Levels of PBDEs reported in the environment and biota from different locales worldwide.
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",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"cc796459268324e827219d1d904e4265",bookSignature:"Prof. Moulay Tahar Lamchich",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7196.jpg",keywords:"Induction motor, smart motor, electrical vehicles, energy generation, drives, electromechanical, hybrid transportation, smart control, high efficiency motor, variable speed drives, power electronic, energy efficiency.",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 4th 2018",dateEndSecondStepPublish:"July 25th 2018",dateEndThirdStepPublish:"September 23rd 2018",dateEndFourthStepPublish:"December 12th 2018",dateEndFifthStepPublish:"February 10th 2019",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"21932",title:"Prof.",name:"Moulay Tahar",middleName:null,surname:"Lamchich",slug:"moulay-tahar-lamchich",fullName:"Moulay Tahar Lamchich",profilePictureURL:"https://mts.intechopen.com/storage/users/21932/images/system/21932.png",biography:"Moulay Tahar Lamchich is a Professor at the Faculty of Sciences Semlalia at Marrakech (Morocco). He completed his thesis in electromechanics in September 1991 and received his third cycle degree. Dr. Lamchich received his Ph.D. from the same university in July 2001. His main activity is based on short-circuit mechanical effects in substation structures, control of different types of machine drives, static converters, active power filters. In the last decennia, his research interests have included renewable energies, particularly the control and supervision of hybrid and multiple source systems for decentralized energy production, and intelligent management of energy. He has published more than fifty technical papers in reviews and international conferences. With IntechOpen, he has published two chapters and was editor of the books “Torque Control” and “Harmonic Analysis”. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"44821",title:"The Pharmacological Frontiers in Treatment Resistant Major Depression",doi:"10.5772/53224",slug:"the-pharmacological-frontiers-in-treatment-resistant-major-depression",body:'Major depressive disorder (MDD) is a major burden for society, with a year-prevalence of 5% in the adult population. Usually MDD is treated with psychotherapy or serotonergic and noradrenergic antidepressants. With the first antidepressant, often a Selective Serotonin Reuptake Inhibitor (SSRI), 30-40% of patients achieve symptomatic remission. This rate increases to 67% after ≥4 trials with different classes of antidepressants (Rush et al., 2006). However, non-response (<50% improvement of symptom-severity) occurs frequently and is associated with prolonged suffering by patients and their family members, but also prolonged hospitalisations and increased suicide-rates.
Non-response to more classes of antidepressants is referred to as treatment resistant (or refractory) depression (TRD). TRD is not the same as chronic depression, as a properly treated patient might prove to be treatment resistant within 6 months, while patients suffering from chronic depression have often been undertreated or were non-adherent (also referred to as \'pseudo-TRD\'). In addition, when TRD is considered, a re-evaluation of the patient might reveal unrecognized other axis I disorders (e.g. anxiety and substance abuse disorders), somatic diagnoses or bipolar disorder (Berlim and Turecki 2007a).
Inconsistencies in definitions of TRD impair exact estimations of the prevalence of TRD (Nemeroff 2007), but estimations range between 15-30% of patients. Also, inconsistent definitions diminish transparency in the field of clinical trials to identify the most efficacious next-step treatments, and impair reliable comparisons or meta-analyses of results from next-step studies (Ruhé et al., 2006). Nevertheless, TRD is considered as the main cause of chronic depression with enduring hospitalizations, work-absenteeism and suicides. Therefore, TRD is responsible for the majority of direct and indirect costs of MDD (Beekman and van Marwijk, 2008).
A systematic review of definitions of TRD used in clinical trials (Berlim and Turecki 2007b) identified six different definitions of TRD, ranging from non-response to one antidepressant (for ≤4 weeks ) to a failure to respond to multiple adequate (in terms of duration and dosage) trials of different classes of antidepressants and electroconvulsive therapy (ECT).
Unfortunately, none of the definitions has been properly operationalized, nor systematically investigated. For these definitions, it was most often not stated explicitly whether previous treatments were considered to determine TRD when these had been applied during the current or also during any previous episode. Furthermore, TRD-assessment was often unspecified regarding the adequacy and duration of previous antidepressant treatments, assessed retrospectively (based on patient-recall only), with occasional assessment of previous non-response by clinical global impression or validated rating scales. All definitions of TRD only focused on previous pharmacological treatment, leaving out psychological treatments like cognitive behavioural therapy (CBT) or interpersonal therapy (IPT).
In summary, Berlim et al. (2007) defined TRD as an episode of MDD which has not improved after at least two adequate trials of different classes of antidepressants, which is supported by the deteriorating chances of response after the second antidepressant observed in STAR*D (Rush et al., 2006;Ruhe et al., 2006). This definition assumes that treatment with drugs from the same class of antidepressants are less effective than successive treatments that apply a between class switch. There is very little evidence that actually supports this notion (Ruhe et al., 2006;Papakostas et al., 2008).
Berlim et al. (2007) suggested that consequent and international use of this definition would improve understanding of research findings and communication between investigators and clinicians. The European Medicines Agency (EMEA) revised their definition of TRD, stating that a “clinically relevant TRD is a current episode of depressive disorder which has not benefited from at least two adequate trials of antidepressant compounds of different mechanism of action” (Committee for medicinal product for human use (CHMP) 2009). This definition will define TRD for clinical registration studies of (new) antidepressant agents, especially to license next-step treatments. It will also exclude the inclusion of partial responders, and increase homogeneity of study-populations. Nevertheless, it should be taken into consideration that any definition of TRD is based on clinical parameters/outcomes, while it does not address underlying pathophysiology.
The above definitions of TRD imply a dichotomy, which does not acknowledge the clinical impression of a more dimensional nature of TRD (Berlim and Turecki 2007a). Therefore, a staging model for TRD appears more appropriate. Such a model should be able to classify patients according to their level of resistance to treatment for MDD, predict chances of future remission and guide clinical treatment selection. Like in oncology (Fagiolini and Kupfer 2003), in the future, psychopathological and biological markers for staging of TRD might be useful to better predict the course and prognosis of the disease. Several clinical variables might influence the development or level of TRD: duration of the episode, depression subtype, depression severity, and psychiatric and/or somatic co-morbidity (Berlim and Turecki 2007b).
We recently systematically reviewed the literature to identify staging models for TRD and compared these models regarding predictive utility (possibility to discriminate different levels of treatment response in relation to unresponsiveness to subsequent treatments) and reliability (adequacy of staging between and within raters) (Ruhe et al, 2012). Several staging methods have been developed: the the Antidepressant Treatment History Form (Sackeim, et al.,1990), the Thase and Rush Model (TRSM) (Thase and Rush, 1997), the European Staging Model (Souery et al.,1999), the Massachusetts General Hospital Staging model (Fava 2003) and the Maudsley Staging Model (MSM) (Fekadu et al., 2009a; Fekadu et al., 2009b), but to date, no staging model has been widely accepted.
With these models, an evolution from single antidepressant adequacy ratings, towards a multidimensional and more continuous scored staging model occurred over time, while also illness characteristics (severity and duration) have been introduced. The operationalization criteria for these models improved over time. The scoring of different treatment strategies (between/within class switching, augmentation/combination) changed according to the existing evidence. Over time, efforts to validate models improved as well.
The most comprehensive clinical staging/profiling model for TRD is the MSM, which was validated as measure for treatment resistance as well (Fekadu et al., 2009a; Fekadu et al., 2009b). The MSM summarizes the actual stage of TRD in a single score, varying between 3 and 15. Staging of TRD can also be presented in 3 ordinal categories: mild (scores = 3-6), moderate (scores = 7-10) and severe (scores = 11-15). The predictive utility of the MSM was tested by using prospective data (average treatment duration 26±16 weeks) from case notes (N=88) from all patients discharged from a specialized TRD-inpatient unit (Fekadu et al., 2009a). With logistic regression the MSM and its components (number of medications, duration of presenting episode, and severity of illness) were associated with failure to achieve remission (Hamilton Depression Rating Scale (21-items) ≥11) at discharge. Furthermore, variations of the MSM were examined by the introduction of additional items.
Duration, severity and treatment were independently associated with non-remission at discharge (OR: 2.27 (1.4-3.8), 2.14 (1.1-4.3) and 1.43 (1.1-2.0) respectively), as was the total MSM-score (OR 1.67 (1.3-2.2)). The MSM correctly predicted treatment resistance in 85.5% of the cases. A second study tested whether this model predicted clinical outcome after a longer follow-up (Fekadu et al., 2009b). For this purpose, 62 patients (Fekadu et al., 2009b) were followed-up (median follow-up 29.5 months (IQR 19.0-52.5 months)). Of the patients, 21% remained depressed continuously, while 37.7% remained depressed for ≥50% of the follow-up. Higher MSM scores were found to predict the persistence of a depressive episode throughout follow-up (OR = 2.01 (1.1-3.5), p= 0.015), and the presence of a depressive episode for ≥50% of time (OR 2.11 (1,3-3.6), p =.005). In contrast with the MSM, the TRSM also predicted future non-response, albeit worse than the MSM, but the TRSM failed to predict long-term clinical outcome.
Regardless of the initial choice of antidepressant, about 30% to 50% of patients with MDD do not achieve full remission to adequately performed first-line treatment (Fava and Davidson, 1996). Several treatment strategies have been proposed for patients not responding sufficiently to monotherapy with an antidepressant. The strategies which are most commonly used are: 1) switching to a new antidepressant, either from within the same pharmacologic class or from a different class, 2) augmenting the antidepressant with other agents to enhance antidepressant efficacy, 3) combining 2 antidepressants from different classes, and 4) combining the antidepressant with depression-specific psychotherapy (Fava and Davidson, 1996). Potential benefits of switching are: this strategy is heuristically clear, because of less side effects compliance may be better than with augmentation/combination. Possible disadvantages of switching are: loss of partial response, and withdrawal symptoms (Papakostas, 2009). Potential benefits of augmentation/combination: therapeutic effect of the first drug is preserved, and augmentation may lead to a faster response. Possible disadvantages of combination/augmentation: more adverse effects, lower compliance, and the risk of possible drug interactions (Papkostas, 2009)
Currently, there is no consensus about which strategy should be favored for nonresponding patients, since until now no randomized clinical trials have been conducted to answer this question (Spijker and Nolen, 2010). Some authors argued in favor of augmentation strategies, instead of switching, because there is no need for a washout period between antidepressants and possible partial response to the antidepressant is maintained. Indeed, patients who have had some response may be reluctant to risk a loss of that improvement, and in this situation, augmentation may be beneficial. When effective, benefits of augmentation can be observed rapidly. In this chapter we will discuss three different augmentation strategies: Lithium augmentation, T3 augmentation, and The augmentation of atypical antipsychotics.
If a patient fails to respond to treatment with an antidepressant (usually an SSRI), an obvious strategy would be to switch to another antidepressant. A review by Ruhé et al. (2006) found 23 open studies and 8 randomized studies, often conducted in heterogeneous patient samples and with considerable variation in methodological standards. The response rates of the switch studies varied between 12 and 86%. No clear-cut advantage of switching between classes of antidepressants compared with switching within the same class emerged. Switching to venlafaxine showed a modest and clinically equivocal benefit over switching between SSRIs with a number needed to treat = 13. This difference increased when the largest and methodologically poorest study was omitted (NNT=10). After a first SSRI, the majority of open studies reveal that switching to any of the current classes of antidepressants leads to a response rate of about 50%. However, in the randomized but unblinded STAR*D study the response rate after switching was lower; 26.8%, which may have been due to the inclusion of a higher proportion of patients with a chronic course of depression, a lower socioeconomic status and more somatic and psychiatric comorbidity (Rush et al., 2006). The level of treatment resistance was inversely correlated with outcome in the switch studies (Ruhé et al., 2006; Rush et al., 2006).
Combination treatment involves prescription of two different antidepressants at the same time. By combining two different antidepressants treatment may be more effective since different neurotransmitter systems can be influenced. Several studies have shown that combination treatment may be superior to antidepressant monotherapy. Blier et al. (2010) showed that three combination therapies (fluoxetine+mirtazapine, venlafaxine+mirtazapine, bupropion+mirtazapine) were all superior to fluoxetine monotherapy. In an earlier study, Blier et al. (2009) found that the combination of mirtazapine and paroxetine was more effective than mirtazapine or paroxetine as monotherapy. In the STAR*D study a combination of citalopram and bupropion showed a significant larger decrease of the Inventory of Depressive Symptoms (IDS) than a citalopram-buspiron combination, but the difference in the number of patients attaining remission was not significantly different (Gilmer et al., 2008). The co-med study (Rush et al., 2011) compared escitalopram-placebo with both an escitalopram-bupropion combination, and a venlafaxine-mirtazapine combination in a single-bind randomized study. In this study similar response and remission rates were found both after 12 weeks and 7 months of treatment for all three treatment conditions. When trying to explain why the Blier et al (2010) study found combination therapy superior to monotherapy, while the co-med study did not, Rush et al. noted that in their study only a small proportion of patients had melancholic features (20%), and the majority suffered from chronic depression. In the study by Blier et al. (2010) the majority of patients had melancholic features and lthe proportion of patients with a chronic course of depression was less. In conclusion, combining two different antidepressants may be useful, but this strategy has not been studied in specific subgroups of depressed patients, and it has not been compared with other strategies, i.e. lithium addition or non-selective MAOIs.
Lithium has been used to augment the efficacy of antidepressant medications for about 30 years. The first study to test the efficacy of this augmentation strategy in patients with major depression was performed by de Montigny et al. (1981). The authors observed a rapid response, within 48 hours, when lithium was added to the ongoing antidepressant treatment of patients who had not responded to at least 3 weeks of treatment with tricyclic antidepressants (TCAs). The efficacy of the augmentation and its rapid response has led to relatively many studies concerning lithium augmentation. It has been well established in controlled trials that approximately one-half of all patients with treatment-refractory depression respond when lithium is added to their ongoing antidepressant treatment. The level of evidence for the efficacy of lithium augmentation is higher than that for other augmentation strategies (Fava and Davidson, 1996). Therefore, lithium augmentation should be considered a first-line treatment strategy in patients with major depression that does not sufficiently respond to standard antidepressant treatment. There are clues that lithium augmentation to TCAs has a higher efficacy than lithium added to modern antidepressants (Bruijn et al., 1998; Birkenhäger et al., 2004). However, lithium may also augment the therapeutic effects of SSRIs and venlafaxine. Whether lithium augmentation is effective for specific subtypes of major depression is unclear. The presence of melancholic features might be related to a higher efficacy of lithium augmentation: in the STAR*D study the efficacy of lithium addition appeared to be very low in a patient population, of which 12% fulfilled criteria for major depression with melancholic features (Nierenberg et al, 2006). In another study in depressed inpatients showing a high efficacy of lithium augmentation, 88% of the patients suffered from major depression with melancholic features (Bruijn et al., 1998). Whether or not patients with bipolar depression show a superior response to lithium augmentation is unknown.
The efficacy of lithium augmentation in depressed patients with psychotic features has been studied scarcely. In a small (n=15) open study, 60% of the patients achieved full remission during four weeks of lithium augmentation (Birkenhäger et al., 2009). Although the effect of lithium augmentation may appear during the first week of treatment, for other patients the effect becomes apparent within 2-6 weeks. The target lithium level should be at least 0.5 mmol/l, while levels of 0.6-0.8 mmol/l are recommended.In patients who respond to lithium augmentation, both lithium and the antidepressant should be continued for at least 12 months, with therapeutic plasma levels.
The thyroid gland produces two hormones, triiodothyronine (T3) and levothyroxine (T4). T4 is the main hormone secreted by the thyroid, a large proportion of T4 is converted to T3 in peripheral tissues in order to perform its physiological function. T3 has been used in combination with antidepressants (mostly TCAs) in three different ways: A. during the first week of treatment with an antidepressant with the purpose of acceleration of the antidepressant effect; B. in combination with an antidepressant throughout the antidepressant trial, with the purpose of enhancement of the antidepressant effect; C. as additional treatment after apparent nonresponse to antidepressant monotherapy: T3 augmentation. In this chapter we will focus on C.T3 augmentation for refractory depression.
Triiodothyronine (T3) was first used in the treatment of depression in 1958. Early studies used T3 with TCAs to accelerate the response to TCAs. A meta analysis of six double-blind, placebo-controlled studies (125 patients total) of T3 acceleration of tricyclics by Altshuler et al. (2001) was positive, as shown by d=0.58. Furthermore, a significant gender effect was observed, with women responding more robustly than men. By definition, these were short-term studies of 2 to 3 weeks, and no study investigated the option of continuing T3 once antidepressant response was achieved. Several placebo-controlled studies confirmed a more rapid effect in patients treated with both TCAs and T3 compared with TCA monotherapy.
Several open studies suggested that the augmentation of T3 to an ongoing treatment with TCAs leads to a response in a substantial proportion of patients with refractory depression. Aronson et al. (1996) performed a meta-analysis of 8 clinical trials of T3 augmentation comprising a total of 292 patients. The duration of T3 addition varied from 10 days to 6 weeks and the daily dose T3 was between 20 and 50 microgram. Patients receiving T3 were twice as likely to respond as controls, the NNT was 3. Aronson et al. (1996) concluded that T3 augmentation is an effective and safe method of increasing response in patients refractory to TCAs. However, most of the studies included in this meta-analysis had methodological flaws. When the authors restricted their analysis to 4 randomized double-blind studies, the effect of T3 augmentation was not significant any more.
Recently, a number of studies have examined the addition of triiodothyronine to selective serotonin reuptake inhibitors (SSRIs) in non-responders, but the data are more limited than with TCAs. A review by Cooper-Kazaz and Lerer (2008) found that there were insufficient data for a meta-analysis but that a positive trend was revealed when the available double- and single-blind studies were analyzed, response to T3 augmentation amounted to 40%. Papakostas et al. (2009) performed a meta-analysis which only included three double-blind, randomized, placebo-controlled studies. This analysis found response rates of 64.6% for SSRIs + T3 versus 58.5% for SSRI monotherapy, this difference was not significant.
The Sequenced treatment Alternatives to Relieve Depression (STAR*D) study compared SSRI augmentation with either lithium or T3 during 12 weeks in 142 depressed outpatients, who were refractory to treatment with citalopram and a second step (either augmentation with bupropion, buspirone or cognitive therapy or switching to a second antidepressant). This study revealed no statistical difference in efficacy between the treatments (Nierenberg et al., 2006). T3 was tolerated better and adherence was higher. However, remission rates were surprisingly low: 16% for lithium addition and 25% for T3 augmentation, respectively.
In conclusion, T3 augmentation, given at a daily dose of 25-50 microgram, is effective for patients who failed to respond to treatment with a TCA. Compared with lithium addition, the efficacy of T3 addition is established less firmly. It is unknown how long continuation treatment with T3 is necessary, following response to T3 augmentation.
First generation antipsychotics have been used to treat MDD, but extrapyramidal side effects and the risk of tardive dyskinesia limited the use of these agents. Since 1999 several case reports and open studies appeared, concerning the use of atypical antipsychotics as adjunctive treatment in patients with insufficient response to treatment with an SSRI, also/especially in non-psychotic patients. Following these case series, a number of double-blind, placebo-controlled augmentation trials have been conducted. A recent meta-analysis by Nelson and Papakostas (2009) comprised sixteen double-blind studies. The following atypical antipsychotics were used in these 16 studies: quetiapine, olanzapine(both 5 studies), risperidone and aripiperazole (both 3 studies). The duration of the addition with the antipsychotics varied from 4-12 weeks, the majority of the studies investigated effects over 6-8 weeks. The meta-analysis by Nelson and Papakostas (2009) analyzes the efficacy of each of the antipsychotics separately. Olanzapine, quetiapine, riperidone and aripiperazole augmentation appeared to be superior to placebo addition. The effect of olanzapine addition was relatively small, with an Odds Ratio (OR) of 1.39. For the other antipsychotics the ORs varied between 1.63-2.00. With regard to the sixteen double-blind studies, included in this meta-analysis, it is remarkable that only a minority shows a statistically significant effect compared to placebo (six of sixteen studies). In four studies this failure to find a difference may have been caused by the fact that these studies were small. However, these figures suggest that the effect of augmentation with atypical antipsychotics is relatively small. Furthermore, Nelson and Papakostas (2009) find signs indicating publication bias. An unanswered question regarding the effect of augmentation with atypical antipsychotics: is it merely an effect on anxiety and sleep disturbance, or does this augmentation also has an effect on ‘core symptoms’ of MDD (depressed mood, psychomotor retardation, diurnal variation, weight loss)? Another unanswered question is whether augmentation with atypical antipsychotics is more effective than switching antidepressants.
It is unknown how long continuation treatment with both the antidepressant and the atypical antipsychotic is necessary, following response to augmentation with an antipsychotic.
Although MDD is considered to have a favourable prognosis, remission rates in controlled studies are considerably less than 50%. Insufficient response to antidepressant treatment is often caused by inadequately performed pharmacotherapy, i.e., suboptimal dosage or suboptimal duration of treatment. Since residual symptomatology carries a high risk of relapse during continuation treatment and, subsequently, a chronic course of depression, full remission should be the aim of treatment (Thase and Rush, 1997). Therefore, both inadequate treatment and actual treatment resistance constitute major problems in the management of patients with major depression. The use of a systematic treatment algorithm may decrease the variance and increase the appropriateness of antidepressant treatment and, therefore, improve outcome. Only a few studies compared the efficacy of a treatment algorithm with treatment as usual (TAU). The only prospective randomized trial (Bauer et al., 2009) found a higher remission rate in the algorithm-treated sample (54% versus 39% in the TAU sample).
The algorithm proposed in the most recent version of the Dutch multidisciplinary guideline for depression consists of five subsequent steps. Since antidepressants are effective in moderate to severe major depression, and in both primary and secondary care (psychiatric outpatients) there is no clear difference in efficacy between antidepressants, SSRIs, SNRIs, mirtazapine, bupropion and TCAs are good options as first antidepressant treatment. SSRIs are the most frequently used antidepressants in the first treatment step. If there is insufficient response after 6-10 weeks of treatment, the second step is to switch to another antidepressant. There is a slight preference for switching from an SSRI to a TCA or venlafaxine, although switching from one SSRI to another is also possible. Lithium augmentation has the strongest evidence in treatment resistant depression, but because of its potential poorer tolerability, lithium augmentation is chosen as third step. Most of the evidence for lithium augmentation concerns augmentation of a TCA. The fourth step consists of switching to a non-selective MAOI (preferentially tranylcypromine). Although the evidence for ECT is strong, ECT is sometimes not acceptable to patients, and its availability is limited. Therefore, ECT is the fifth step in this algorithm (Spijker and Nolen, 2010).
Is it appropriate to use one algorithm for a very heterogeneous illness like major depression? Some of the treatment steps prove to be effective in one subtype of MDD whereas they appear less effective in another. Therefore we propose three different algorithms, after distinguishing three subtypes of major depression, based on the DSM-IV criteria for melancholic and psychotic features. The specific treatment steps in the algorithms are selected, when proven effective for the subtype of major depression.
Considerations: SSRIs, SNRIS, mirtazapine, bupropion, TCAs, interpersonal psychotherapy (IPT) and cognitive behavioural therapy (CBT) all appear to be effective. The efficacy of lithium augmentation is doubtful: lithium augmentation appeared to be ineffective in the third step of the STAR*D study (Nierenberg et al., 2006). This lack of efficacy could be explained by the fact that lithium levels were determined in only 50% of the patients and 50% of the lithium levels were low. An alternative explanation for the poor result is the very low prevalence of melancholic features in this patient sample (12%). Lithium augmentation appeared to be very effective in a study concerning depressed inpatients; of whom 88% had melancholic features. Non-selective MAOIs can be effective regardless of the presence of melancholic features. ECT has a higher efficacy in patients with melancholic depression, compared with patients without melancholic features. These considerations result in the following algorithm:
Step 1.SSRI or another modern antidepressant
Step 2.a second SSRI or another modern antidepressant
Step 3.a TCA
Step 4.a non-selective MAOI (preferentially tranylcypromine)
The addition of IPT or CBT can be considered with every step.
Considerations: Treatment with SSRIs or other modern antidepressants (with the exception of venlafaxine) appears to be less effective than treatment with a TCA. SSRIs are less effective than TCAs or venlafaxine in depressed inpatients. This difference in efficacy may be explained by a higher compliance in inpatients, but it can also be due to a higher presence of melancholic features among inpatients compared with outpatients. Lithium augmentation to TCAs is (very) effective in patients with melancholic features. Lithium augmentation to venlafaxine has never been the subject of a double-blind study, but may possibly be effective, based on open studies. Non-selective MAO inhibitors may be effective, whereas the efficacy of ECT is high. Both CBT and IPT appear to be less effective in melancholic depression as opposed to non-melancholic depression. These considerations result in the following algorithm:
Step 1.a TCA or venlafaxine
Step 2.Lithium addition to a TCA
Step 3.a non-selective MAOI (preferentially tranylcypromine)
Step 4.ECT
Depending on the patient’s condition, step 3 and 4 can be switched.
Considerations: Monotherapy with a TCA is not effective according to studies from the US, while European studies found TCAs as monotherapy to be effective for psychotic depression. Whether a combination of a TCA and an antipsychotic is superior to TCA monotherapy is unclear. A Combination of venlafaxine and quetiapine proved to be superior to venlafaxine monotherapy. Treatment with lithium addition has been studied scarcely in psychotic depression, but possibly it may be effective. The efficacy of non-selective MAOIs in psychotic depression is unknown. Treatment with ECT is very effective. These consideration results in the following algorithm:
Step 1.TCA with/without an antipsychotic OR venlafaxine + quetiapine
Step 2.If Step 1 was TCA, add an antipsychotic. If Step 1 was venlafaxine switch to a TCA
Step 3.Lithium addition to a TCA
Step 4.ECT
ECT may be performed prior to step 4, especially for patients in a critical condition.
Treatment-resistant depression is a major health issue, since major depression is a prevalent disorder and remission is not easily attained. Futhermore, treatment-resistance appears to be difficult to define. In this chapter, we discuss several staging methods for treatment-resistant depression. With regard to treatment options for patients who fail to respond to the first antidepressant, these consist of switching antidepressants, combining antidepressants, and augmentation strategies. Optimisation of antidepressant treatment can be achieved by applying those treatment strategies as an algorithm. In the Dutch multidisciplinary guideline for depression one standard algorithm is proposed, without considering the (limited) evidence that various subtypes of major depression respond differently to specific treatment steps. Therefore, we propose three algorithms, which are based on the limited evidence regarding the efficacy of several treatment steps for a specific subtype of major depression.
In the first part of the chapter various definitions of treatment resistant (refractory) depression (TRD) are reviewed. We conclude that there is no consensus regarding the operational criteria for TRD. For TRD, five different staging models have been developed to determine a staging level of refractoriness: the Antidepressant Treatment History Form, the Thase and Rush Staging model, the Massachusetts General Hospital Staging Model, the European Staging Model and the Maudsley Staging Model. The utility of these models will be discussed.
The second part of the chapter focuses on treatment options for TRD. Apart from switching the antidepressant, various augmentation strategies are currently applied for refractory depression, e.g. lithium addition, triiodothyronine addition and the addition of second generation antipsychotics. The advantages of these strategies will be discussed.
Finally, we will discuss the use of treatment algorithms. The algorithm for the pharmacological treatment of major depression of the Dutch multidisciplinary guidelines for depression is presented. The five subsequent steps of this algorithm (treatment with an antidepressant, switching to another antidepressant, lithium addition, switching to an MAO inhibitor, electroconvulsive therapy) will be discussed and a proposal for three different algorithms will be presented, depending on the presence or absence of melancholic and psychotic features will be discussed, as well as alternative strategies for the pharmacological treatment of TRD, which are not (yet) included in the algorithms.
The preservation and conservation of the environment are of great significance for healthy living. However, efforts to conserve the environment have been futile due to escalated pollution from biogenic and anthropogenic sources, which constantly release pollutants to the environment [1]. In the recent past, increased industrial and agricultural activities have immensely contributed to the pollution of aquatic environments such as rivers and streams, which pose major detrimental environmental problems to humans [2]. It is evident that industrial development has generated a myriad of new chemicals produced and applied in daily activity, which is becoming a major concern for citizens, the research community, and authorities [3]. Among the pollutant chemicals that have been introduced into the environment are polybrominated diphenyl ethers (PBDEs). PBDEs are toxic, lipophilic, hydrophobic, and persistent artificial chemicals characterized by high physical and chemical stability [4]. They are commonly applied as flame retardants in polymer products such as electronics, plastics, textiles, and building materials [5, 6]. PBDEs have become a growing concern over the last two decades due to their ubiquity, persistence and accumulation capacity in the environment, as well as their potential risks to human health and wildlife [7, 8]. PBDEs are normally additive compounds, meaning they are not covalently bound to the polymeric products [9]. Therefore, they may leach out into the surrounding environment during their production, usage, disposal, or recycling process [10]. PBDEs can be transported away from their sources for long-ranges through aqueous and/or terrestrial environmental compartments [11, 12]. In this context, monitoring and assessment of environmental pollution by these compounds are very important.
Their determination involves a series of steps from sample pre-treatment to quantification of analytes using various detection systems. Different sample preparation strategies that range from conventional to advanced strategies have been applied for the determination of PBDEs in environmental samples. Some of the conventional sample enrichment methods include Soxhlet extraction [13, 14] and liquid-liquid extraction (LLE) [15]. More recently, ultrasound-assisted extraction (UAE) [16, 17], pressurized liquid extraction (PLE) [18, 19], microwave-assisted extraction (MAE), solid-phase extraction (SPE), and solid-phase microextraction (SPME) have exhibited successful extraction of PBDEs from environmental samples [20, 21]. The application of SPE and SPME has advanced from conventional adsorbent formats to the most improved formats which allow easy transfer of analytes from their complex matrices. This has been achieved by using novel adsorbent materials to replace conventional silica-based adsorbents which exhibit low selectivity towards targeted analytes [22]. Similarly, analytical techniques for the qualitative and quantitative determination of PBDEs have advanced from well-known gas chromatography-electron capture detection (GC-ECD) to sensor-based techniques that are more advantageous in terms of excellent selectivity, with opportunities for in-situ application. The following sections provide detailed information on PBDEs, advances in sample pre-treatment methods and detection techniques with a view of providing the current state-of-the-art as far as their monitoring is concerned.
PBDEs comprise of two halogenated aromatic rings bonded by an ester bond and are classified in relation to the number and position of bromine atoms in a particular molecule [23]. They have a general molecular formula of C12H(10 - x) BrxO, where x is the number of bromine atoms in a molecule with numerical values [x = 1, 2, 3, …, 10 = m + n] (Figure 1). Substitution of bromine atoms can take place at 10 possible positions on the two benzene rings resulting in 209 possible congeners [24].
General structural formula of PBDEs.
Different congeners are easily identified by their corresponding IUPAC numbers ranging from 1 to 209. In this case, 2,2′,4,4′-tetrabromodiphenyl ether is BDE-47, with bromine atoms in ortho and para positions on the first and second benzene rings, respectively (Figure 2).
Chemical structure of 2,2′,4,4′-tetrabromodiphenyl ether (BDE 47).
Molecules with one to four bromine atoms are classified as low molecular mass PBDEs, whereas the ones with five to ten bromine atoms are categorized as high molecular mass PBDEs. Less brominated PBDEs are more persistent and toxic than highly brominated diphenyl ethers [25]. The substitution pattern also affects the physicochemical properties of PBDEs, whereby the solubility of PBDEs decreases significantly with an increase in bromine substitution. The aqueous solubility (SW) of low molecular mass PBDEs at room temperature ranges from 6.57 × 10−7 to 7.82 × 10−11 mol L−1 while those of high molecular mass have aqueous solubility values lower than 7.82 × 10−11 mol L−1 [26]. A wide range of PBDE congeners exhibit high lipophilic capacity and high resistance to degradation; a property that makes them bioaccumulate and magnify in biota [7]. PBDEs are also associated with high octanol-air partition coefficients (KOA) with values ranging between 9.3 and 12.0 from BDE-17 to -126, which is approximately 1 to 2 orders of magnitude greater than PCBs [27]. Therefore, PBDEs are easily transported through air from one point to another, increasing their chances of exposure to humans. Dissolved organic matter has shown a high tendency to interact with hydrophobic compounds such as PBDEs, which hinders their mobility and degradation in the environment [28]. Reported binding coefficients of PBDEs (log KDOC) towards organic matter range from 5.1 to 7.14, which implicates the high capability of PBDEs to adsorb and partition on organic matter [29].
PBDEs were commercially produced in three technical mixtures, typically known as pentaBDE, octaBDE, and decaBDE, basing on the number of bromine atoms [10]. By early 2000, the global production of commercial PBDE formulations was approximately 67,000 tons in the ratio 1:1.98:14.8 for octa-BDE, penta-BDE, and deca-BDE respectively, of which the United States production was approximately 50% of the global production [30]. Several governmental regulations and international environmental agencies have restricted and completely banned the use and production of some PBDE congeners [31]. In 2004, the European Union phased out the use and production of penta-BDE and octa-BDE. Consequently, in December 2004, Great Lakes Chemical Corporation, a sole manufacturer of penta-BDE and octa-BDE in North America, voluntarily phased out the production of these BDE formulations [32]. These efforts were boosted by the Stockholm Convention in 2012 when it listed commercial octa-BDE and penta-BDE among persistent organic pollutants that need to be eliminated. Despite the ban in the production of most PBDEs, they are still reported in air, soil and aquatic environments, which is attributed to their stability and subsequent release from techno-ecosystems, and production of deca-BDE, which still continues to be produced in some countries [33, 34].
There are diverse pathways by which PBDEs enter the environment. Major environmental sources of PBDE pollution comprise of leakage from consumer products and industrial facilities that synthesize PBDEs or PBDE-containing products [5]. Besides, PBDEs may enter the aquatic environment from illegal disposal of obsolete electrical appliances and electronic devices flame-retarded with PBDEs or other PBDEs-containing products [7]. They can also enter the aquatic environment through raw sewage and into the surrounding air through volatilization from products containing PBDEs and toxic fumes from e-waste recycling plants [35]. Since the first discovery of PBDEs in the aquatic environment on the West coast of Sweden in 1981, several studies have reported the presence of PBDEs in the environment [36]. This is despite the strict regulatory measures imposed by some governments and international environmental agencies to phase out some PBDE congeners and subsequent reduction in the production of particular PBDEs. BDE-47, 99, 100, and 153 are the ones that are frequently investigated because they are primary components of commercial mixtures, therefore, their ratios in the environment are expected to be significantly high. Moreover, less substituted BDE congeners such as BDE-28 and 47 are more toxic and non-biodegradable, hence their investigation in the environment and biota is of great significance in the monitoring of these pollutants [37]. Soil and sediment harbour higher concentrations of PBDEs, which is attributed to the organic carbon content, which makes them a sink for most organic pollutants [38]. Elevated levels of PBDEs have since been reported in agricultural soils after the application of sewage sludge at a concentration of 21 to 690 ng g−1 dry weight (dw) [39]. From statistics, human beings spend more than 70% of their lifetime indoors, in occupational offices, homes, learning institutions, and transport vehicles, and are therefore exposed to an array of contaminants from indoor dust [40]. The highest levels of PBDEs in dust samples have been reported in major industrialized cities in China and Europe at a concentration of 397–40,236 ng g−1 and 950–54,000 ng g−1, respectively [41, 42], with comparably lower levels of 1710 ng g−1 in African regions [43]. Table 1 presents a summary of reported PBDE levels in selected environmental matrices.
Country | Sample matrix | Concentration | Reference |
---|---|---|---|
South Africa | River water | 2.60–4.83 ng L−1 | [44] |
North America | River water | 0.00013–0.01 ng L−1 | [45] |
Great Britain | Indoor dust | 950–54,000 ng g−1 | [46] |
South Africa | Home dust | 1710 ng g−1 | [43] |
Office dust | 1520 ng g−1 | ||
Nigeria | Indoor dust | 3700–19,000 ng g−1 | [47] |
China | Indoor dust | 397–40,236 ng g−1 | [41] |
Uganda | Air | 0.00340–0.00984 ng m−3 | [48] |
Kenya | Soil | 0.19–35.64 ng g−1 | [49] |
China | Soil | 4.8–533 ng g−1 | [50] |
China | Sediment | 0.03- 5.22 ng g−1 | [51] |
China | Sediment | 0.13–1.98 ng g−1 | [52] |
Sweden | Sewage sludge | nd-450 ng g−1 | [53] |
Spain | Sewage sludge | 197–1185 ng g−1 | [39] |
Kuwait | Sewage sludge | 52.5–377* ng g−1 | [54] |
USA | Serum | 5.0–27.9 | [55] |
South Africa | Tigerfish | 5.8 | [56] |
Uganda | Breast milk | 0.59–8.11 | [57] |
Levels of PBDEs reported in the environment and biota from different locales worldwide.
Mean concentration.
nd, not detected.
The principal route for PBDE exposure to humans was thought to be through food consumption [58]. However, inhalation of contaminated indoor and outdoor dust is also a significant pathway via which human beings may be exposed to PBDEs [46, 59]. Dermal absorption is another potential route for PBDE exposure [60]. Numerous studies have reported levels up to 160.3 ng g−1 of PBDEs in human samples, such as serum and milk. Increased application of PBDEs in electronics has significantly aroused more research work on the concentration of these pollutants in the blood of workers in e-waste processing plants and other exposed populations [61]. BDE 47, 153, and 209 are the most predominant congeners reported in human serum and milk [55, 62]. The toxicity of PBDEs is backed up by numerous epidemiological studies. Scientific research has linked PBDE exposure to an array of adverse health effects [63]. To mention a few, penta- and octa-BDEs at a concentration of 10,000 ng g−1 have been associated with disruption of thyroid hormone homeostasis [7]. Moreover, penta- and tetra-BDEs, within the range of 8000–18,000 ng g−1, have been reported to affect the neurodevelopment of mice [64]. Exposure to high levels of deca-BDEs is likely to cause breast cancer [7]. PBDEs have been linked to developmental neurotoxicity and hence leading to severe effects on cognitive ability, behaviour, and health of both animals and humans [65, 66]. Several studies have also linked PBDEs with adverse effects on the human reproductive system. In particular, BDE-47, BDE-153, and BDE-154 in the range of 0.2–1.6 ng g−1 have been confirmed to have negative impacts on testosterone, luteinizing hormone, and estradiol [67]. Therefore, there is a need to have robust, accurate and reproducible methods to quantify PBDEs in different environmental matrices. The sections that follow will discuss these aspects with a particular focus on aquatic media.
Sample pre-treatment steps such as pre-concentration and clean-up are paramount before instrumental analysis [2, 68]. These steps ensure that analytes are enriched and converted into the right form/state to achieve their detection and any matrix that may interfere with the determination of the analytes is removed [69]. The choice of sample pre-treatment step is dependent on the physicochemical properties of the targeted analytes, their concentration in the environment, and the complexity of matrix interference [70, 71]. Soxhlet extraction, a traditional liquid-solid extraction method, has been used for decades in the extraction of analytes from their complex solid matrices. With the combination of polar and non-polar solvents, the Soxhlet extraction strategy has been proved to be efficacious, achieving extraction efficiencies greater than 70% [72, 73]. However, this method is hindered by several factors such as long extraction duration, excessive solvent consumption, and the need for subsequent clean-up steps [74]. With increasing demand for economical and fast sample extraction strategies with high enrichment factors, coupled with SPE clean-up procedures, techniques such as UAE, PLE, MAE, and supercritical fluid extraction (SFE) have been adopted in enrichment of analytes from solid matrices.
UAE encompasses the introduction of a finely divided sample contained in a sample holder in an ultrasonic bath with solvent and subjected to ultrasonic radiation. UAE is a vital technique in achieving sustainable green chemistry and is primarily employed in the extraction of analytes from solid sample matrices [75, 76]. This technique can achieve complete extraction with high reproducibility within a short duration. Moreover, small quantities of extraction solvents are used as compared to conventional Soxhlet extraction [77]. Methanol, acetonitrile, ethanol, and acetone are typical extractants used in this method in minimal volume. UAE based on ultrasound assisted-dispersive solid phase extraction (UAE-DSPE) coupled to GC-MS has been reported to achieve exemplary limits of detection and extraction efficiencies for 7 BDE congeners from dust samples collected from air conditioning filters in the range of 1.4–8.4 ng g−1 and 90–102%, respectively [78]. Some of the benefits of UAE include faster kinetics and an increase in extraction yield. Ultrasound can also reduce the operating temperature allowing the extraction of thermally labile compounds [79].
Unlike traditional Soxhlet extraction that consumes a large volume of solvent, PLE, also referred to as pressurized solvent extraction, has been of great interest due to its extraction effectiveness. Extraction of analytes from their environmental matrices is achieved via a synergistic mechanism that proceeds through liquid solvents at elevated temperature and pressure, which altogether enhance extraction throughput as compared with other techniques performed at ordinary atmospheric conditions [80]. PLE is viewed as another \'green\' option for traditional sample extraction methods. High temperature accomplishes a higher dispersion rate, while high pressure keeps the extraction solvent below its boiling point. During the determination of brominated flame retardants in e-waste samples, PLE and UAE were evaluated in regard to extraction efficiencies. PLE demonstrated high extraction efficiencies of 95–100% as compared to 10–50% for UAE [81]. When contrasted with the conventional methods, PLE shows a decrease in extraction time and a significant decrease in the overall consumption of organic solvents [82].
Another type of extraction technique that enables a three-fold reduction in extraction time and solvent is MAE. This is a sample extraction method that employs microwave energy to extract analytes from solid sample matrices in contact with extraction solvents. Microwave energy directly generates heat which initiates molecular motion of the analytes in the solid-solvent complex mixture, hence facilitating the mass transfer of the target analyte from the solid matrix to the extracting solvent [83, 84]. MAE has been reported to achieve good recoveries of 80–106%, 72.4–108.4%, and 80–110% in the extraction/pre-concentration of PBDEs from airborne particulate matter [85], e-waste materials [86], and sewage sludge samples [87], respectively. Compared with Soxhlet extraction, MAE achieves better recoveries and uses small amounts of solvents (30 mL versus 200 mL for Soxhlet extraction), at the same time allowing control of extraction parameters, such as extraction time and temperature [88]. However, MAE has some shortcomings, whereby the extracted sample usually contains some matrix interferences, such as lipids and lipophilic compounds, therefore, filtration and clean-up steps are required, which subsequently consume extra organic solvents.
Supercritical fluid extraction (SFE) is another method employed to extract PBDEs from solid matrices. Supercritical CO2 is often used as an extracting solvent, which has the capability of attaining recoveries above 97%. Moreover, the extraction efficiency of SFE can be further improved by the use of modifiers such as acetonitrile, toluene, and tetrahydrofuran [89]. A successful application of SFE in the extraction of PBDEs from polymeric materials was reported by Peng et al. [90]. The authors used supercritical CO2 as a solvent and SFE operating parameters such as temperature and pressure were optimized at 65°C and 20 MPa, respectively, achieving 97.6% extraction efficiency. This technique is a greener alternative to other techniques that use a large volume of solvents.
Numerous methodologies have been adopted in the determination of PBDE pollutants in liquid matrices. SPE and conventional LLE have been embraced as routine extraction techniques for PBDEs in liquid samples. The extractive capability of LLE is based on the transfer of analytes from an aqueous polar phase to a non-polar organic phase [91]. LLE coupled with GC-MS has been applied in the determination of 13 PBDEs and their metabolites in water, with recoveries of 77%-102% [92]. LLE has also been a desirable extraction method in the preparation of biota samples for the determination of PBDEs. Recently, a study aimed at assessing in utero exposure of 24 tri- to deca- BDE congeners on primiparous mothers in Kampala, Uganda reported a successful application of LLE, with appreciable recoveries of 81–91% [93]. However, LLE has some shortcomings; it suffers from low recovery, poor selectivity, high matrix interference in chromatographic analysis and increased sample loads [94]. In addition, the extraction of PBDEs from water samples requires extremely large volumes of solvents due to their hydrophobic character and low concentration in water, thus limiting its applications [95]. To overcome these challenges, different configurations of SPE have been adopted in sample enrichment strategies. SPE is a modern sample pre-treatment technique employed to concentrate analytes from liquid samples and to remove matrix interferents during the clean-up step, achieving exemplary recoveries and reproducible results over LLE [96, 97]. SPE protocols are usually performed by the use of a small column or separation cartridge packed with an appropriate sorbent material [98, 99]. Target analytes are adsorbed by the sorbent materials and later eluted with a solvent that has a greater affinity for the analytes. The chemistry behind this separation is based on intermolecular forces between the analytes, active sites of the adsorbent, and the liquid phase of the matrix [100]. SPE can be performed through an on-line or off-line approach. The on-line SPE configuration, which may enable automation, is directly coupled with specific analytical systems such as gas chromatography (GC) or high-performance liquid chromatography (HPLC). Whereas in the off-line protocol, a pre-concentration step is done separately using cartridges and further eluting the adsorbed analyte with an appropriate solvent for eventual chromatographic analysis [101]. Because of its robustness and flexibility, SPE has been widely employed in different analytical procedures in pre-concentration and clean-up steps in the determination of PBDEs [96, 102].
While SPE continues to be used because of its affordability and ease of use, other formats that offer high enrichment factors and shorter extraction times, such as SPME, stir-bar sorptive extraction (SBSE) and dispersive solid-phase extraction (DSPE), have been introduced [103]. SPME is an innovation and improvement of conventional SPE. Its stationary phase comprises of fused-silica fibers coated with a polydimethylsiloxane (PDMS) layer which are reusable. With this new formulation, the application of SPE has become versatile such that it can accommodate small volumes of samples. Furthermore, SPME has been considered an almost solvent-free extraction technique and can be easily automated as compared to conventional SPE [104, 105]. A miniaturized SPME has been applied in the extraction of PBDEs in environmental water samples followed by GC-MS quantitation, with low limits of detection and appreciable recoveries of 76.5–125.4% [106]. SBSE is a similar technique to SPME that has been adopted in the enrichment of PBDEs in liquid samples due to its improved extraction efficiency. The stir bars are coated with a thinner PDMS layer, as opposed to a thicker layer in SPME, a factor that allows improved enrichment efficiency [107, 108]. DSPE is another format of SPE based on the dispersion of solid sorbent materials in liquid samples to facilitate the isolation and extraction of target analytes from the complex sample matrix. In this process, matrix interferences remain embedded in the supernatant, which is later discarded while the target analyte is bound to the sorbent material and which is eventually eluted with a viable solvent [109]. DSPE has been employed in the enrichment and determination of PBDEs with recoveries within the range of 60–140% [110].
Complexity and matrix interferences encountered during sample preparation steps have attracted the invention of more selective sorbents to replace conventional silica sorbents that are associated with a number of drawbacks, such as instability at extreme pHs and low extraction efficiencies [111]. The new sorbents that include, nanocomposite materials, metal-organic frameworks, and molecularly imprinted polymers, among others, are characterized with high sensitivity and selectivity towards various environmental organic pollutants. They achieve fast dispersion and efficient recycling when applied in complex sample matrices [112, 113]. Reported nanocomposite sorbents in SPE for PBDE-containing samples include carbon nanotubes, graphene oxide (GO) [114, 115], and magnetic nanocomposite materials [113]. However, nanocomposite sorbents in classical SPE schemes have been associated with various drawbacks. A few of these challenges have been described in flow as well as batch systems, which originate from a slow flow rate of the sample through the packed SPE column and difficulty in separating the sorbent from the large volume of aqueous sample [113].
Other sorbent materials with fascinating properties are metal-organic frameworks (MOFs). These are hybrids of organic and inorganic materials characterized by a porous structure, large surface area, uniform nanoscale cavities, high adsorption capacity, and high thermal and chemical stability. Due to these advantageous properties, this class of materials has recently attracted enormous attention in the field of sample preparation [116]. The development of MOF adsorbents is still at its infancy stages, therefore, a limited number of studies have reported their application particularly in enrichment and determination of environmental PBDEs. A zirconium-based metal-organic framework material (UiO-66-OH) is a good example of a MOF. It has been synthesized and successfully applied as an adsorbent in SPME for enrichment and detection of 5 BDE congeners in milk samples using GC-MS, with low limits of detection in the range of 0.15–0.35 ng L−1 and excellent recoveries of 74.7%–118.0% [117]. A contrast study using silica-based sorbents in SPE for determination of 12 PBDEs in human serum, achieved mean recoveries of 64–95% and limits of detection in the range of 0.1–4.0 ng g−1 by using GC-MS [102], an evidence that MOF sorbents offer promising analytical results as compared with conventional sorbents.
With growing interest in sorbents that offer extraordinary extractive capability in SPE, molecularly imprinted polymers (MIPs) have been extensively explored as attractive options due to their robustness and selectivity towards particular target analytes providing exemplary substitute sorbents in sample clean-up and pre-concentration steps, especially in SPE and SPME [118]. MIPs are synthesized through molecular imprinting technology that involves polymerizing functional and cross-linking monomers in the presence of a target analyte, followed by the removal of the analyte to leave behind analyte-specific cavities. Their selectivity enables substantive removal of matrix interferents during the sample pre-treatment step [119]. MIP-based sorbents are readily available substitutes to silica-based adsorbents, which are reported to suffer from matrix interference, low selectivity, and sensitivity towards organic pollutants and may involve multiple steps that are labour-intensive for complete removal of interferences [120]. For example, commercial molecularly imprinted solid-phase extraction (MISPE) cartridges alongside alkaline extraction have been applied in aqueous enrichment and quantitation of PBDEs using GC-MS [121]. The extraction of PBDEs using MISPE gave recoveries above 60% compared to alkaline extraction which was below 60%. This confirms the selectivity capability of MIPs towards PBDEs from a complex environmental matrix. A more recent study has also reported recoveries of 60–87% in clean-up of soil and sediment samples using dummy molecularly imprinted polymers as SPE sorbent materials during determination of BDE-47 and BDE-99 [122].
However, a wide range of limitations still exist in MIPs, especially their poor water compatibility. Consequently, since MIPs and target analytes mainly interact through hydrogen-bonding, their recognition capability would be easily disturbed by polar solvents such as water. Therefore, the adsorption process is normally performed in non-polar or low-polar solvents such as dichloromethane and n-hexane rather than polar solvents. Additionally, polar solvents have a tendency to occupy binding sites, which affects the recognition capacity for the target analytes. In this context, it is necessary to continually invent new synthesis strategies for water-compatible MIPs [123, 124]. A summary of some of the sample pre-concentation strategies and their extraction efficiencies is presented in Table 2.
Sample preparation technique | PBDE congeners | Sample analyzed | Analytical technique | % Recoveries | Reference |
---|---|---|---|---|---|
SPE | BDE-28, 47, 49, 66, 85, 99, 100, 138, 153, 154, 183 & 209 | Human serum | GC-ECD | 64–95 | [102] |
BDE-47 and 99 | Soil and bottom sediment | GC-MS | 60–87 | [125] | |
PLE | BDE-28, 47, 99, 100, 153, 154 & 183 | Soil | GC-MS | 95 ± 9 | [68] |
BDE-28, 47, 99, 100, 154, 155 & 183 | Soil and sediment | GC-MS | 84–103 | [92] | |
LLE | BDE-17, 28, 47, 66, 71, 85, 99, 100, 138, 153, 154, 183 & 190 | Soil and sediment | GC-MS | 85–103 | [92] |
Soxhlet extraction | 42 mono- to deca-BDEs | Indoor dust sample | GC-MS | ≥ 70 | [72] |
UAE | BDE-1, 3, 7, 8, 28 & 47 | Industrial effluent | HPLC | 98.7 | [126] |
SPME | BDE-49, 99, 100, 153 & 154 | Milk and water | GC-ECD | 90–119 | [127] |
MAE | BDE-47, 99, 100, 138, 153, 154, 184 & 209 | Sewage sludge | GC-MS | 80–110 | [87] |
Examples of sample preparation strategies.
Sample pre-treatment steps are followed by quantification of the analytes using various detection systems. The choice of detection system depends on the physicochemical properties of the target analyte and the required detection levels. Detection techniques for quantification of PBDEs have evolved from liquid chromatography to gas chromatography and recently, miniaturized systems that involve the use of sensors. For chromatographic techniques, it’s important to optimize the operational parameters to actualize reliable instrumental results. It is highly recommended to use a sample injector with programmed temperature vaporization (PTV) to avoid degradation of thermally labile BDE congeners. Additionally, the temperature of injection should be accurately defined, especially when using a split/splitless injector, which minimizes chances of thermal degradation of higher BDEs congers as well as discrimination of lower brominated congeners [95, 128]. The choice of a column is another important aspect in the analysis of PBDEs where lower brominated congeners are well separated on longer columns, whereas higher brominated congeners are well separated on shorter columns. In the case of a mixture comprising of a wide range of BDE congeners, a short column is highly recommended, which well separates nona- and deca-BDEs [129]. HPLC coupled with mass spectrometry (MS), is one of the chromatographic techniques which has rarely been applied in the quantification of some PBDE congeners. The HPLC separation is hindered by several factors such as poor solubility of highly brominated diphenyl ethers in the polar solvents of the mobile phase, especially in reversed-phase, and, thus, requiring the sample to be enriched with an organic modifier. Normal phase HPLC has offered better separation of some PBDEs though it still results in incomplete separation, especially when an electrospray ionization detector is incorporated [130]. One group used an automated on-line sample preconcentration device coupled with HPLC-MS to determine decabrodiphenyl ether in human serum samples. This method achieved detection limits of 26.0 ng L−1 [130]. Otherwise, better detection limits of 0.2-25 ng L−1 were tenable when similar samples were analyzed for 12 PBDEs including decabromodiphenyl ether using gas chromatography-electron capture detection (GC-ECD) [102]. However, GC-ECD exhibits low selectivity and suffers from matrix interferences originating especially from halogenated species, as compared to GC-MS, which overcomes these challenges [131]. Fontana et al. [16] employed a coupled system, ultrasound-assisted emulsification microextraction-GC-MS (UAEMA-GC-MS) to determine PBDEs in water samples, with appreciably low detection limits of 1–2 ng L−1. Moreover, lower limits of detection are achievable when tandem-mass spectrometry (MS2) is utilized. For example, GC-MS2 has been reported to achieve detection limits within the range of 0.002–0.0136 ng g−1 lipid weight (lw) in the determination of PBDEs in breast milk and serum samples [132].
With the recent technological revolution, a more sensitive mass spectrometer, a high-resolution mass spectrometer (HRMS), has been found to be a promising alternative to a conventional mass spectrometer as it identifies the analyte without mass fragmentation and at the lowest mass unit [133]. With this new format of detection, very low detection limits of 0.000262–0.046 ng g−1 for 23 PBDEs in dust samples were achieved [134]. However, GC-HRMS is more expensive than conventional GC-MS, compelling researchers to often rely on GC-MS since it is less expensive and readily available. Besides, the demand for techniques that provide rapid results at minimal cost has resulted in the introduction of sensor technology in the determination of PBDEs. In this context, various detection systems have been fabricated and shown a discerning capability in the detection of PBDEs. For instance, an immunoassay detection system based on graphene oxide-polydimethylsiloxane has demonstrated desirable limits of detection of 0.018 ng g−1 for PBDEs in a standard solution and environmental water samples [135]. Similarly, a novel electrochemical immunoassay sensor used for the detection of BDE-28, 47, 99, 100, 153, and 154 in food samples, achieved a detection limit of 0.00018 ng L−1 [136]. These limits are comparable with those obtained by HPLC, GC-MS, or GC-HRMS. A surface-enhanced Raman scattering-based sensor is another detection system that has been successfully applied for rapid detection of BDE 47 in aqueous media, with detection limits of 0.0364 ng L−1 [137]. The use of sensory techniques is cheaper and a low concentration of contaminants can be detected. Moreover, the analysis duration is reduced from 10 minutes to 3 minutes. Thus, these sensor methods offer scope for further evaluation.
This chapter has discussed PBDEs as emerging environmental pollutants, their sources, and toxicological implications on humans and their determination in the environment. Sample pre-concentration methods for PBDE-containing samples that include UAE, PLE, UAME, PLE, SFE, SPE, SPME, SBSE, and DSPE have been critically reviewed as preferred alternatives to LLE and Soxhlet extraction due to their enhanced extraction efficiency. Novel SPE and SPME sorbents that provide the desired selectivity in the determination of PBDEs have also been discussed. Though these sorbents are promising, their application in MISPE in the determination of PBDEs has been scantly employed and its dynamics are still at its infancy stages. Therefore, there is room for continuous introduction of highly selective materials for the quantification of PBDEs in the environment. Alongside the evolution of sample pre-treatment techniques for the detection of PBDEs, rapid sensor-based techniques that achieve the desired figures of merit similar to traditional instrumentation techniques have demonstrated great potential.
ENN is grateful to the FLAIR fellowship programme, which is a partnership between the African Academy of Sciences and the Royal Society, funded by the UK Government’s Global Challenges Research Fund (GCRF), for financial support. BSM and VON thank the National Research Foundation (NRF) of South Africa, the University of KwaZulu-Natal (UKZN) and the UKZN Nanotechnology Platform for research support. BSM is also grateful for support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 734522, and for funding from NAS, USAID and DST (South Africa), under the PEER program cooperative agreement number: No. AID-OAA-A-11-00012.
The authors declare no conflict of interest.
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