1. Brendel Br 301 Manual 2017
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Abstract

Aim:

To develop a combined population pharmacokinetic model (PPK) to assess the magnitude and variability of exposure to both clozapine and its primary metabolite norclozapine in Chinese patients with refractory schizophrenia via sparse sampling with a focus on the effects of covariates on the pharmacokinetic parameters.

Methods:

Relevant patient concentration data (eg, demographic data, medication history, dosage regimen, time of last dose, sampling time, concentrations of clozapine and norclozapine, etc) were collected using a standardized data collection form. The demographic characteristics of the patients, including sex, age, weight, body surface area, smoking status, and information on concomitant medications as well as biochemical and hematological test results were recorded. Persons who had smoked 5 or more cigarettes per day within the last week were defined as smokers. The concentrations of clozapine and norclozapine were measured using a HPLC system equipped with a UV detector. PPK analysis was performed using NONMEM. Age, weight, sex, and smoking status were evaluated as main covariates. The model was internally validated using normalized prediction distribution errors.

Brendel Br 301 Manual 2017

Results:

A total of 809 clozapine concentration data sets and 808 norclozapine concentration data sets from 162 inpatients (74 males, 88 females) at multiple mental health sites in China were included. The one-compartment pharmacokinetic model with mixture error could best describe the concentration-time profiles of clozapine and norclozapine. The population-predicted clearance of clozapine and norclozapine in female nonsmokers were 21.9 and 32.7 L/h, respectively. The population-predicted volumes of distribution for clozapine and norclozapine were 526 and 624 L, respectively. Smoking was significantly associated with increases in the clearance (clozapine by 45%; norclozapine by 54.3%). The clearance was significantly greater in males than in females (clozapine by 20.8%; norclozapine by 24.2%). The clearance of clozapine and norclozapine did not differ significantly between Chinese patients and American patients.

Conclusion:

Smoking and male were significantly associated with a lower exposure to clozapine and norclozapine due to higher clearance. This model can be used in individualized drug dosing and therapeutic drug monitoring.

Introduction

Clozapine is a tricyclic dibenzodiazepine antipsychotic drug that is commonly used in the treatment of schizophrenia, particularly in patients who are refractory or intolerant to the side effects of traditional antipsychotics. Although clozapine may cause agranulocytosis in some patients, the incidence is approximately 0.38%. In addition, clozapine does not cause acute extrapyramidal toxicity or irreversible neurologic side effects. Because its benefits outweigh its side effects, clozapine has been accepted internationally as an antipsychotic drug and is used to treat approximately 31.7% of Chinese schizophrenia patients³. Clozapine is metabolized primarily by human cytochrome P450 (CYP) isozyme 1A2, yielding a pharmacologically active metabolite, norclozapine. Several investigators have shown that the degree to which clozapine is converted into norclozapine predicts the clinical outcome with respect to multiple measures of cognition, negative and positive symptoms, as well as quality of life^,. Thus, routine therapeutic drug monitoring (TDM) of clozapine and norclozapine is recommended to ensure safety and minimize toxic adverse events.

Significant intra- and inter-individual pharmacokinetic (PK) variability for clozapine and norclozapine has been observed in routine TDM^,. The influences of sex, smoking, CYP1A2 activity and other factors on clozapine plasma concentrations have been previously reported^,. However, studies exploring the effect of sex and smoking status on dose and concentration have shown mixed effects, and the clinical administration of clozapine is characterized by frequent dose changes and different dose intervals. Therefore, comparing only dose-normalized concentrations does not ensure the magnitude of the effect or provide suggestions for suitable personalized therapy.

Population pharmacokinetic (PPK) analysis is a robust tool for obtaining valuable PK information from both sparsely and intensively sampled data. The influence of potential covariates on drug exposure can also be quantitatively evaluated in PPK analysis by incorporating these covariates into the modeling process. This method also enables the characterization of both the inter-individual and intra-individual variabilities. Our research group has built a PPK model for clozapine in Chinese patients using retrospective TDM data. However, this model was limited by the evaluation methods and the small sample size and failed to provide thorough information on the PPK parameters of both clozapine and norclozapine. A review of the recent literature yielded two studies by one group on the PPK analysis of clozapine and norclozapine simultaneously. Robert R BIES et al developed a combined PPK model of clozapine and norclozapine for American patients based on the assumption that all clozapine was excreted as norclozapine, and they estimated the PK parameters using a fixed central volume of 7 L/kg for both clozapine and norclozapine^, using retrospective TDM data and outpatient concentration data.

In this study, another structural model for simultaneously analyzing clozapine and norclozapine in Chinese patients with schizophrenia was built without a fixed volume of distribution. The concentration data were collected using a prospective study design covering plasma data for both the absorption and distribution phases to calculate the PK parameters for clozapine and norclozapine. The results of this study may be used to better understand the PK characteristics of clozapine in Chinese patients with schizophrenia. Ultimately, our goal is to link the PK model with the pharmacodynamics (PD) of clozapine's antipsychotic effect to guide personalized therapy.

Materials and methods

Patients and data collection

The inclusion criteria for patients included a diagnosis of schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) established by administering the Structural Clinical Interview for DSM-IV (SCID) augmented by a review of the medical records. Most patients were treated with oral clozapine twice or three times per day. In this study, only one brand of clozapine (Jiangsu Nhwa pharmaceutical corporation limited, Xuzhou, China) was administered. Persons who had smoked five or more cigarettes per day within the last week were defined as smokers. Compliance was assessed by the determination of the serum concentration of clozapine several times during the study and by interview with the attending psychiatrists. Twenty percent of total samples were obtained between 0.5 h and 4.5 h after the last dose, and the remaining samples were obtained between 8.5 h and 15.5 h after the last dose. Previous institutional ethical approval was obtained, and all patients in the study gave written informed consent.

Relevant patient concentration data (eg, demographic data, medication history, dosage regimen, time of last dose, sampling time, concentrations of clozapine and norclozapine, etc) were collected using a standardized data collection form. The demographic characteristics of the patients, including sex, age, weight, body surface area, smoking status, and information on concomitant medications as well as biochemical and hematological test results were recorded.

Determination of clozapine and norclozapine concentrations in serum

An Agilent 1200 integrated high-performance liquid chromatography system equipped with a UV detector was used. The separation of the compounds was performed on an Inertsil ODS-3 column (5 μm, 150 mm×4.6 mm, id) at room temperature. The column was preceded by a Phenomenex SecurityGuard^TM C18 guard column (4.0 mm×3.0 mm id). The mobile phase consisted of methanol:acetonitrile:water:acetic acid:butylamine at 32:73:95:2:1 (v/v/v/v) and was pumped at a flow-rate of 1.0 mL/min. Detection was performed at the wavelength of 254 nm. The height ratios of the compounds' peaks to the desipramine peak (internal standard) were employed for all calculations. The lower limits of detection for clozapine and norclozapine were both 0.08 μmol/L. The coefficient of variation was less than 5% for both clozapine and norclozapine.

Population pharmacokinetic modeling

The PPK analysis was performed using NONMEM (version 7, level 1.0, ICON Development Solutions, Ellicott City, MD, USA)^,. The plasma concentration-time profiles for clozapine and norclozapine were described by a base structural model using the subroutine ADVAN5. The structural model (Figure 1) was based on the following assumptions: a proportion of clozapine was converted into norclozapine in the central compartment of clozapine through a first-order conversion mechanism and the rest was converted into other metabolites. The PK structural model was parameterized in terms of the apparent clearance and the apparent distribution volume of clozapine (CL/F and V/F), where F is the unknown oral bioavailability of clozapine; the clearance and the distribution volume of norclozapine (CL_M and V_M); and the fraction of the absorbed dose of clozapine converted into norclozapine (KF). A Bayesian approach conditioned on the population characteristics was used to estimate individual specific parameters. First-order conditional estimation methods (FOCE) and first-order conditional estimation methods with interaction (FOCE-I) were tested during model development.

Population pharmacokinetic model structure for clozapine and norclozapine. K₂₀, elimination rate constant for clozapine; V, volume of distribution for clozapine; K₂₃, rate constant for the conversion of clozapine into norclozapine; K₃₀, elimination rate constant for norclozapine; V_M, volume of distribution for norclozapine; KF, fraction of clozapine converted into norclozapine.

The unexplained random variability in the individual values of the structural model parameters were described in the inter-individual variability (IIV) model. The IIV of the PK parameters was assumed to be log normally distributed; the relationship between a parameter and its variance could be expressed as follows:

where P_j is the value of the parameter as predicted for the individual j, P_TV is the population typical value of the parameter, and η_p represents the difference in the estimated parameter for the jth subject from the population typical value, which was identically distributed with a mean of zero and a variance of ω_p².

The residual error was tested using additive, proportional, and combined error structures as follows:

Additive error model: y_ij=y_ij'+ɛ_ij

Proportional error model: y_ij=y_ij'×(1+ɛ_ij)

Combined additive and proportional error model:

where y_ij is the jth observation in the ith individual; y_ij' is the model's predicted value; and ɛ_ij and ɛ_ij' are the normally distributed random errors with mean values of zero and variances of σ₁² and σ₂², respectively. The residual error model was tested for the parent and metabolite in this model.

The full model was built by stepwise forward inclusion and the effect of each covariate was evaluated. The individual PK parameters obtained from the basic model were plotted against each covariate separately and the scatter plots helped to identify the trends and the regression pattern. Then, each covariate was incorporated stepwise into the basic regression model to develop the intermediate and full models. When the addition of a covariate resulted in a decrease in the objective function value (OFV) of >6.63 (chi-square, P<0.01, df=1), the covariate was considered statistically significant during the covariate forward-inclusion process. In this study, both continuous covariates (eg, age and weight) and discrete covariates (eg, sex, smoking status, and concomitant medications) were introduced into each parameter in a stepwise fashion. The relationship between the population typical value of the clearance and a continuous covariate such as age was evaluated using the following relationship:

where TVCL is the population typical value of clearance, AGE is the age of the individual in years, AGE_AVE is the average value of the age in the population and θ_AGE is the factor contributed by the covariate.

The following example shows the effect of a discrete covariate such as sex on clearance (CL/F):

When sex is female (female=0, male=1), TVCL equals θ_CL. For male subjects, the θ_cov term is added to the population estimate of clearance to modify this estimate.

For the final model, a backward elimination process was employed to identify significant covariates. The covariates in the full model were excluded one by one. The OFV was compared with that of the full model. A covariate was retained in the model when the elimination of that covariate resulted in an increase in the OFV of 7.88 (chi-square, P<0.005, df=1). We selected the model according to the reduction in the OFV value, goodness-of-fit plots, reductions in the IIV of structure model parameters, residual error, robust model parameter estimation, and model stability.

Model validation

Because the data were collected from routine TDM, there was a wide range in the time points, the number of drug administrations, and the drug dose for different patients. Additionally, observations were sparse data. To evaluate a model on the basis of such a complicated data set, the normalized prediction distribution error (NPDE) method, recently developed by Brendel and colleagues, was suitable. NPDE is a type of model evaluation method that can be used for internal or external evaluation. The NPDE should follow the normal standard distribution in theory, such that it can be used to evaluate different types of study designs. This method was implemented using the NPDE add-on software package, which was run in R (version 2.12.2).

Results

A total of 809 clozapine concentration data sets and 808 norclozapine concentration data sets were collected from 162 patients (74 males, 88 females) at multiple mental health sites in China. The characteristics of the patients are summarized in Table 1.

Table 1

A one-compartment pharmacokinetic model with mixture error best described the concentration of clozapine and norclozapine. The first-order absorption rate constant (K_a) for clozapine was fixed at 1.3 h⁻¹ based on several pharmacokinetic studies that obtained rich data describing the pharmacokinetics of clozapine in patients. The fraction of the absorbed dose of clozapine converted into norclozapine (KF) was fixed at 0.66 in published papers and was validated by the ratio of the mean amount of norclozapine to the mean amount of clozapine at steady-state in these articles^,. The PK parameters determined using the FOCE-I method deviated more from the theoretical population and individual predictions than the parameters determined using the FOCE. Therefore, the FOCE method was selected for the model described. The goodness-of-fit plots for clozapine and norclozapine were good (Figure 2).

Diagnostic plots for the final pharmacokinetic model. Plot of the observed concentrations vs the population-predicted clozapine (A) and norclozapine (E) concentrations. Plot of the observed concentrations vs the individual population-predicted clozapine (B) and norclozapine (F) concentrations. Plot of the conditional weighted residual error (CWRES) vs the population-predicted clozapine (C) and norclozapine (G) concentrations. Plot of the conditional weighted residual error (CWRES) vs the time after first dose of clozapine (D) and norclozapine (H).

In the final model, sex, and smoking status were identified as significant covariates for the clearance of clozapine and norclozapine. Adding each covariate independently using stepwise forward inclusion improved the fit of the model. The population-predicted clearance of clozapine and norclozapine in female nonsmokers were 21.9 and 32.7 L/h, and the population-predicted volumes of distribution for clozapine and norclozapine were 526 and 624 L, respectively. Smoking was associated with increases in the clearance of clozapine and norclozapine of 45% (P<0.001) and 54.3% (P<0.001), respectively. The clearance of clozapine and norclozapine were 20.8% (P<0.005) and 24.2% (P<0.005) greater, respectively, in males than in females (Figure 3). Other covariates such as weight and age did not significantly influence the PK parameters of clozapine and norclozapine. The combined effects of sex (P<0.005) and smoking (P<0.001) on the clearance of clozapine and norclozapine were determined. Male smokers were exposed to larger dosages due to the higher clearance of clozapine. The final population PK parameters are summarized in Table 2.

Boxplot with the median and interquartile range of the clearance of clozapine and norclozapine according to sex and smoking status. Clozapine clearance in female nonsmokers, male nonsmokers and male smokers (A). Norclozapine clearance in female nonsmokers, male nonsmokers, and male smokers (B).

Table 2

CL, clozapine clearance; V, clozapine volume of distribution; CLM, norclozapine clearance; V_M, norclozapine volume of distribution; KF, the fraction of clozapine converting to norclozapin; θSmoking, effect of smoking status on clearance; θGender, effect of smoking status on clearance; σ₁, coefficient of variation of additive residual error; σ₂, coefficient of variation of proportional residual error; N/A: not available

The graphs of clozapine (A) and norclozapine (B) for the NPDE validated the prediction of the model (Figure 4). The upper left graph is a quantile-quantile plot (QQ plot) comparing the distribution of the NPDE to the theoretical N (0, 1) distribution, and the upper right graph is a histogram of the NPDE with the density of N (0, 1) overlaid. In the two lower graphs, the NPDE is plotted against time (the independent variable X) and the predicted concentration (predicted Y), respectively^,. For clozapine and norclozapine, the mean of the distribution of the NPDE was close to 0, and the variance was small. The two lower graphs show that the same trend was observed for both NPDE and the real data. These results indicate that the model of the clozapine and norclozapine concentrations was relatively accurate and reliable.

Results of the NPDE analysis for clozapine (A) and norclozapine (B). The left upper plot is a QQ-plot for NPDE; the right upper plot is a histogram of the NPDE; the left lower and right lower plots represent the NPDE versus time and the NPDE versus the predicted concentrations of clozapine or norclozapine, respectively.

Discussion

The NONMEM method plays an important role in routine therapeutic drug monitoring, and an increasing number of researchers are using modeling to estimate PK parameters with the sparse sampling design. In our study, we collected blood samples during the absorption and distribution phases and combined these samples with those for elimination phase from TDM to obtain typical values for the clearance and volume of distribution of clozapine and norclozapine. The combined PPK model was used to quantify the effects of sex and smoking status on the clearance of clozapine and norclozapine and to predict the concentrations of these compounds at different dosage levels in Chinese patients with schizophrenia.

Clozapine can be converted into more than 10 metabolites. The primary enzymes involved in the biotransformation of clozapine are CYP1A2 and CYP3A4, which convert clozapine into norclozapine and clozapine-N-oxide, respectively. CYP2C19 may also have significant effects on the conversion of clozapine into norclozapine. CYP2C9 and CYP2D6 appear to play minor roles. Norclozapine is further converted into a more polar compound for elimination. The clozapine and norclozapine metabolic pathway had been described in detail by Dain and Khan et al^,. We built a combined structural model to explain the conversion of clozapine into norclozapine (Figure 1) based on the metabolism of clozapine. Therefore, in the current study, we assumed that clozapine was partly converted into norclozapine with a conversion fraction (KF), which was more reasonable than the assumption in previous PPK studies with respect to the process of clozapine metabolism^,. The model proposed in this study provided an adequate fit to the data for both clozapine and norclozapine.

Smoking status was previously identified as a statistically significant covariate affecting the clearance of clozapine and norclozapine^,. Because the activity of CYP1A2 is greater in smokers than in non-smokers, the clearance of clozapine is likely influenced by smoking status. It was reported that smoking can increase the clearance of clozapine and that the sudden cessation of smoking can cause a significant decrease in caffeine clearance (P<0.01) of 36.1%^,. Sex was also reported to be a significant covariate affecting the clearance of clozapine and norclozapine^,. Previous studies have shown that CYP1A2 activity was lower in females than in males, whereas other studies found no differences in CYP1A2 activity between males and females and insisted that smoking could induce CYP1A2 activity and reduce clozapine clearance^,. Smoking and male were associated with lower exposure to clozapine and norclozapine due to their higher clearance. Considering the mixed effect of smoking and sex, we made use of the PPK model to quantify the mixture effect on the clearance and concentrations of clozapine and norclozapine. Although our study contained only a few female patients who smoked (n=2), the effect of smoking on females could be inferred by the built model. The clearance of clozapine and norclozapine was 23.6% higher in female smokers than in male nonsmokers.

The clearance of clozapine and norclozapine in Chinese patients was similar to the published value for American patients. The results suggest that the enzyme activity in different populations is not significantly different.

The impact of age on the clearance of clozapine and norclozapine has been investigated. Haring et al found that clozapine concentrations in patients aged between 45 and 54 years were higher than that in patients aged between 18 and 26 years, but the effect was not statistically significant. Another recent study without smoking information demonstrated there was a negative effect of age on the clearance of clozapine. However, in some studies with smoking information, the investigators were unable to find a significant effect of age on the clozapine blood concentration^,. In our research, the effect of age on the clearance of clozapine and norclozapine was also not statistically significant compared with the effects of smoking status and sex.

The model used in the present study was internally validated using the NPDE. The prediction for clozapine was accurate and reliable, although slight bias existed in the prediction for norclozapine.

There were some limitations in our study. First, smoking status was assessed using patient self-reporting and nurse evaluations. We dichotomized patients into smokers and nonsmokers but did not assess the magnitude of smoking. A lack of objective biological measures (such as the serum nicotine level) may have resulted in false-positive and false-negative cases. Second, the intake of concomitant medications in our patients was complicated. Although these concomitant medications might have interacted with the pharmacokinetics of clozapine and norclozapine, this interaction was not adequately confirmed by our study design.

In conclusion, a one-compartment model with first-order absorption adequately described the concentration data for clozapine and its active metabolite norclozapine. Smoking status and sex were identified as two significant covariates affecting the clearance of clozapine and norclozapine. These findings may account for some of the variability in clozapine and norclozapine exposure, and the dosage regimen in Chinese populations may need to be adjusted to improve the efficacy and safety of clozapine based on patients' smoking status and sex.

Author contribution

Brendel Br 301 Manual Free

Chuan-yue WANG and Wei LU designed the research; Chuan-yue WANG, Li-jun LI, Pei-xin, FU, and An-ning LI performed the research; Wen-biao LI and Wei GUO determined the serum concentrations of clozapine and norclozapine; Wei LU, De-wei SHANG, Li-jun LI, Xi-pei WANG, Yu-peng REN, and Shuang-min JI built the model; and De-wei SHANG and Li-jun LI wrote the paper.

Acknowledgments

The authors are extremely grateful to Chen-hui DENG, Liang ZHANG and Fu-chun ZHOU, who provided valuable comments.

This study was supported by the National Natural Science Foundation of China (Grant No 30770776), the National High Technology Research and Development Program of China (863 Program) (Grant No 2009AA022702), the Beijing Municipal Education Commission Science and Technology Development Program (Grant No KM201110025025), the Beijing Municipal Science and Technology Commission (Grant No D101107047810001) and the Beijing Postdoctoral Research Foundation (Grant No 2011ZZ-11).

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The current considerations about complete suicides and suicide attempts in different cultures call the attention of professionals from different areas to this serious public health problem. It is estimated that almost 1 million people commit suicide per year worldwide. In Brazil, there are 32 complete suicides every day from which about 96.8% had some identifiable mental disorder.

Suicidology is the field of interdisciplinary knowledge that brings together professionals from different specialties to enable them to construct proposals of prevention and acute intervention from the integration of different levels of analysis. Considering the levels of analysis derived from the basic sciences, the search for biomarkers related to suicidal behavior points, for example, to the participation of neural systems associated to the modulation of responses to stressors (e.g., hypothalamic–pituitary–adrenal axis and the noradrenergic system of the locus coeruleus), neuroinflammatory mechanisms system (e.g., increased levels of interleukins 1 and 6 in the frontopolar cortex), and also the serotonergic hypofunction (1). On the other hand, considering the levels of macroscopic analysis studied by Sociology, the relationship between the individual and society has been considered since the first trials to understand the phenomenon. Durkheim’s classic works (2), for example, already predicted different types of suicide sociologically determined such as the anomic (suicidal behavior that happens because of the acute social chaos), fatalistic (suicide due to the lack of hope of overcoming oppression and of repression of individual freedom), altruistic (suicide motivated by an ideal that the subject considers him/herself as more important than his/her own life), and selfish (characterized by the chronicity of a weak bond between the person and the society).

More recently, integrative approaches have shown that the confluence of multiple biological and social factors modulate diverse psychopathologies and dysfunctional behaviors, such as suicidal behavior (3). Some of the results of the Dunedin longitudinal study clearly show that throughout life, biological vulnerability factors added to adverse social conditions increase the chance of ideation and suicide attempts (4). In this study, individuals who presented two copies of the short allele of this gene (related to a lower serotonergic function) and experienced more stressful situations presented a higher risk of showing ideation and suicide attempts. In this perspective, Brodsky (5), using the stress-diathesis model, suggests that adverse childhood experiences (e.g., family stress, abuse, and other types of violence) associated with biological characteristics (e.g., serotonergic hypofunction and HPA axis dysfunction) can create a vulnerability factor that, in adult life, in the face of stressful situations increases the risk of suicidal behavior. This, in its turn, involves self-aggression, suicidal preparation, the attempt and the act itself.

Considering the level of intermediate analysis, the one of individual differences, personality traits, and cognitive functioning, are also of great importance for understanding the suicidal phenomenon. For example, DeShong et al. (6) have demonstrated that personality traits can be considered important predictors of suicidal ideation. The results of this study indicate that high levels of neuroticism and low levels of extroversion are related to the current presence of suicidal ideation in a sample of university students. The authors also verified that high levels of neuroticism are positively and significantly correlated with two other personality traits that indicate the high risk for a suicide attempt: the perception of oneself as burdensomeness for the others (Perceived Burdensomeness) and the perception to be disconnected from society (Thwarted Belongingness). Moreover, the relationship between personality traits and the success of interventions has also been verified. For example, the extroversion trait seems to make the individual more sensitive to social support. In situations of low social support, extroversion tends to be significantly related to suicidal ideation (7).

Cognition and Suicide Behavior

In relation to cognitive factors, we can group them into cognitive schemas of reality interpretation and basic cognitive processes. Cognitive schemata of hopelessness (belief that the situation will not be resolved in the future) and intolerance to suffering are examples of interpretation patterns of reality in patients with suicidal ideation (8). On the other hand, different types of primary cognitive alterations are related to suicidal behavior, especially those resulting from changes in frontostriatal circuits (9). Among such cognitive mechanisms can be highlighted the attentional bias for environmental cues related to suicide, impulsive behavior, verbal fluency deficits, non-adaptive decision-making, and reduced planning skills.

Attentional bias consists in the effect of thoughts and emotions, frequently not conscious, about the perception of environmental stimuli. Attentional bias makes the individual to pay too much attention to specific environmental cues that are related to the psychiatric disorder he/she presents (10). According to Wenzel et al. (8), suicidal ideation and hopelessness can make the patient unable to find alternative solutions to their problems other than suicide, biasing their attention to environmental cues related to such behavior.

One of the most used tasks to evaluate attentional bias is the modified Stroop paradigm, involving processing of stimuli related to emotion. Stroop paradigms of an emotional nature usually require the patient to say, as quickly as possible, the name of the color with which certain words were written. These words may be neutral or have negative or positive emotional valence. In studies of attentional bias for suicide, neutral words, positive valence words, words with general negative valence, and words with negative valence related to suicide are generally used. As the patient processes more automatically read the word and give the desirable answer, that is, to name the color. Richard-Devantoy et al. (11) verified that patients with a history of suicide attempt perform poorly on emotional Stroop tasks when they have to process suicide-related words. Cha et al. (12), in a longitudinal study, found that the attentional bias for suicide-related stimuli is an important predictor of future attempts. In this study, the authors evaluated patients treated in the psychiatric emergency with a modified version of the Stroop test containing words with positive (e.g., happiness), negative (e.g., solitude), neutral valence (e.g., museum), and suicide related (e.g., death). Besides presenting a greater attention bias for words related to suicide, attentional bias was an important predictor of further attempts during the 6 months after the evaluation.

Bias in the processing of emotional information may also influence the way facial expressions are identified, even in individuals with subclinical depressive symptoms who have suicidal ideation. Maniglio et al. (13) verified, in a sample of subjects of the general population, that the presence of suicidal thoughts was related to the tendency to interpret neutral faces as they were expressing sadness.

Recent research efforts are directed to assess the possible use of attention bias as a therapeutic target in patients presenting suicide behavior. In a randomized control trial (RCT), a community sample of individuals who reported past month suicide ideation and inpatients admitted for suicide ideation or attempt were submitted to four training sessions of attentional bias modification (ABM) and their cognitive performance compared to a control group (14). The authors did not find significant differences between the groups even stratifying the analysis for severe suicide ideators. Possible reasons for the negative findings included number and duration of training sessions, lack of AMB efficacy for suicide-specific attentional bias, and type II error due to sample size (14). Other studies addressed the potential of AMB in alleviating clinical conditions that are known risk factors for suicide attempts. In a trial for testing the potential of ABM in reducing depressive symptoms in adolescents with depressive disorders, a major risk for suicidal behaviors (15), Yang et al. have compared active AMB intervention versus placebo ABM training (16). The former was associated with reduction on clinical-rated and self-reported depressive symptoms compared with the control group at 12-month follow-up assessment, as well as with higher remission rates (16). On the other hand, de Voogd et al., throughout a multi-center RCT with a non-clinical sample of adolescents, did not find significant differences regarding anxiety and depressive symptoms between intervention and control group (17). Insomnia, another clinical characteristic associated with suicidal behaviors (18, 19), was a target for ABM in a RCT by Lancee et al. (20). The results showed no evidence of efficacy for AMB in decreasing sleep symptoms problems, which may be related to the low level of attention bias at baseline (20). The discrepancy in the literature for the AMB as a potential therapeutic tool for treating suicidal thoughts as well as its risk factors, suggests that further studies exploring different protocols interventions and clinical sample characteristics should be performed.

The relationship between impulsivity and suicide has been largely investigated over the last decades and there is still controversy about the theme. Although there is strong evidence linking impulsivity to suicide attempts, and particularly to violent attempts [see, for example, Ref. (21)], Smith et al. (22) suggest that, if suicide is planned, the suicide behavior could not, in these cases, be explained by the impulsive tendency. Reyes-Tovilla et al. (23) verified important differences between people who attempt suicide in an inapposite way and those who premeditate the attempt. The second group tends to try in a more lethal way, has higher rates of comorbidity with alcoholism, and use cannabis besides having lower level of education. On the other hand, given the multidimensional nature of impulsivity, it is plausible to think that some specific types of impulsive manifestation would be more related to suicidal behavior. Thus, Malloy-Diniz et al. (24) and Neves et al. (25) verified that the impulsive and immediate decision-making is more related to the suicide attempts in bipolar patients. It is plausible to think here that even planned suicides could be understood by these results, yet planning would focus on the end of immediate suffering. These findings have been consistently replicated by other studies (11) showing that the decisional focus on psychiatric patients may be one of the risk factors for the suicide act.

Impulsivity can serve both as a moderator and mediator variable in the association between several diagnostic and clinical conditions with suicide ideation or attempts. Wang et al. (26) evaluated 162 patients with major depressive disorder and found that those with higher impulsivity, regardless depression severity, were more likely to present suicide ideation (26). In addition, specific neural circuits associated with impulsivity and aggression may mediate the lethality of suicidal behavior in patients with borderline personality disorder (27).

Thus, effective interventions address to reduce impulsivity in clinical populations at higher risk for suicide could help in the prevention. An internet-based psychoeducation approach was compared to a control group with no psychoeducation for the treatment of women with DSM-IV (28) criteria for borderline personality disorder. Among several others outcomes, the experimental group with psychoeducation showed significantly decrease in impulsivity scores (29). Thylstrup et al. (30) applied a different type of psychoeducational program for patients with substance use and antisocial personality disorder in a randomized trial. The Impulsive Lifestyle Counselling, with sessions that included linking “patients’ impulsive behaviors to the immediate consequences,” was associated with positive effects in reducing substance use behaviors, a major suicide risk factor (30, 31). Impulsivity was also decreased in a sample of high school students, an age group which suicide is the second main cause of dead (32), after a mindfulness training program (MTP). Ten weekly sessions of the MTP was associated with significantly reduction in cognitive, motor, and non-planning dimensions of impulsivity as compared to the control group (33).

As mentioned above, impulsive decision-making related to imediatistic focus regardless long-term consequences is frequently associated with suicide behavior. Oldershaw et al. (34) evaluated whether there was an association between improvements in decision-making and reduction on suicide ideation in adolescents with a history of self-harm, after a CBT treatment (34). According to the authors, the therapy has included elements with the aim of strength decision-making skills, measured in this study by the Iowa Gambling Task (IGT) (35). Despite improvements of IGT scores within the CBT group, changes in IGT scores did not correlate with suicide ideation (34).

Deficits in problem-solving ability also seem to be distorted in patients who attempt suicide. In particular, in situations of stress, problem-solving skills are important factors in minimizing the effect on mental health. In this perspective, Grover et al. (36) verified that deficits in problem-solving abilities in patients submitted to moderate and high situations of stress are related to suicidal behavior in adolescents. Khan et al. (37) found that university students who present productive strategies to manage stressful situations (work with the focus on the problem, maintaining optimism, seeking help and support from other people) and social support had lower chances to attempt suicide. In a study that evaluated the relationship between problem-solving skills and suicidal ideation in people who experienced childhood abuse, Kwok et al. (38) found that the rational style of problem-solving acts as a moderator in the relationship between abuse in childhood and suicide attempt in adulthood. Such association was described only in the women who participated in the study. The style of problem solving also seems to be related to suicidal behavior. Quinones et al. (39) found that patients who attempt suicide tend to present more passive strategies of solution of problems, that is, they are dependent on the action of other people, related to luck and chance or over time.

Cognitive-behavioral problem solving was compared to “treatment as usual” for several outcomes, including suicide ideation and attempts, in a sample of high-risk individuals, with positive findings, especially in short term (40). Cognitive therapy was also associated with a faster improvement in negative problem orientation among individuals with history of recent suicide attempt in another randomized controlled trial (41). Problem-solving therapy may be especially effective in older patients with depression and executive dysfunction. Gustavson et al. (42) reported lower frequency of reported suicide ideation in the experimental group in comparison to supportive therapy up to 36 weeks after treatment (42). At least part of the effectiveness of mindfulness-based interventions for suicide prevention has also been related by means of improvement in problem solving, as well as attentional dyscontrol and abnormal stress response (43). Thus, therapies with the aim of improve problem-solving abilities, especially active problem solving (39), should be considered in individuals at higher risk for suicide.

Conclusion

Evidences linking cognitive deficits and suicide behavior are consistent. Jollant et al. (9) group the main cognitive difficulties in people who attempt suicide in three categories that synthesize the above-mentioned findings and aggregate other described changes. The categories include

(1) Changes in the modulation and attribution of values to the experiences, which would involve an attentional and emotional biased response to environmental stimuli and less adaptive decisions in relation to the perception of environmental risks.

(2) Deficits in emotional and cognitive regulation including cognitive inflexibility, poor repertoire related to planning and problem solving, lowered verbal fluency affecting communicational ability.

(3) Behavioral facilitation in emotional contexts, characterized by impulsive response in situations of major stress and affective overload.

Moreover, cognitive deficits in psychiatric patients are important therapeutic targets, despite the paucity of intervention studies considering cognition as a therapeutic target in suicide patients, training self-regulatory processes, including decision-making skills, efficient problem solving, and impulse control present potential for clinical use in suicide prevention.

Behavioral and cognitive interventions has been associated with reductions on suicide ideation, as well as suicide attempts in different populations (1–4), probably by targeting different cognitive dysfunctions associated to suicide behaviors, in addition to anxiety and depressive symptoms. Thus, specific interventions toward these cognitive domains, such as attentional bias, impulsivity, problem solving, and decision-making, could help to maximize the efficacy of the available therapeutic options. Future studies are needed to evaluate the effectiveness of cognitive training for this purpose.

Author Contributions

All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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