How precise is precision medicine for schizophrenia?

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

PsyCoP - A Platform for Systematic Semi-Automated Behavioral and Cognitive Profiling Reveals Gene and Environment Dependent Impairments of Tcf4 Transgenic Mice Subjected to Social Defeat

Recently, hundreds of risk genes associated with psychiatric disorders have been identified. These are thought to interact with environmental stress factors in precipitating pathological behaviors. However, the individual phenotypes resulting from specific genotype by environment (G×E) interactions remain to be determined. Toward a more systematic approach, we developed a novel standardized and partially automatized platform for systematic behavioral and cognitive profiling (PsyCoP). Here, we assessed the behavioral and cognitive disturbances in Tcf4 transgenic mice (Tcf4tg) exposed to psychosocial stress by social defeat during adolescence using a "two-hit" G×E mouse model. Notably, TCF4 has been repeatedly identified as a candidate risk gene for different psychiatric diseases and Tcf4tg mice display behavioral endophenotypes such as fear memory impairment and hyperactivity. We use the Research Domain Criteria (RDoC) concept as framework to categorize phenotyping results in a translational approach. We propose two methods of dimension reduction, clustering, and visualization of behavioral phenotypes to retain statistical power and clarity of the overview. Taken together, our results reveal that sensorimotor gating is disturbed by Tcf4 overexpression whereas both negative and positive valence systems are primarily influenced by psychosocial stress. Moreover, we confirm previous reports showing that deficits in the cognitive domain are largely dependent on the interaction between Tcf4 and psychosocial stress. We recommend that the standardized analysis and visualization strategies described here should be applied to other two-hit mouse models of psychiatric diseases and anticipate that this will help directing future preclinical treatment trials.

Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)

Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.

Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates

Schizophrenia is a severe neuropsychiatric disorder that affects approximately 0.5–1% of the population. Response to antipsychotic therapy is highly variable, and it is not currently possible to predict those patients who will or will not respond to antipsychotic medication. Furthermore, a high percentage of patients, approximately 30%, are classified as treatment-resistant (treatment-resistant schizophrenia; TRS). TRS is defined as a non-response to at least two trials of antipsychotic medication of adequate dose and duration. These patients are usually treated with clozapine, the only evidence-based pharmacotherapy for TRS. However, clozapine is associated with severe adverse events. For these reasons, there is an increasing interest to identify better targets for drug development of new compounds and to establish better biomarkers for existing medications. The ability of antipsychotics to improve psychotic symptoms is dependent on their antagonist and reverse agonist activities at different neuroreceptors, and some genetic association studies of TRS have focused on different pharmacodynamic factors. Some genetic studies have shown an association between antipsychotic response or TRS and neurodevelopment candidate genes, antipsychotic mechanisms of action (such as dopaminergic, serotonergic, GABAergic, and glutamatergic) or pharmacokinetic factors (i.e., differences in the cytochrome families). Moreover, there is a growing body of literature on the structural and functional neuroimaging research into TRS. Neuroimaging studies can help to uncover the underlying neurobiological reasons for such resistance and identify resistant patients earlier. Studies examining the neuropharmacological mechanisms of antipsychotics, including clozapine, can help to improve our knowledge of their action on the central nervous system, with further implications for the discovery of biomarkers and the development of new treatments. The identification of the underlying mechanisms of TRS is a major challenge for developing personalized medicine in the psychiatric field for schizophrenia treatment. The main goal of precision medicine is to use genetic and brain-imaging information to improve the safety, effectiveness, and health outcomes of patients via more efficiently targeted risk stratification, prevention, and tailored medication and treatment management approaches. The aim of this review is to summarize the state of art of pharmacogenetic, pharmacogenomic and neuroimaging studies in TRS.

Comparison between long-acting injectable aripiprazole versus paliperidone palmitate in the treatment of schizophrenia: systematic review and indirect treatment comparison

We investigated the relative efficacy and tolerability of aripiprazole once monthly (AOM) versus paliperidone palmitate (PP) for treating schizophrenia. Extensive databases searches on short-term, placebo-controlled, randomized studies of AOM and PP were performed. Indirect treatment comparisons were performed between the two long-acting injectable antipsychotics (LAIAs). The primary efficacy endpoint was the mean change in the Positive and Negative Syndrome Scale total score from baseline between each LAIA and placebo. The effect sizes were mean differences and odds ratio (ORs) with 95% confidence intervals (CIs) for the primary efficacy endpoint and safety/tolerability between two LAIAs, respectively. Mean difference in the primary efficacy endpoint was significantly different, favouring AOM over PP (OR: -6.4; 95% CI: -11.402 to -1.358); sensitivity analyses and noninferiority test (AOM vs. PP) confirmed the primary results. The overall early dropout rate was not significantly different between AOM and PP (OR: 1.223; 95% CI: 0.737-2.03). However, there was a significant difference in the early dropout rate in terms of lack of efficacy favouring AOM over PP (OR: 0.394; 95% CI: 0.185-0.841). Within the context of the inherent limitations of the current analysis, our results may suggest that there may be relative advantages for AOM over PP in the short-term treatment of schizophrenia.

The Roots of Chronic Posttraumatic Stress Disorder: Childhood Trauma, Information Processing, and Self-protective Strategies

BACKGROUND

Although childhood endangerment often precedes adult posttraumatic stress disorder (PTSD), the mechanism from danger to disorder is unclear. We proposed a developmental process in which unprotected and uncomforted danger in childhood would be associated with "shortcuts" in information processing that, in adulthood, could result in PTSD if the adult experienced additional exposure to danger. Information processing was defined as the basic associative, dissociative, and integrative processes used by all humans. Individual differences in parents' (or primary caregivers') protective and comforting behavior were expected to force unprotected children to use psychological shortcuts that linked early trauma to later vulnerability for PTSD.

METHOD

We compared 22 adults with chronic PTSD to (a) 22 adults with other psychiatric diagnoses and (b) 22 normative adults without any diagnosis, in terms of information processing around childhood danger. The Adult Attachment Interview was used to derive information processing variables, including self-protective strategies, childhood traumas, and depression.

RESULTS

The two patient groups differed from the normative group on all variables. Adults with chronic PTSD differed from other psychiatric patients in having more childhood traumas and using more transformations of associative and dissociative processes. Within the PTSD group, there were three psychologically different subgroups.

CONCLUSION

Our findings suggest that (1) prediction of risk for adult PTSD may be possible, (2) treatment might be facilitated by provision of a protective and supportive therapist, (3) who included a focus on correction of information processing errors and use of more adaptive strategies, and (4) subgroups of adults with PTSD may require different forms of treatment.