New pharmacotherapies to tackle the unmet needs in bipolar disorder: a focus on acute suicidality.

INTRODUCTION: Suicidal behavior is relatively frequent in patients with bipolar disorder (BD) and constitutes their most frequent cause of death. Suicide rates remain high in patients with BD despite adherence to guidelines recommending lithium as first line, and/or antidepressants, antipsychotics, psychotherapy, psychosocial interventions, and electroconvulsive therapy. Hence the need to identify more effective and rapid anti-suicide interventions. AREAS COVERED: To tackle the unmet needs of pharmacotherapy, we investigated the PubMed database on 24-25 January 2024 using strategies like ('acute suicid*'[ti] OR 'suicide crisis syndrome' OR 'acute suicidal affective disturbance') AND (lithium[ti] OR clozapine[ti]), which obtained 3 results, and ('acute suicid*'[ti] OR 'suicide crisis syndrome' OR 'acute suicidal affective disturbance') AND (ketamine[ti] OR esketamine[ti] OR NMDA[ti] OR glutamat*[ti]), which yielded 14 results. We explored glutamatergic abnormalities in BD and suicide and found alterations in both. The noncompetitive NMDS antagonist ketamine and its S-enantiomer esketamine reportedly decrease acute suicidality. EXPERT OPINION: Intranasal esketamine or subcutaneous ketamine, single-bolus or intravenous, and possibly other glutamate receptor modulators may improve suicidal behavior in patients with unipolar and bipolar depression. This may be achieved through prompt remodulation of glutamate activity. The correct use of glutamatergic modulators could reduce acute suicidality and mortality in patients with BD.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

DRD2, DRD3, and HTR2A Single-Nucleotide Polymorphisms Involvement in High Treatment Resistance to Atypical Antipsychotic Drugs.

BACKGROUND: The objective of this study was to investigate the DRD2 rs1800497, rs1799732, rs1801028, DRD3 rs6280, and HTR2A rs6314, rs7997012, and rs6311 single-nucleotide polymorphism (SNP) correlations with resistance to second-generation antipsychotics (SGAs) in a real-world sample of patients with treatment-resistant mental disorders. METHODS: We divided 129 participants into a high treatment resistance (HTR) group (current treatment with two SGAs, or clozapine, or classic neuroleptics for a failure of previous SGAs trials) and a low treatment resistance (LTR) group (current treatment with one atypical antipsychotic). We used Next-Generation Sequencing on DNA isolated from peripheral blood samples to analyze the polymorphisms. We performed logistic regression to search for predictors of HTR membership. RESULTS: A diagnosis of schizophrenia significantly predicted the HTR membership compared to other diagnoses. Other predictors were the DRD3 rs6280 C|T (OR = 22.195) and T|T (OR = 18.47) vs. C|C, HTR2A rs7997012 A|G vs. A|A (OR = 6.859) and vs. G|G (OR = 2.879), and DRD2 rs1799732 I|I vs. D|I (OR = 12.079) genotypes. CONCLUSIONS: A diagnosis of schizophrenia and the DRD2 rs1799732, DRD3 rs6280, and HTR2A rs7997012 genotypes can predict high treatment resistance to SGAs.

Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies.

Autism spectrum disorders (ASD) are neurodevelopmental disorders correlated to various neuroanatomical modifications. We aimed to identify neuroanatomical changes assessed in magnetic resonance imaging (MRI) studies of autism spectrum disorder (ASD) through Activation Likelihood Estimate (ALE) meta-analysis. We included 19 peer-reviewed magnetic resonance imaging (MRI) studies that analyzed cortical volume in patients with ASD compared to healthy control subjects (HCs). The between-group analyses comparing subjects with ASD to HCs showed a volumetric reduction of a large cluster in the right brain, including the uncus/amygdala, parahippocampal gyrus, and entorhinal cortex, and putamen. The anomalies are primarily found in the right hemisphere, involved in social cognitive function, particularly impaired in ASD. These results correlate with several clinical aspects of ASD. These volumetric alterations can be considered a major correlate of disease in the context of multifactorial etiology. Further studies on brain lateralization in ASD are needed, considering the clinical phenotype variability of these disorders.