A case report of clozapine-treatment-resistant schizophrenia successfully managed with brexpiprazole combination therapy.

Clozapine-resistant schizophrenia (CRS) occurs in 40%- 70% of clozapine-treated schizophrenic patients. Hereby we describe a 20-year-old CRS subject with comorbid cannabinoid use disorder, successfully treated with clozapine-brexpiprazole combination, subsequently switched to clozapine plus long-acting injectable aripiprazole.

Integration analysis of GWAS and expression quantitative trait loci to identify candidate genes and pathways for clozapine-related neutropaenia.

AIMS: Little is known about the genetic basis of clozapine-related neutropaenia. This study aims to explore candidate genes and pathways involved in clozapine-related neutropaenia. METHODS: This study conducted a two-stage integrative analysis of the summary statistics from the genome-wide association study (GWAS, n = 552) of the lowest absolute neutrophil count (ANC) during clozapine treatment and the summary data of the expressed quantitative trait locus (eQTL). First, we use the probabilistic Mendelian randomization (PMR-Egger) to identify genes whose expression is causally related to ANC, and then use Bayesian co-localization analysis to investigate whether there are shared causal variants between them [posterior probability for hypotheses 4 (PP.H4) > 0.80]. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore the pathways that may be associated with ANC during clozapine treatment. RESULTS: PMR-Egger analysis identified 146 genes that may be causally associated with ANC after Bonferroni correction (P-value < 3.25e-6). Bayesian co-localization analysis identified six further genes whose gene expression shared common variants with ANC, including NT5E (PP.H4 = 0.96), GLDC (PP.H4 = 0.82), NUDT17 (PP.H4 = 0.88), MSH4 (PP.H4 = 0.88), PTER (PP.H4 = 0.89) and SERPINB6 (PP.H4 = 0.83). Enrichment analysis identified 52 GO terms and seven pathways associated with ANC, such as NAD metabolic process, drug catabolic process and glyoxylate and dicarboxylate metabolism. CONCLUSION: This study identified multiple candidate genes and pathways that may be involved in clozapine-related neutropaenia, providing novel clues for the mechanism of clozapine-related neutropaenia.

Quantitative biomarkers to predict response to clozapine treatment using resting EEG data.

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biomarkers of clozapine response is extremely valuable to aid on-time initiation of clozapine treatment. In this study, we develop a machine learning (ML) algorithm based on the pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the Positive and Negative Syndrome Scale (PANSS). The ML algorithm has two steps: 1) an effective connectivity named symbolic transfer entropy (STE) is applied to resting state EEG waveforms, 2) the ML algorithm is applied to STE matrix to determine whether a set of features can be found to discriminate most responder (MR) SCZ patients from least responder (LR) ones. The findings of this study revealed that the STE features could achieve an accuracy of 89.90%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

Relegating Psychosis: Blood Work and "Routine Connection" in the Clozapine Clinic.

This paper attends to the sociality available in the clozapine clinic regimen and suggests that the social dimensions of clozapine treatment may be as important as the biochemical efficacy of clozapine. The clozapine clinic is where people diagnosed with chronic schizophrenia who take the antipsychotic clozapine go for routine monitoring of clozapine side effects, particularly haematological effects. Psychopharmaceutical treatments are often criticized for being reductionistic and dehumanizing, but clozapine clinics offer increased clinical contact in the age of deinstitutionalization. The inadvertent social benefits of biomedically reductive treatments have not previously been ethnographically attended to in the clozapine-only context. Drawing on 18 months of ethnographic fieldwork with 43 clozapine clients and 16 clinical caregivers in two clozapine clinics in the United Kingdom in Australia, I argue that routine clinical attachments in the clozapine clinic can serve a therapeutic role in terms of providing opportunities for clients' health agency, social competence and accountability. This socio-therapeutic quality appeared to be available because the clinical emphasis was not on psychotic illness. It depended, however, on reliable and familiar social exchanges inside the clinic and on the predictability of clinical activity. The importance of unemotional but unfailing relationships and rhythms in the clozapine clinic context echoes cross-cultural findings about how schizophrenia is managed more productively in environments that invite more neutral and equal social exchanges.

A Questionnaire-based Study of the Views of Schizophrenia Patients and Psychiatric Healthcare Professionals in Japan about the Side Effects of Clozapine.

OBJECTIVE: It is well documented that clozapine treatment causes agranulocytosis, but it can also induce drowsiness, constipation, and hypersalivation; however, these symptoms are usually less severe. It has been reported that clozapine-treated patients with schizophrenia and psychiatric healthcare professionals consider different side effects to be important. The aim of this study was to assess current practice related to the side effects of clozapine in clozapine-treated patients with schizophrenia and psychiatric healthcare professionals in Japan. METHODS: Data were collected from January 2014 to August 2015 in Okehazama Hospital, Kakamigahara Hospital, and Numazu Chuo Hospital. Clozapine-treated patients with schizophrenia and psychiatric healthcare professionals (psychiatrists and pharmacists) were enrolled in this study. RESULTS: Of the 106 patients and 120 psychiatric healthcare professionals screened, 100 patients and 104 healthcare professionals were included in this study. We asked the patients what side effects caused them trouble and we asked psychiatric healthcare professionals what side effects caused them concern. The patients and psychiatrists held similarly positive views regarding the efficacy of clozapine. The healthcare professionals were concerned about agranulocytosis (92.4%), blood routines (61.3%). On the other hand, the patients experienced hypersalivation (76.0%), sleepiness (51.0%). A positive correlation (R=0.696) was found between patient satisfaction and DAI-10 score. CONCLUSION: Patients experienced more problems than healthcare professionals expected. However, usage experience of clozapine healthcare professionals tended to have similar results to patients. It is necessary that all healthcare professionals fully understand the efficacy and potential side effects of clozapine. This is very important for promoting clozapine treatment in Japan.

A Questionnaire-based Study of the Views of Schizophrenia Patients and Psychiatric Healthcare Professionals in Japan about the Side Effects of Clozapine

OBJECTIVE: It is well documented that clozapine treatment causes agranulocytosis, but it can also induce drowsiness, constipation, and hypersalivation; however, these symptoms are usually less severe. It has been reported that clozapine-treated patients with schizophrenia and psychiatric healthcare professionals consider different side effects to be important. The aim of this study was to assess current practice related to the side effects of clozapine in clozapine-treated patients with schizophrenia and psychiatric healthcare professionals in Japan. METHODS: Data were collected from January 2014 to August 2015 in Okehazama Hospital, Kakamigahara Hospital, and Numazu Chuo Hospital. Clozapine-treated patients with schizophrenia and psychiatric healthcare professionals (psychiatrists and pharmacists) were enrolled in this study. RESULTS: Of the 106 patients and 120 psychiatric healthcare professionals screened, 100 patients and 104 healthcare professionals were included in this study. We asked the patients what side effects caused them trouble and we asked psychiatric healthcare professionals what side effects caused them concern. The patients and psychiatrists held similarly positive views regarding the efficacy of clozapine. The healthcare professionals were concerned about agranulocytosis (92.4%), blood routines (61.3%). On the other hand, the patients experienced hypersalivation (76.0%), sleepiness (51.0%). A positive correlation (R=0.696) was found between patient satisfaction and DAI-10 score. CONCLUSION: Patients experienced more problems than healthcare professionals expected. However, usage experience of clozapine healthcare professionals tended to have similar results to patients. It is necessary that all healthcare professionals fully understand the efficacy and potential side effects of clozapine. This is very important for promoting clozapine treatment in Japan.

A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.

OBJECTIVE: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysiologic changes induced by Clozapine (CLZ) treatment in responding schizophrenic (SCZ) subjects. This objective is of particular interest because CLZ, though a potentially dangerous drug, can be uniquely effective for otherwise medication-resistant SCZ subjects. We wish to determine whether ML methods can be used to identify a set of EEG-based discriminating features that can simultaneously (1) distinguish all the SCZ subjects before treatment (BT) from healthy volunteer (HV) subjects, (2) distinguish EEGs collected before CLZ treatment (BT) vs. those collected after treatment (AT) for those subjects most responsive to CLZ, (3) discriminate least responsive subjects from HV AT, and (4) no longer discriminate most responsive subjects from HVs AT. If a set of EEG-derived features satisfy these four conditions, then it may be concluded that these features normalize in responsive subjects as a result of CLZ treatment, and therefore potentially provide insight into the functioning of the drug on the SCZ brain. METHODS: Odd-ball auditory evoked potentials of 66 HVs and 47 SCZ adults both BT and AT with CLZ were derived from EEG recordings. Treatment outcome, after at least one year follow-up, was assessed through clinical rating scores assigned by an experienced clinician, blind to EEG results. Using a criterion of at least 35% improvement after CLZ treatment, subjects were divided into "most-responsive" (MR) and "least-responsive" (LR) groups. As a first step, a brain source localization (BSL) procedure was employed on the EEG signals to extract source waveforms from specified brain regions. ML methods were then applied to these source waveform signals to determine whether a set of features satisfying the four conditions outlined above could be discovered. RESULTS: A set of cross-power spectral density (CPSD) features meeting these criteria was identified. These CPSD features, consisting of a combination of brain regional source activity and connectivity measures, significantly overlap with the default mode network (DMN). All decrease with CLZ treatment in responding SCZs. CONCLUSIONS: A set of EEG-derived discriminating features which normalize as a result of CLZ treatment was identified. These discriminating features define a network that shares significant commonality with the DMN. Our findings are consistent with those of previous literature, which suggest that regions of the DMN are hyperactive and hyperconnected in SCZ subjects. Our study shows that these discriminating features decrease after treatment, consistent with portions of the DMN normalizing with CLZ therapy in responsive subjects. SIGNIFICANCE: Machine learning is proposed as a potentially powerful tool for analysis of the effect of medication on psychiatric illness. If replicated, the proposed approach could be used to gain some improved understanding of the effect of neuroleptic medications in treating psychotic illness. These results may also be useful in the development of new pharmaceuticals, since a new drug which induces changes in brain electrophysiology similar to those seen after CLZ could also have powerful antipsychotic properties.