Atypical antipsychotics have greatly enhanced the treatment of schizophrenia. The mechanisms underlying the effectiveness and adverse effects of these drugs are, to date, not sufficiently explained. This article summarises the hypothetical mechanisms of action of atypical antipsychotics with respect to the neurobiology of schizophrenia.When considering treatment models for schizophrenia, the role of dopamine receptor blockade and modulation remains dominant. The optimal occupancy of dopamine D(2) receptors seems to be crucial to balancing efficacy and adverse effects - transient D(2) receptor antagonism (such as that attained with, for example, quetiapine and clozapine) is sufficient to obtain an antipsychotic effect, while permanent D(2) receptor antagonism (as is caused by conventional antipsychotics) increases the risk of adverse effects such as extrapyramidal symptoms. Partial D(2) receptor agonism (induced by aripiprazole) offers the possibility of maintaining optimal blockade and function of D(2) receptors. Balancing presynaptic and postsynaptic D(2) receptor antagonism (e.g. induced by amisulpride) is another mechanism that can, through increased release of endogenous dopamine in the striatum, protect against excessive blockade of D(2) receptors. Serotonergic modulation is associated with a beneficial increase in striatal dopamine release. Effects on the negative and cognitive symptoms of schizophrenia relate to dopamine release in the prefrontal cortex; this can be modulated by combined D(2) and serotonin 5-HT(2A) receptor antagonism (e.g. by olanzapine and risperidone), partial D(2) receptor antagonism or the preferential blockade of inhibitory dopamine autoreceptors. In the context of the neurodevelopmental disconnection hypothesis of schizophrenia, atypical antipsychotics (in contrast to conventional antipsychotics) induce neuronal plasticity and synaptic remodelling, not only in the striatum but also in other brain areas such as the prefrontal cortex and hippocampus. This mechanism may normalise glutamatergic dysfunction and structural abnormalities and affect the core pathophysiological substrates for schizophrenia.

This international guideline proposes improving clozapine package inserts worldwide by using ancestry-based dosing and titration. Adverse drug reaction (ADR) databases suggest that clozapine is the third most toxic drug in the United States (US), and it produces four times higher worldwide pneumonia mortality than that by agranulocytosis or myocarditis. For trough steady-state clozapine serum concentrations, the therapeutic reference range is narrow, from 350 to 600 ng/mL with the potential for toxicity and ADRs as concentrations increase. Clozapine is mainly metabolized by CYP1A2 (female non-smokers, the lowest dose; male smokers, the highest dose). Poor metabolizer status through phenotypic conversion is associated with co-prescription of inhibitors (including oral contraceptives and valproate), obesity, or inflammation with C-reactive protein (CRP) elevations. The Asian population (Pakistan to Japan) or the Americas’ original inhabitants have lower CYP1A2 activity and require lower clozapine doses to reach concentrations of 350 ng/mL. In the US, daily doses of 300–600 mg/day are recommended. Slow personalized titration may prevent early ADRs (including syncope, myocarditis, and pneumonia). This guideline defines six personalized titration schedules for inpatients: 1) ancestry from Asia or the original people from the Americas with lower metabolism (obesity or valproate) needing minimum therapeutic dosages of 75–150 mg/day, 2) ancestry from Asia or the original people from the Americas with average metabolism needing 175–300 mg/day, 3) European/Western Asian ancestry with lower metabolism (obesity or valproate) needing 100–200 mg/day, 4) European/Western Asian ancestry with average metabolism needing 250–400 mg/day, 5) in the US with ancestries other than from Asia or the original people from the Americas with lower clozapine metabolism (obesity or valproate) needing 150–300 mg/day, and 6) in the US with ancestries other than from Asia or the original people from the Americas with average clozapine metabolism needing 300–600 mg/day. Baseline and weekly CRP monitoring for at least four weeks is required to identify any inflammation, including inflammation secondary to clozapine rapid titration.

Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

Background-To translate our knowledge about neuroanatomy of bipolar disorder (BD) into a diagnostic tool, it is necessary to identify the neural signature of predisposition for BD and separate it from effects of long-standing illness and treatment. Thus, we examined the associations among genetic risk, illness burden, lithium treatment, and brain structure in BD.