Pharmacopsychiatry最新文献

Artificial intelligence in mental health has emerged as a potent tool to foster precision psychiatry, for example, by stratifying patient populations. A potential step forward would be mental health digital twins-the independent in-silico reconstruction of an individual person within their functional social and environmental systems that continuously incorporate all known and available subject parameters to predict patient trajectories including the outcomes of interventions. Generative artificial intelligence in the form of large language models demonstrated the ability to mimic human responses and integrate diverse sources of information that may foster the development of digital twins. We give a brief historical perspective on concepts and milestones of artificial intelligence in mental health and outline the current state of clinical decision support systems, monitoring and therapy applications based on artificial intelligence. We describe their integration in large behavioral models as a recently met precondition for digital twins and contrast this development with the magnificent hurdles that remain to truly realize clinical benefits of digital twins, from data quality and regulatory compliance to user engagement and public trust, for some of which we propose mitigation strategies here.

Drug research and development continuously encounters prolonged timelines, escalating costs, and high attrition rates. In this narrative review, we integrated recent advances in artificial intelligence across target identification, drug repurposing, de novo molecular design, structural biology, safety prediction, and artificial intelligence-supported clinical development, aligning these innovations with evolving global regulatory frameworks. Predictive and interpretable artificial intelligence could enhance the quality of decision-making throughout the research and development process when combined with causal or mechanistic priors, synthesis-aware and physics-informed molecular design, external validation with clear applicability domains, and governance systems aligned with multiple regulatory guidelines and qualified digital endpoint applications. Case studies of artificial intelligence-assisted discovery and repurposing demonstrate shorter development timelines, improved compound quality, and higher-level early-phase success, while underscoring challenges such as overfitting, model generalizability, and dataset bias. Establishing a context-of-use-based "credibility plan" and adopting equity-by-design through the inclusion of non-European datasets and subgroup performance evaluation are essential for achieving generalizable impact. Artificial intelligence integration with new approach methodologies and adaptive or covariate-adjusted clinical trials may help reduce development inefficiency without compromising scientific or ethical rigor.

The increasing use of antidepressant combinations necessitates a deeper understanding of drug-drug interactions. This study investigated serum concentration-dependent CYP2D6 inhibition by bupropion (via hydroxybupropion) and doxepin at antidepressant doses, using venlafaxine and risperidone as victim drugs.Therapeutic drug monitoring data from inpatients at the University Hospital of Würzburg (2018-2023) were retrospectively analyzed across four interaction groups (hydroxybupropion-venlafaxine, doxepin-venlafaxine, doxepin-risperidone, and hydroxybupropion-risperidone) with controls lacking CYP2D6 inhibitors. Associations between inhibitor concentrations and victim drug levels were assessed linearly. Nonlinear regression (four-parameter logistic model) was applied to relate inhibitor concentrations to metabolite-to-parent ratios; when the model fit was adequate, the half-maximal inhibitory concentration/90% inhibitory concentration values were estimated. Receiver operating characteristic analysis identified inhibitor concentration thresholds for phenoconversion to poor metabolizer status.Hydroxybupropion and doxepin concentrations were positively associated with venlafaxine, risperidone, and venlafaxine active moiety levels (p ≤ 0.02). Adequate sigmoidal curve fitting demonstrated a concentration-dependent inverse association between inhibitor levels and metabolite-to-parent ratios for venlafaxine with hydroxybupropion and doxepin, and risperidone with hydroxybupropion, but not risperidone with doxepin. The half-maximal inhibitory concentration values were 120.4 ng/mL and 28.16 ng/mL for hydroxybupropion (venlafaxine and risperidone, respectively) and 24.4 ng/mL for doxepin (venlafaxine). Phenoconversion thresholds were identified for hydroxybupropion (venlafaxine: 328.5 ng/mL and risperidone: 110.5 ng/mL) and doxepin (venlafaxine: 35 ng/mL and risperidone: 70.5 ng/mL).Hydroxybupropion and doxepin exert serum concentration-dependent inhibition of CYP2D6 activity, significantly affecting the metabolism of venlafaxine and risperidone. These effects and a phenoconversion into poor metabolizer status were detectable at subtherapeutic hydroxybupropion and therapeutic antidepressant doxepin levels. Monitoring inhibitor and victim-drug concentrations may aid in managing clinically relevant CYP2D6-mediated interactions.

Bipolar disorder is a chronic psychiatric condition that requires long-term treatment. Lithium remains the gold standard for mood stabilization; yet, its therapeutic response is highly variable and unpredictable due to the lack of reliable biomarkers. As lithium may exert its effect through interactions with neuronal membranes, particularly their lipid composition, red blood cell membranes have been proposed as a peripheral model to investigate this relationship. We conducted a monocentric, cross-sectional study involving 30 patients with bipolar disorder receiving a stable dose of lithium. Clinical and sociodemographic characteristics were assessed alongside plasma lithium and red blood cell lithium levels. Fatty acid profiles of red blood cell membranes were analyzed. The cohort was divided into three groups based on the red blood cell lithium/plasma lithium ratio to explore potential associations with fatty acid profiles. Additionally, an unsupervised clustering approach was used to identify patient subgroups based on fatty acid profiles and clinical characteristics, and their lithium levels were compared. No significant differences in fatty acid composition were found across the red blood cell lithium/plasma lithium ratio groups. However, older age was associated with higher red blood cell lithium/plasma lithium ratios. No clear association was found between fatty acid concentrations and intracellular lithium. Cluster analysis based on clinical data revealed two clinical subgroups, with the less severe group exhibiting significantly higher plasma lithium and higher omega-6 fatty acid levels. While no direct relationship was observed between fatty acid composition and lithium distribution, this study suggests that the lithium's action may involve other membrane components. Future longitudinal studies with larger samples and advanced lipidomic profiling are needed to identify potential composite biomarkers of lithium response.

Serotonergic psychedelics, such as lysergic acid diethylamide, and psilocybin, and the entactogen 3,4-methylenedioxymethamphetamine exhibit agonist activity at the 5-hydroxytryptamine 2B receptor, a signalling pathway known to mechanistically mediate drug-induced valvular heart disease. This systematic review evaluates whether chronic or repeated use of psychedelics and 3,4-methylenedioxymethamphetamine may contribute to valvular heart disease through sustained 5-hydroxytryptamine 2B receptor activation. A systematic search of Google Scholar, OVID and PubMed was conducted from inception to June 1, 2025. We sought to include studies that reported an association between psychedelics or 3,4-methylenedioxymethamphetamine at 5-hydroxytryptamine 2B binding and molecular, cellular, structural, and/or functional evidence of cardiac valvulopathy. Seventeen studies were included in this review. No studies were found on psilocybin, dimethyltryptamine or mescaline. Both lysergic acid diethylamide and 3,4-methylenedioxymethamphetamine have high affinity for 5-hydroxytryptamine 2B receptors and promote downstream signaling linked to mitogenic and fibrotic changes in valvular tissue. In vitro and structural studies show that lysergic acid diethylamide exhibits high affinity and induces β-arrestin-biased signaling in valvular interstitial cells, while 3,4-methylenedioxymethamphetamine displays moderate affinity and similar functional responses. In vivo studies confirm serotonin-induced valvulopathy, and chronic 3,4-methylenedioxymethamphetamine use has been associated with valvular abnormalities in humans. No clinical cases of lysergic acid diethylamide-induced valvulopathy have been reported, but preclinical data support its potential to engage fibrotic signaling pathways under sustained exposure. Preliminary converging mechanistic and preclinical evidence suggests that lysergic acid diethylamide and 3,4-methylenedioxymethamphetamine may promote cardiac valvulopathy via 5-hydroxytryptamine 2B receptor signalling. This is consistent with existing Food and Drug Administration concerns and supports the need for ongoing cardiac and valvular safety monitoring in psychedelic research.

Fear and anxiety perform essential protective roles, yet when they become dysregulated, they can trap trauma survivors in persistent hypervigilance and distress. Post-traumatic stress disorder (PTSD) manifests as intrusive memories, avoidance, and heightened arousal long after the precipitating event. Although current pharmacotherapies - including selective serotonin reuptake inhibitors, adrenergic blockers, benzodiazepines, and atypical antipsychotics - provide relief for some, many patients contend with residual symptoms or intolerable adverse effects. Recent discoveries position the endocannabinoid system as a pivotal regulator of fear acquisition, consolidation, and extinction. Clinical observations of altered anandamide levels and cannabinoid receptor CB₁ upregulation in individuals with severe PTSD underscore the therapeutic potential of restoring endocannabinoid tone. Preclinical studies demonstrate that direct CB₁ agonists, fatty acid amide hydrolase (FAAH) inhibitors, and phytocannabinoids such as tetrahydrocannabinol (THC) and cannabidiol (CBD) can facilitate extinction learning and attenuate anxiety-like behaviours. Preliminary human trials report that nabilone alleviates trauma-related nightmares and that acute cannabinoid administration modulates amygdala reactivity to a threat. Yet optimal dosing strategies, sex-specific responses, and ideal THC:CBD ratios remain to be defined. Self-medication with cannabis can offer transient relief but carries a risk of cannabis use disorder and potential worsening of PTSD symptoms. By elucidating molecular targets - including CB₁, CB₂, FAAH, and monoacylglycerol lipase - this review outlines a strategic framework for next-generation cannabinoid-based interventions. Harnessing the endocannabinoid system promises to expand the therapeutic arsenal for PTSD, offering hope for more effective and better-tolerated treatments.

Polypharmacy has an important role in psychiatry, as the kidney function can be affected by medication. Risperidone is metabolized hepatically to 9-hydroxyrisperidone and excreted renally. Here, we study how serum concentrations of risperidone, 9-hydroxyrisperidone and active moiety (risperidone+9-hydroxyrisperidone) are related to impairment of kidney function and potentially interacting comedications that affect renal functions ((1) nonsteroidal anti-inflammatory drugs, (2) angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, and (3) diuretics). In this retrospective study, data from risperidone-treated inpatients (2015-2020, n=517) at the University Hospital of Würzburg were analyzed. Routine therapeutic drug monitoring was performed at trough levels at a steady-state. Groups were compared by means of the Kruskal-Wallis test. To correct for confounding parameters, additional multiple linear regression modeling was performed. After correction for age, sex, body mass index and the respective interaction between the estimated glomerular filtration rate and the number of interacting drugs, the dose-corrected serum concentration of 9-hydroxyrisperidone and the active moiety of risperidone were positively associated with the number of interacting drugs. The active moiety was negatively associated with the estimated glomerular filtration rate. Our data suggest that renal functions and the number of interacting drugs influence the pharmacokinetics of risperidone. Previous studies often explained the increasing serum concentration with age as a surrogate, whereas our results suggest that the kidney function and comedication affecting kidney function might be more relevant. When prescribing risperidone, especially in patients with renal impairment or co-medicated with interacting drugs, we suggest to start with lower starting doses and recommend monitoring serum concentrations to prevent overdosing.

Depression is a mental disorder characterized by high incidence and relapse rates, and the search for effective treatments has been continuously pursued. To assess the effectiveness of existing studies, recent research advances are summarized with an emphasis on potential pathophysiological mechanisms through specific pathways of intestinal flora affecting depression, including microbial and metabolic activities, neuroendocrine regulation, immune and inflammatory responses, and microbial dysbiosis. This review also presents current therapeutic strategies targeting intestinal flora in depression and provides potential directions for future research.

Electrocardiographic changes indicating a significantly increased risk of adverse cardiac effects, such as heart-rate corrected QT interval prolongation, are rare during antidepressant treatment; however, systematic analyses are lacking. Therefore, our study aimed at investigating the relationship between heart-rate corrected QT interval changes and both dosage and serum levels of escitalopram and venlafaxine in a well-characterized study sample.Two hundred seven depressed patients received escitalopram (10-20 mg/d) for 4 weeks. Non-responders were switched to high-dose venlafaxine (150-375 mg/d) for an additional 4 weeks of treatment. Serum concentrations were measured weekly and electrocardiograms were recorded at baseline, day 28 and day 56. Risk factors for heart-rate corrected QT interval prolongation were included as covariates in the analysis.Escitalopram did not significantly affect heart-rate corrected QT intervals. Switching to high-dose venlafaxine resulted in a significant increase of mean QT intervals from day 29 to day 56 (p=0.007), more pronounced in men (interaction p=0.038). Heart-rate corrected QT interval prolongation occurred in 5% of patients after escitalopram and 12% after venlafaxine; notably, 12 patients experienced critical prolongations, all with higher rates of known risk factors. No correlation between serum concentrations and heart-rate corrected QT intervals were observed.Our findings indicate that escitalopram does not affect heart-rate corrected QT intervals, whereas venlafaxine is associated with a modest but significant heart-rate corrected QT interval prolongation, particularly in males. Although infrequent, critical prolongations highlight the need for individualized risk assessment, especially in patients with risk factors. The lack of correlation with serum concentrations suggests that pharmacokinetic monitoring alone may not reliably predict cardiac risk. Our results underscore the importance of electrocardiogram monitoring with attention to sex-specific and clinical risk profiles.

As accumulating evidence suggests the antisuicidal properties of ketamine, elucidating its underlying mechanisms and predictors of treatment response has become crucial. Whether a combination of clinical markers can enhance the prediction of the antisuicidal response to ketamine remains unclear.Using data from our previous randomized placebo-controlled and open-label trials, this post hoc analysis evaluated 67 patients with treatment-resistant depression and prominent suicidal ideation who received a single ketamine infusion of 0.5 mg/kg. A classification and regression tree model was used to identify the combinations of clinical and demographic characteristics at baseline that can predict the antisuicidal response to ketamine infusion.Combinations of clinical predictors, including mild or moderate depression severity, a shorter duration of the current episode, no more than four trials of antidepressant failures, low or moderate current suicide risks, and a history of suicide attempts, offered superior predictive power for the rapid and sustained antisuicidal effects of sub-anesthetic ketamine infusion, outperforming the predictive power of any individual predictor.Clinicians can use our findings to identify individuals who are most likely to benefit from the antisuicidal effects of ketamine. Further research is required to corroborate these findings.CART found combined baseline markers, including mild/moderate depression, shorter episode, ≤4 failed antidepressants, low/moderate suicide risk, and prior attempts, predict rapid/sustained antisuicidal response of sub-anesthetic ketamine in TRD.