Background and objective Antipsychotic-associated metabolic abnormalities, including weight gain, dyslipidemia, hyperglycemia, obesity, and metabolic syndrome, vary among drugs. Receptor pharmacology offers mechanistic clues, while transcriptome-wide association studies identify genetically regulated expression signals linked to lipid traits. These evidence streams are usually evaluated separately. This secondary computational sensitivity analysis used previously generated Ki-DDD-TWAS outputs and was not a primary discovery or clinical validation study. The primary hypothesis was that removing literature-derived metabolic receptor weights would preserve overall drug ranking, with Spearman ρ approximately 0.9, while shifting receptor-level contributions from histaminergic and serotonergic systems toward dopaminergic and low-prior TWAS signals. Secondary objectives were to identify low-weight genes with strong TWAS signals, assess discordance with an author-defined clinical-liability comparator, and test sensitivity to gene removal and TWAS scaling. Methods Previously generated outputs were analyzed. Ki, the inhibition constant, and DDD, the defined daily dose, served as affinity and exposure-proxy inputs. Receptor-level and per-drug risk-score files were analyzed for 52 antipsychotics across LDL, HDL, log-transformed triglycerides, non-HDL, and total cholesterol. Analyses included reconstruction validation, pooled receptor contributions, TWAS signal versus literature weight, clinical-liability discordance, leave-one-gene-out and TWAS-scaling sensitivity, and receptor co-occurrence in high-risk drug sets. No patient-level data, independent TWAS resources, longitudinal outcomes, or outcome-based validation were used. Results Reconstruction reproduced saved risk ranks with Spearman correlation 1.000 for all 10 mode-trait combinations. V4 rankings remained concordant after removing literature weights, with trait-specific correlations of 0.900-0.923 and a mean of 0.913. The weighted model was dominated by HRH1, HTR2A, and HTR2C, contributing 25.7%, 21.6%, and 10.3% of pooled signal. Under uniform weighting, DRD2, DRD3, and HTR2A led, accounting for 22.1%, 12.9%, and 12.6%. ADRB1 was the strongest high-priority low-weight TWAS candidate, with an absolute HDL z-score of 13.53 and literature weight of 0.08. Other candidates included CHRM4, DRD2, DRD4, SLC6A4, and ADRB2. The model most strongly under-ranked paliperidone versus the supplied 12-drug comparator, while ziprasidone rose under uniform weighting. Removing HRH1 produced the largest rank shifts, particularly for levomepromazine, promazine, and acepromazine. Conclusions Removing literature-derived weights preserved overall drug ranking but substantially changed receptor-level score allocation. The weighted model emphasized histaminergic and serotonergic receptors prioritized in metabolic-liability literature, whereas the uniform model revealed broader dopaminergic and low-prior contributions. ADRB1 is a computational hypothesis-generating candidate, not a confirmed mechanism or established metabolic-risk contributor. HDL-association direction was not analyzed. The framework supports dual reporting of clinically anchored weighted and discovery-oriented uniform models as an internal sensitivity strategy only. It is not externally validated, does not predict patient-level outcomes, and should not be interpreted as estimating clinical metabolic risk or establishing receptor-level causality.

Atypical antipsychotics (AAPs)-induced QT interval prolongation represents a major clinical safety challenge. Traditional mechanisms based solely on hERG (IKr) channel blockade cannot fully explain the heterogeneity of clinical phenotypes and severe arrhythmias during overdose. This study systematically evaluated the effects of representative AAPs at therapeutic and toxic concentrations on cardiomyocyte electrophysiological activities. Using whole-cell patch-clamp techniques, we recorded action potentials and major transmembrane ionic currents (IKr, IKs, IK1, Ito, and INa) in primary adult mouse ventricular myocytes exposed to five AAPs (olanzapine, quetiapine, clozapine, ziprasidone, and risperidone) at a moderate therapeutic concentration. Dose-responses for olanzapine and quetiapine across low, medium, and high concentrations were also analyzed. At therapeutic concentrations, all the five AAPs significantly prolonged 90% action potential duration (APD90). Olanzapine, clozapine, and risperidone also prolonged APD50, which was unlike quetiapine and ziprasidone. Ion current analysis revealed quetiapine and ziprasidone significantly inhibited IKr, whereas olanzapine, clozapine, and risperidone suppressed Ito. Olanzapine and clozapine additionally reduced IK1 and INa. At supratherapeutic concentrations, olanzapine and quetiapine concentration-dependently prolonged APD90 and non-selectively inhibited all measured currents. In conclusion, at therapeutic concentrations, AAPs delay ventricular repolarization via distinct ion channel blockades. At supratherapeutic doses, the loss of channel selectivity and synergistic multi-channel blockade constitute the core mechanism for malignant arrhythmias during drug overdose, highlighting the need to consider drug-specific multi-channel profiles in future clinical cardiac safety evaluations.

Temozolomide (TMZ) resistance is a major challenge in glioblastoma (GBM). The role of BRCA1-associated RING domain protein 1 (BARD1), a DNA damage response protein, in GBM and its potential link to liquid-liquid phase separation (LLPS) remain unclear. Bioinformatics analysis of TCGA, CGGA, and GEO datasets revealed that high BARD1 expression correlates with poor prognosis in GBM. Functional studies demonstrated that BARD1 knockdown inhibited glioma cell proliferation, migration, and invasion. We discovered that BARD1 undergoes LLPS via its intrinsically disordered region (IDR, aa 113-425). Upon TMZ-induced DNA damage, BARD1 forms nuclear condensates that recruit X-ray repair cross-complementing protein 5 (XRCC5) to damage sites, promoting repair and driving TMZ resistance. Disrupting this phase separation capability impaired DNA repair. Through structure-based virtual screening, our results suggest that Ziprasidone and Apomorphine may disrupt the BARD1-XRCC5 interaction. Combining either compound with TMZ enhanced cytotoxicity in vitro and suppressed tumor growth in vivo. Our findings unveil a novel LLPS-mediated mechanism by which BARD1 confers TMZ resistance in GBM, positioning it as a prognostic marker and a therapeutic target. Targeting the BARD1-XRCC5 axis presents a promising strategy to overcome chemoresistance.

The selection of a "good" model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult-or even impossible-to quantify precisely. Multi-objective optimization (MOO) provides a systematic way to handle this challenge by explicitly incorporating and balancing both objective (measurable) and subjective (judgment-based) considerations when choosing among candidate solutions. The generated Pareto front represents a set of non-dominated models where no single solution can be improved in one objective without sacrificing the performance in another objective. Using the non-dominated sorting genetic algorithm II (NSGA-II), an implementation of MOO, we simultaneously considered objective function value and number of estimated parameters as competing criteria. Concentration measurements of 17-DMAG, quetiapine, clozapine and ziprasidone were applied to build population pharmacokinetic models through traditional stepwise search, machine learning based single-objective hybrid genetic algorithm (SOHGA) and MOO. Local downhill search with MOO was also assessed in this study. While both objectives improved, models with lower objective function value generally contained more estimated parameters. The number of non-dominated solutions for DMAG, ziprasidone, clozapine, and quetiapine was 17, 9, 9, and 13, respectively. The optimal model selected by SOHGA appeared on the Pareto front for DMAG, ziprasidone and clozapine datasets. Overall, MOO provides objective transparency to the cost of tradeoffs between competing model objectives, allowing researchers to better contextualize subjective criteria (e.g., biological plausibility, improvements in diagnostic plots) when aligning model selection with clinical context.

Schizophrenia is a chronic mental health condition that most commonly develops in the third decade of life, but may emerge during adolescence, although in rare cases it can also present in childhood. Pre-adult onset is associated with substantial morbidity, functional impairment, and long-term disability. Accurately diagnosing schizophrenia in youth is particularly challenging, as its symptoms frequently overlap with or are misattributed to other psychiatric or developmental conditions. Conversely, misdiagnosis can also occur when psychotic symptoms are present in patients suffering from other conditions. Establishing an accurate and timely diagnosis is therefore critical to prevent errors in treatment and to optimize prognosis and functional outcomes. Antipsychotic medications are first-line interventions for schizophrenia in youth, with second-generation antipsychotics (SGAs) having the strongest evidence base in this population. Randomized controlled trials and open-label studies support the efficacy of risperidone, aripiprazole, olanzapine, quetiapine, paliperidone, lurasidone, and brexpiprazole in reducing psychotic symptoms in adolescents. In contrast, ziprasidone and asenapine have not demonstrated clear benefit in treatment of schizophrenia in youth. Similar to studies in adults, evidence from pediatric clinical trials suggests that clozapine remains the most effective option for treatment-resistant adolescent schizophrenia, although its use requires close monitoring for hematologic and other adverse effects. While SGAs are generally favored over first-generation antipsychotics (FGAs) in youth because of their reduced propensity for causing extrapyramidal side effects as well as a stronger evidence base to support their use, their safety profiles require careful consideration. Weight gain, metabolic syndrome, sedation, extrapyramidal symptoms, and endocrine effects have been reported in youth with schizophrenia. Such side effects can significantly impact adherence and long-term physical health, necessitating the need for ongoing monitoring and thoughtful agent selection tailored to each patient's individual clinical profile and risk factors. Overall, current evidence supports SGAs as the foundation of psychopharmacologic treatment for schizophrenia in pediatric-aged patients, with clozapine reserved for refractory cases. However, the relative paucity of pediatric-specific data compared with adult populations underscores the importance of continued research to guide clinical practice. In the meantime, accurate diagnosis, early intervention, and vigilant monitoring of both therapeutic response and side effects remain essential to optimizing outcomes for youth with schizophrenia.