Pharmacopsychiatry最新文献

Rates of Hypoprolactinemia in Bipolar Patients Receiving Once-monthly Long-acting Injectable Aripiprazole. 服用每月一次长效注射阿立哌唑的双相患者低泌乳素血症的发生率。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-07 DOI: 10.1055/a-2861-6690

Fatma Simsek

{"title":"Rates of Hypoprolactinemia in Bipolar Patients Receiving Once-monthly Long-acting Injectable Aripiprazole.","authors":"Fatma Simsek","doi":"10.1055/a-2861-6690","DOIUrl":"https://doi.org/10.1055/a-2861-6690","url":null,"abstract":"Introduction: Hypoprolactinemia, an often overlooked condition in psychiatric practice, has been suggested to be associated with potential adverse effects on sexual and metabolic functions, similar to those reported in hyperprolactinemia. Recently, numerous studies have indicated that oral aripiprazole can lead to hypoprolactinemia in patients with various psychiatric disorders. However, the frequency and severity of hypoprolactinemia in bipolar patients receiving a 400-mg dose of once-monthly long-acting injectable aripiprazole remain unknown. This study aimed to assess the effects of once-monthly long-acting (400 mg) injectable aripiprazole on prolactin levels in patients with bipolar disorder.Methods: Fifty patients with bipolar disorder (25 women and 25 men) receiving the 400-mg dose of once-monthly long-acting injectable aripiprazole, either as monotherapy or in combination with other psychiatric medications, were recruited for this study.Results: Overall, the rate of hypoprolactinemia was 52% among the study participants. When stratified by sex, the prevalence was 68% in men and 36% in women. In addition, 28% of male patients and 4% of female patients had serum prolactin levels below 1 ng/mL. Mean serum prolactin levels were significantly higher in women than in men (z=- 3.435, p=0.001). No significant correlation was observed between serum prolactin concentrations and age or treatment duration in either sex.Discussion: Once-monthly injectable aripiprazole treatment in bipolar disorder was associated with a relatively high prevalence of hypoprolactinemia in both female and male patients. Given emerging evidence of potential metabolic and sexual effects of hypoprolactinemia, monitoring prolactin levels may be clinically relevant in patients receiving injectable aripiprazole.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147841334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Be Aware of AI Limitations. 注意AI的局限性。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2026-01-09 DOI: 10.1055/a-2743-2564

Scott Monteith, Tasha Glenn, John Richard Geddes, Peter C Whybrow, Eric Achtyes, Rita Bauer, Michael Bauer

引用次数: 0

Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision. 人工智能在药物发现和开发中的应用：提高决策质量。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2026-03-05 DOI: 10.1055/a-2810-8972

Shota Furukawa, Hiroyuki Uchida, Taishiro Kishimoto

{"title":"Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision.","authors":"Shota Furukawa, Hiroyuki Uchida, Taishiro Kishimoto","doi":"10.1055/a-2810-8972","DOIUrl":"10.1055/a-2810-8972","url":null,"abstract":"Abstract: 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.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":"103-116"},"PeriodicalIF":2.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147366263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing Machine Learning to Forecast 3-Month Remission Outcomes in Bipolar Disorder Patients Treated with Lithium. 利用机器学习预测锂治疗双相情感障碍患者3个月缓解结果

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI: 10.1055/a-2751-7163

Alessandro Cuomo, Despoina Koukouna, Simone Pardossi, Mario Pinzi, Caterina Pierini, Maria Beatrice Rescalli, Andrea Fagiolini

{"title":"Utilizing Machine Learning to Forecast 3-Month Remission Outcomes in Bipolar Disorder Patients Treated with Lithium.","authors":"Alessandro Cuomo, Despoina Koukouna, Simone Pardossi, Mario Pinzi, Caterina Pierini, Maria Beatrice Rescalli, Andrea Fagiolini","doi":"10.1055/a-2751-7163","DOIUrl":"10.1055/a-2751-7163","url":null,"abstract":"Introduction: Lithium remains a first-line mood stabilizer for bipolar disorder; yet, only a subset of patients achieves symptomatic remission. Early prediction of treatment response could guide personalized management. In this study, we leveraged machine learning algorithms to predict 3-month remission, defined as a Montgomery-Åsberg Depression Rating Scale score≤10, in 593 patients with bipolar disorder initiating lithium.Methods: In this retrospective cohort, baseline sociodemographic, clinical and laboratory data as well as concomitant medication usage were collected. Montgomery Åsberg Depression Rating Scale and Mania Rating Scale were administered at baseline and 3 months. Data were preprocessed (missing imputation and normalization) and then split into 80% training and 20% test sets. We evaluated various machine learning techniques such as random forest, XGBoost, neural network and support vector machines with five-fold cross validation. Performance metrics included area under the receiver operating characteristic curve and accuracy.Results: The mean age was 44±16.9 years and 53% of participants were females. The remission rate at 3 months was 44%. The random forest model (augmented by polynomial transformations) performed best (area under the receiver operating characteristic curve=0.76 and accuracy=0.64) improving by 10% of the standard logistic model. Key predictors included the baseline Montgomery Åsberg Depression Rating Scale and Mania Rating Scale, creatinine, thyroid-stimulating hormone levels, body mass index and age.Discussion: Machine learning, particularly gradient boosted trees, can help to predict the 3-month remission in bipolar disorder patients who start lithium therapy. Incorporating clinical and laboratory features enhances the early identification of likely responders, enabling personalized treatment strategies.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":"129-136"},"PeriodicalIF":2.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145793810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health. 阻止反思：数字双胞胎在心理健康方面的里程碑和障碍。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2026-03-19 DOI: 10.1055/a-2816-2869

Falk Gerrik Verhees, Isabella Catharina Wiest, Jakob Nikolas Kather, Joseph Kambeitz, Pavol Mikolas

{"title":"Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.","authors":"Falk Gerrik Verhees, Isabella Catharina Wiest, Jakob Nikolas Kather, Joseph Kambeitz, Pavol Mikolas","doi":"10.1055/a-2816-2869","DOIUrl":"10.1055/a-2816-2869","url":null,"abstract":"Abstract: 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.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":"117-125"},"PeriodicalIF":2.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147486997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble Machine Learning Model for Real-Time Valproic Acid Prediction in Epilepsy Treatment. 癫痫治疗中丙戊酸实时预测的集成机器学习模型。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2025-06-02 DOI: 10.1055/a-2593-3125

Jiangchuan Xie, Pan Ma, Xinmei Pan, Liya Cao, Ruixiang Liu, Lirong Xiong, Hongqian Wang, Xin Zhang, Linli Xie, Yongchuan Chen

{"title":"Ensemble Machine Learning Model for Real-Time Valproic Acid Prediction in Epilepsy Treatment.","authors":"Jiangchuan Xie, Pan Ma, Xinmei Pan, Liya Cao, Ruixiang Liu, Lirong Xiong, Hongqian Wang, Xin Zhang, Linli Xie, Yongchuan Chen","doi":"10.1055/a-2593-3125","DOIUrl":"10.1055/a-2593-3125","url":null,"abstract":"Aims: To develop an optimal model to predict valproic acid (VPA) concentrations by machine learning, ensuring that the VPA plasma concentration is in the effective treatment range, and thus effectively control the patient's epilepsy.Methods: This single-center, retrospective study included patients diagnosed with epilepsy from January 2014 to January 2022. Patients receiving VPA and having undergone therapeutic drug monitoring were enrolled. Top three algorithms exhibiting superior model performance were selected to establish the ensemble prediction model, with Shapley Additive exPlanations (SHAP) employed for model interpretation. An independent dataset was collected as a clinical validation group to verify the prediction model performance.Results: The algorithms chosen for the ensemble model-Light Gradient Boosting, Categorical Boosting, and Gradient Boosted Regression Trees-demonstrated high R 2 (0.549, 0.515, and 0.503, respectively). Post-feature selection, the final model incorporated 20 variables, proving superior in predictive performance compared to models considering all 24 variables. The R 2 , mean absolute error, mean square error, absolute accuracy (±20 mg/L), and relative accuracy (±20%) of external validation were 0.621, 10.67, 221.50, 78.98%, and 66.48%, respectively. The importance and direction of each variable were visually represented using SHAP values, with VPA administration and liver function emerging as the most significant factors.Conclusion: The innovative application harnesses advanced multi-algorithm mining methodologies to forecast VPA concentrations in adult epileptic patients. Furthermore, it employs SHAP to elucidate the nuanced influence of each feature within the integrated prediction model, thereby providing a robust and plausible explanation for the determinants affecting VPA concentration predictions.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":"145-156"},"PeriodicalIF":2.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Robots in Psychiatry for Patients Suffering from Major Depression-A Pilot Study. 重度抑郁症患者精神病学中的社交机器人——一项试点研究。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2026-02-06 DOI: 10.1055/a-2792-9006

Paraskevi Mavrogiorgou, Robert Heymel, Luisa Richter, Fabian Rahman, Benedikt Hoffmann, Georg Juckel

{"title":"Social Robots in Psychiatry for Patients Suffering from Major Depression-A Pilot Study.","authors":"Paraskevi Mavrogiorgou, Robert Heymel, Luisa Richter, Fabian Rahman, Benedikt Hoffmann, Georg Juckel","doi":"10.1055/a-2792-9006","DOIUrl":"10.1055/a-2792-9006","url":null,"abstract":"Introduction: Since the coronavirus pandemic in 2019, interest in implementing and establishing digital media, for example, in the form of humanoid social robots, in everyday psychiatric and psychotherapeutic practice has grown enormously.Methods: Thirty patients with depressive disorders (24 men and 6 women, mean age [standard deviation]: 39.1 [14.4] and 30 healthy subjects (14 men and 16 women, mean age [standard deviation]: 33.1 [14.2]) were videotaped in a standardised experimental setting while interacting with the robot \"Pepper\" and completed questionnaires about their attitudes and experiences with it for comparative analysis.Results: There was no significant difference between the depressed patients and the healthy subjects in their individual statements on the robot's user-friendliness (system usability scale). Overall, patients reported that they could imagine using Pepper more, even though they did not find it easy to use, understand or master in practical applications, but they felt safer in its presence than healthy subjects. There were significant differences in the statements that the depressed patients attributed less human-like attributes to the robot and considered it less of an interesting enrichment in life in general.Discussion: Some initial studies and our findings on experiences and attitudes towards the use of a social robot suggest that depressed patients do not differ significantly from healthy subjects in their assessment of the robot's user-friendliness or in their critical but favourable attitude towards it. Despite all skepticism, also in Germany, patients with depressive disorders appear to be receptive to robot applications, so further clinical expansion of the robot use is recommended.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":"137-144"},"PeriodicalIF":2.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Great Expectations, Limited Evidence: The Emerging Role of Artificial Intelligence in Pharmacopsychiatry. 期望远大，证据有限：人工智能在药物精神病学中的新兴作用。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2026-05-08 DOI: 10.1055/a-2831-1165

Falk Gerrik Verhees, Michael Bauer

引用次数: 0

Patient and Physician Exposure to Artificial Intelligence Hype. 病人和医生接触人工智能炒作。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-05-01 Epub Date: 2025-05-12 DOI: 10.1055/a-2577-7214

Scott Monteith, Tasha Glenn, John R Geddes, Peter C Whybrow, Eric D Achtyes, Rita Bauer, Michael Bauer

引用次数: 0

Clinical and Neurobiological Findings in a Patient With Unipolar Depressive 48-hour-ultrarapid-cycling. 1例单相抑郁患者48小时超快循环的临床和神经生物学表现。

IF 2.2 3区医学

Pharmacopsychiatry Pub Date : 2026-04-29 DOI: 10.1055/a-2858-0331

Georg Juckel, Kai Wetzel, Paraskevi Mavrogiorgou

{"title":"Clinical and Neurobiological Findings in a Patient With Unipolar Depressive 48-hour-ultrarapid-cycling.","authors":"Georg Juckel, Kai Wetzel, Paraskevi Mavrogiorgou","doi":"10.1055/a-2858-0331","DOIUrl":"https://doi.org/10.1055/a-2858-0331","url":null,"abstract":"Objective: The rare cases of patients with 48-hour bipolar or unipolar ultra-rapid-cycling allow a more precise understanding of mood cycles in affective disorders, as the rhythmic changes in the psychopathological state and biological parameters are quite precise.Methods: A 73-year-old male patient with several years of recurrent affective-depressive disorder (International Classification of Diseases, 10th Revision: F33.1) developed unipolar 48-hour-ultra-rapid-cycling with 1 day of severe depression up to acute suicidality and 1 day of euthymia (International Classification of Diseases, 10th Revision: F31.8) at the beginning of 2024.Results: Ultra-rapid-cycling could be objectified both psychopathologically (Beck Depression Inventory and visual analogue scale) and neurobiologically (especially serotonin using loudness dependence of auditory evoked potentials) at the end of the year. The patient responded promptly to the subsequent adjustment to 900 mg daily lithium and has been symptom-free and with-out a 48-hour rhythm ever since.Conclusions: Unipolar depressive 48-hour-ultra-rapid-cycling of marked mood and drive fluctuations is also associated with characteristic biological changes, and that lithium represents a successful treatment strategy here in contrast to \"normal\" rapid-cycling.","PeriodicalId":19783,"journal":{"name":"Pharmacopsychiatry","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147778035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0