drug. Statistical analysis was carried out using the software IBM SPSS Statistics, version 24 (IBM Corporation, Armonk, New York, USA); P values of less than or equal to 0.05 were considered to be statistically significant.
Amitriptyline and nortriptyline
The group of patients (n=503) included 218 men, of whom 75 were smokers and 143 were nonsmokers. Accordingly, the group included 285 women, with 88 smokers and 197 nonsmokers. The mean age was 47.12±12.29 years (range: 18–87 years). The daily doses ranged from 10 to 300 mg (mean±SD: 117.05±49.56 mg). The mean serum concentrations (±SD) of AMI, NOR, and AMI+NOR were 82.15±54.50, 72.75±58.49, and 154.98±100.03 ng/ml, respectively. The mean C/D (±SD) of AMI, NOR, AMI+NOR, and the ratio NOR/AMI were 0.77±0.62, 0.69±0.69, 1.46±1.16 (ng/ml)/(mg/day), and 1.01±0.82, respectively (Table 1).
Linear regression analysis showed a significant effect of age [P<0.001, β (standardized)=0.163], smoking (P=0.038, β=−0.092), and sex (P=0.037, β=−0.091) on the C/D of AMI. For the C/D of AMI+NOR, only age (P=0.007, β=0.121) and sex (P=0.031, β=−0.096) and for the ratio NOR/AMI, only smoking (P=0.001, β=0.153) showed a significant effect. Therefore, C/D of AMI and AMI+NOR increased with age and was higher in women. Smokers showed lower C/D of AMI and a higher ratio NOR/AMI compared with nonsmokers.
Doxepin and nordoxepin
In a sample of 198 patients, 35 male smokers and 57 male nonsmokers and 27 female smokers and 79 female nonsmokers were included. The mean age of the group was 49.78±15.20 years (range: 18–88 years). The daily dose ranged from 5 to 300 mg (mean±SD: 133.48±59.88 mg). The mean serum concentrations (±SD) of DOX, N-DOX, and DOX+N-DOX were 48.06±42.77, 50.93±50.67, and 99.00±90.42 ng/ml, respectively. The mean C/D (±SD) of DOX, N-DOX, DOX+N-DOX, and the ratio N-DOX/DOX were 0.40±0.40, 0.43±0.51, 0.83±0.89 (ng/ml)/(mg/day), and 1.16±0.74, respectively (Table 1).
According to linear regression analysis, a significant effect of age (P=0.011, β=0.187) and female sex (P=0.025, β=−0.159) on C/D of DOX was observed, with higher values in older patients and in women. C/D of N-DOX and C/D of DOX+N-DOX were also higher in women (N-DOX: P=0.009, β=−0.188; DOX+N-DOX: P=0.011, β=−0.182). The ratio N-DOX/DOX was significantly affected by age (P=0.05, β=−0.143) and smoking (P=0.014, β=0.182), with a lower ratio in older patients and nonsmokers (Fig. 1).
The group of patients (n=572) included 77 male and 77 female smokers and 169 male and 249 female nonsmokers. The mean age was 53.25±16.51 years (range: 18–92 years). The administered daily dose ranged from 7.5 to 75 mg (36.27±14.28 mg). The mean serum concentration (±SD) of MIRT was 52.58±29.86 ng/ml. The mean C/D (±SD) of MIRT was 1.55±0.82 (ng/ml)/(mg/day; Table 1).
C/D was significantly lower among smokers (P=0.002, β=−0.131). In addition, male patients showed a significantly lower C/D compared with female patients (P=0.001, β=−0.132), and C/D increased significantly with age (P<0.001, β=0.156).
Venlafaxine and O-desmethylvenlafaxine
The sample of 534 patients included 221 men (77 smokers and 144 nonsmokers) and 313 women (92 smokers and 221 nonsmokers). The mean age was 47.36±16.48 years (range: 18–88 years). The daily doses ranged between 37.5 and 450 mg (194.03±83.47 mg). The mean serum concentrations (±SD) of VEN, ODV, and AMI+ODV were 136.48±134.55, 211.18±139.00, and 347.65±219.27 ng/ml, respectively. The mean C/D (±SD) of VEN, ODV, VEN+ODV, and the ratio ODV/VEN were 0.75±0.74, 1.16±0.65, 1.90±1.06 (ng/ml)/(mg/day), and 2.67±2.20, respectively (Table 1).
Age and sex showed a significant effect on the C/D of VEN, ODV, and of the sum [age: P<0.001, β=0.201 (VEN), P<0.001, β=0.232 (ODV), P<0.001, β=0.282 (sum), sex: P=0.001, β=−0.145 (VEN), P=0.004, β=−0.121 (ODV), P<0.001, β=−0.175 (sum)], with higher values in older patients and women. The ratio ODV/VEN was significantly higher in men compared with women (P=0.007, β=0.117). C/D of VEN, ODV, VEN+ODV, and the ratio ODV/VEN was not affected by smoking.
Clozapine and norclozapine
The group of patients (n=106) included 66 men, of whom 34 were smokers and 72 were nonsmokers. Accordingly, the group included 40 women, with 11 smokers and 29 nonsmokers. The mean age was 43.05±13.59 years (range: 20–82 years). The daily doses ranged between 37.5 and 700 mg (273.94±145.47 mg). The mean serum concentrations (±SD) of CLZ and of NCLZ were 365.02±205.89 and 215.88±117.53 ng/ml. The mean C/D (±SD) of CLZ, NCLZ, and the ratio NCLZ/CLZ was 1.58±1.08, 0.95±0.66, and 0.64±0.31 (ng/ml)/(mg/day), respectively (Table 1).
C/D of CLZ was significantly lower in smokers (P=0.02, β=−0.221). C/D of NCLZ and the ratio NCLZ/CLZ were not affected by smoking.
In a sample of 182 patients, of whom 69 were men (31 smokers and 38 nonsmokers) and 113 were women (45 smokers and 68 nonsmokers), the daily doses ranged between 50 and 1200 mg (404.48±230.85 mg). The mean C/D (±SD) of QUET was 0.62±0.52 (ng/ml)/(mg/day; Table 1). The mean age was 44.34±15.35 years (range: 18–84 years).
C/D was not affected by age, sex, or smoking.
Risperidone and 9-OH-risperidone
The patient group (n=136) included 60 men, of whom 20 were smokers and 40 were nonsmokers. Accordingly, the group included 76 women, of whom 22 were smokers and 54 were nonsmokers. The mean age was 42.56±15.28 years (range: 18–85 years). The daily doses ranged between 0.5 and 8 mg (3.15±1.57 mg). The mean C/D (±SD) of RISP, 9-OH, the RISP+9-OH combination, and the ratio 9-OH/RISP were 4.41±6.31, 8.69±6.38, 13.04±9.65 (ng/ml)/(mg/day), and 7.38±12.37 (Table 1).
C/D of RSIP and of the sum RISP+9-OH was significantly higher in older patients [P=0.005, β=0.260 (RISP), P=0.004, β=0.264 (sum)]. C/D of RISP, 9-OH, the RISP+9-OH combination, and the ratio 9-OH/RISP were not affected by smoking.
Drug metabolism and serum concentrations are important variables in clinical response. Here, we present evidence for the effects of smoking on DOX, a drug that has never been investigated previously in this context. In addition, we present initial evidence that smoking modulates metabolism not only through CYP1A2 but also through alternative pathways.
Although the effect of smoking on serum concentrations of drugs has been analyzed repeatedly, only some studies corrected for confounding factors (Desai et al., 2001; Aichhorn et al., 2005; Castberg et al., 2007, 2009, 2017; Reis et al., 2009; Bowskill et al., 2012; Sirot et al., 2012; Unterecker et al., 2012, 2013; Olsson et al., 2015). Therefore, in the discussion, the focus will only be on studies with a comparable design.
AMI is metabolized predominantly through CYP2C19 and CYP2D6 into its active metabolite NOR and the less active 10-hydroxy metabolite (Olesen and Linnet, 1997; Breyer-Pfaff, 2004; Dean, 2012). In terms of the quantitative relations, CYP2C19 and CYP2D6 showed high affinities (Km: 5–13 µmol/l), whereas the affinities of CYP1A2, CYP3A4, and CYP2C9 were lower (Km: 74–92 µmol/l; Olesen and Linnet, 1997). In addition, CYP2C19 showed the highest reaction capacity per mole (Vmax=475 mol/h/mol CYP) in-vitro (Olesen and Linnet, 1997). Furthermore, an in-vitro simulation study suggested that about 60% of the metabolism of AMI depended on CYP2C19 (Olesen and Linnet, 1997). Therefore, demethylation of AMI is mediated by CYP1A2 only to a small extent (Table 2; Olesen and Linnet, 1997).
Like AMI, DOX is metabolized predominantly by CYP2C19 and to a minor extent by CYP2C9 and CYP1A2 (Table 2; Hartter et al., 2002; Kirchheiner et al., 2004). In-vitro studies showed an inhibition of the N-demethylation of DOX by tranylcypromine (CYP2C19) to more than 50%, whereas furafylline (CYP1A2) and sulfaphenazole (CYP2C9) inhibited the N-demethylation to a lesser extent (Hartter et al., 2002).
For AMI and DOX, CYP2C19 seems to be the most important enzyme in their metabolism. Thus, significantly lower C/D of AMI along with an increased ratio of NOR/AMI and a significantly higher ratio of N-DOX/DOX in smokers suggest that smoking may induce CYP2C19 in addition to CYP1A2. In addition, glucuronidation has an impact on the metabolism of AMI and DOX (Liston et al., 2001). Both drugs are substrates of UGT1A3 and UGT1A4 (Liston et al., 2001; Breyer-Pfaff, 2004). A factor that is known to induce glucuronidation is tobacco smoke (Liston et al., 2001). Hence, the metabolism of AMI and DOX might be altered by smoking because of a possible additive effect of UGT1A3 and UGT1A4 induction with CYP1A2 induction (Liston et al., 2001). Nevertheless, for AMI, glucuronidation appears to be a minor pathway in-vivo (Dahl-Puustinen et al., 1989). Furthermore, previously, no difference in the excretion of AMI-glucuronide between smokers and nonsmokers in-vivo was observed (Dahl-Puustinen et al., 1989). For AMI, our results are in accordance with a previous report (Linnoila et al., 1981). However, to our knowledge, no study has focused on smoking and serum levels of DOX so far. Thus, we report for the first time an acceleration of DOX metabolism by smoking.
For MIRT, CYP1A2 is a minor metabolic pathway in its degradation (Table 2; Timmer et al., 2000; Anttila and Leinonen, 2001). In accordance with our results, lower serum concentrations of MIRT were reported previously among smokers (Lind et al., 2009). Moreover, we confirmed that age and sex affected the serum concentrations of MIRT, with lower levels in males and younger patients (Reis et al., 2009; Sirot et al., 2012; Unterecker et al., 2013).
In line with the hypothesis that smoking induces CYP1A2 and possibly CYP2C19, smoking showed no significant effect on the metabolism of VEN. In-vitro studies showed that ODV was formed by CYP2C9, CYP2C19, and CYP2D6 (Fogelman et al., 1999). CYP2D6 was dominant, with the highest intrinsic clearance (Vmax/Km) and the lowest Km (Fogelman et al., 1999). In addition, an in-vitro experiment showed that CYP2D6 influences the O-demethylation, whereas CYP2C19 influences the N-demethylation of VEN and its metabolites (Karlsson et al., 2015). N-demethylation is catalyzed by CYP3A4 and CYP2C19, but is generally a minor metabolic pathway (Sangkuhl et al., 2014). However, the high in-vivo abundance of CYP3A4 will increase the importance of this enzyme and therefore decrease the impact of CYP2C19 (Fogelman et al., 1999). Thus, in the metabolism of VEN, CYP2C19 plays only a minor role and therefore the possible induction of CYP2C19 by smoking will not affect the VEN and ODV concentrations.
In terms of sex and age, to the best of our knowledge, no comparable analysis with correction for the confounder smoking has been reported previously. Therefore, the results of previous studies (Reis et al., 2009; Unterecker et al., 2012; Sigurdsson et al., 2015), showing lower serum concentrations in smokers and younger patients, might be due to ignoring these confounders.
In terms of antipsychotics, CLZ is mainly metabolized by CYP1A2 (Table 2; Olesen and Linnet, 2001; Patteet et al., 2012; Vasudev et al., 2017), which is induced by PAH in cigarette smoke (Tanaka, 1998; Ma and Lu, 2007; Gunes et al., 2009; Al-Arifi et al., 2012), leading to a higher CYP1A2 activity among smokers in-vivo (Dobrinas et al., 2011). In line with previous analyses, CLZ serum concentrations were higher in nonsmokers (Haring et al., 1989; Ulrich et al., 2003). Results from earlier studies on the influence of sex on the plasma concentrations of CLZ are inconsistent, possibly because previous investigations did not take these confounders into account (Castberg et al., 2009, 2017; Olsson et al., 2015).
Previous reports on the effect of smoking on the pharmacokinetics of RISP and 9-OH were also inconsistent (Feng et al., 2008; Schoretsanitis et al., 2017). In agreement with our analysis, Feng et al. (2008) did not observe a significant effect of smoking on RISP and 9-OH. In contrast, Schoretsanitis et al. (2017) reported significantly lower C/D of 9-OH in smokers taking RISP, but the clinical relevance as well as the mechanism remained unclear. Previously, age was identified as a covariate in 9-OH clearance (Feng et al., 2008), whereas in the present analysis, age showed only a significant effect on C/D of RISP and on RISP+9-OH, but not on C/D of 9-OH. In contrast to our analysis, data from controlled clinical trials were used previously (US National Library of Medicine, 2015). The patients were between 18 and 65 years old, resulting in only partly comparable patient characteristics, because of a closer age range than in our sample (US National Library of Medicine, 2015). In addition, contrary to our investigation, in the previous analysis, a population pharmacokinetic model was applied, which correlates variability in plasma concentrations between individuals (Feng et al., 2008). This might also account for the discrepancies in the results. Furthermore, in a previous analysis, ~30% of African–Americans and ~65% of Caucasians were included, but in our analysis, only Caucasians were included (Feng et al., 2008). Ethnic differences in CYP enzymes, especially in CYP2D6 phenotypes, are well known. In African–Americans, a wider range of appearance of poor metabolizers (1.9–7.3%) was described compared with Caucasians (American: 7.7%, German: 7.7%; Bernard et al., 2006). In addition, ultrarapid metabolizers appeared more frequently in White Americans and African–Americans (4.3 and 4.9%) compared with Germans or Danish (both 0.8%; Bernard et al., 2006). Hence, demographic differences may also yield different results for RISP.
Tobacco smoke significantly influenced the pharmacokinetics of the antidepressants AMI, DOX, and MIRT as well as the antipsychotic drug CLZ. As CYP2C19 is a metabolizing enzyme for all of these drugs (Olesen and Linnet, 1997; Hartter et al., 2002; Breyer-Pfaff, 2004; Dean, 2012), we conclude that PAH of tobacco smoke may not only induce CYP1A2 (Kroon, 2007) but also CYP2C19, although to a lesser extent. CYP3A4 was proposed to be induced by smoking (Rahmioglu et al., 2011; Kumagai et al., 2012). However, MIRT, which is mainly metabolized by CYP2D6 and CYP3A4, yet only to a minor extent by CYP1A2 (Timmer et al., 2000), showed significantly lower serum levels in smokers. Therefore, our results may indicate a larger proportion of CYP1A2 in the metabolism of MIRT, or may confirm the induction of CYP3A4 by tobacco smoke. However, the C/D of QUET, which is predominantly metabolized by CYP3A4 (Bakken et al., 2012), did not differ between smokers and nonsmokers. Thus, smoking may have only a minor inductive effect on CYP3A4.
The results of the present analysis should be interpreted with caution because of the retrospective, naturalistic, and explorative nature of this investigation. Retrospective investigations may not prove causality. The major limitation of the analysis was the lack of information on concomitant medications. Comedication is known to severely affect the pharmacokinetics of different drugs, especially by inductors or inhibitors of CYP450 enzymes (Dahlin and Ohman, 2004; Sandson et al., 2005; Aichhorn et al., 2006; Patteet et al., 2012; Schoretsanitis et al., 2017; Paulzen et al., 2018). As polypharmacy increases with age (Hovstadius et al., 2010; Castioni et al., 2017), the risk for pharmacokinetic interactions also increases and may contribute toward higher dose-corrected serum levels among older patients. Moreover, genetic information on the metabolic phenotype, especially for CYP2C19, was missing because of the naturalistic setting. There also was a lack of information on the number of cigarettes smoked by an individual patient, allowing no differentiation of the extent of smoking. In addition, data on body weight and data on the clinical response of each patient were not available. Because of the explorative nature of the investigation, a correction for multiple testing was not applied. However, the large sample size of the present analysis may at least partly compensate for several of these limitations as we could include larger samples than most of the previous naturalistic studies (Edelbroek et al., 1987; Balant-Gorgia et al., 1999; Haslemo et al., 2006; Reis et al., 2007; Lind et al., 2009; Sirot et al., 2012; Unterecker et al., 2012; Schoretsanitis et al., 2017).
Taken together, we provide initial evidence that tobacco smoke may alter the pharmacokinetics of DOX in addition to AMI. Therefore, daily clinical practice needs to consider that smoking may exert prominent, unexpected effects on the pharmacokinetics of administered drugs. Induction of CYP2C19 in addition to CYP1A2 by tobacco smoke may be the underlying molecular effect. Hence, when choosing a medication dose, considering the smoking behavior of the patient is recommended. Also, upon initiation of such a medication, TDM should be recommended. Nevertheless, the clinical relevance of the results remained unclear. For a better understanding of the influence of smoking on the serum concentrations of psychopharmacological drugs, corrected not only for age and sex, an even larger sample size including data from several study centers would be necessary to improve dose individualization in psychiatry.
The authors are very grateful to the staff of the participating TDM laboratory at the Psychiatric Department of the University Hospital of Würzburg, Rainer Burger (medical-technical assistant), Margit Burger (medical-technical assistant), Kerstin Balcioglu (medical-technical assistant), Marion Weyer (medical-technical assistant), and Renate Keil (typist) for processing patient samples.
Conflicts of interest
Prof. Dr Jürgen Deckert is the co-recipient of a grant from the Bavarian State Government for the development of an App to individualize pharmacotherapy of mental disorders. For the remaining authors, there are no conflicts of interest.
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