Aims: Olanzapine, a second-generation antipsychotic, can cause dose-adverse drug reactions. Its steady-state concentration can vary due to several factors. This study explores how physiological factors, smoking, inflammation, concomitant medications, and metabolic enzyme single nucleotide polymorphisms (SNPs) influence its metabolic levels, aiming to guide personalized dosing. Methods: This study analyzed data from 310 olanzapine-treated patients at Beijing Anding Hospital. Liquid chromatography-mass spectrometry (LC-MS/MS) quantified 1002 serum concentrations. Eleven SNPs from CYP1A2, CYP3A5, UGT1A4, and FMO1/3 were identified through real-time fluorescence quantitative polymerase chain reaction (q-PCR). A Bayesian network was applied to elucidate causal relationships between variables, followed by g-computation to quantify the effect of individual factors on the dose-adjusted concentration (C/D ratio) of olanzapine. The Wilcoxon signed-rank test assessed the intra-individual variations in the steady-state C/D ratio. Results: The Bayesian network suggested causality between smoking, sex, sertraline, Danggui-Longhui, valproic acid, and the olanzapine C/D ratio. None of the SNPs reached significance levels. The Wilcoxon signed-rank test confirmed that both polypharmacy and inflammation increased the C/D ratio within individuals. G-computation showed that the log ratio of olanzapine C/D decreased by 0.392 ± 0.037 in males and 0.004 ± 0.001 with smoking. Danggui-Longhui, sertraline, and valproic acid reduced the ratio by 0.652 ± 0.131, 0.398 ± 0.127, and 0.328 ± 0.039, respectively. Conclusion: Our study confirms that sex, age, smoking, inflammation, and co-prescription with sertraline, Danggui-Longhui, and valproic acid contributed to variability in olanzapine’s steady-state concentration. Considering these factors in clinical practice may help to personalize olanzapine treatment in Asian patients.

Purpose: To study the antiplatelet mechanisms of ticagrelor. Experimental design: Platelets underwent activation with 20 μM ADP for 30 seconds followed by inhibition with 2 nM ticagrelor for another 30 seconds. Mass-spectrometry-based phosphoproteomic technique was applied to obtain phosphorylation spectra in platelets. Results: We successfully quantified 2285 phosphopeptides with high confidence in 1189 phosphoproteins. Compared with intact platelets, ADP-activated platelets showed significant upregulation of PDE5ASer102 and downregulation of 178 phosphopeptides in 154 proteins. Gene Ontology analysis showed that downregulated phosphoproteins were enriched in molecular functions and pathways associated with RNA processing and surveillance. After ticagrelor treatment, we identified 53 significantly regulated phosphopeptides, including 17 upregulated and 36 downregulated, in 45 phosphoproteins. Eight phosphopeptides in STIM1, DENND4C, TNIK, BCL9L, DBN1, DOCK10, FRMD4B, and PRKAR2B were significantly downregulated after ADP stimulation and significantly upregulated after adding ticagrelor. They were mainly implicated in regulation of Ca2+ flow, Wnt/β-catenin signaling, and cytoskeleton remodeling, suggesting their potential role as mediators in ticagrelor-related signaling pathways. Conclusions and clinical relevance: By sequential activation and inhibition of platelets using mutual competitive inhibitors, ADP and ticagrelor, we demonstrated alternations in phosphorylation status of phosphoproteins, which could help to interpret the mechanism of bleeding complications associated with ticagrelor.

Objective: We aimed to investigate the relationship between gene polymorphisms and the occurrence of adverse clinical events following dual antiplatelet therapy in Patients with Symptomatic Intracranial Atherosclerotic Stenosis. Methods: A total of 195 patients were enrolled, categorized into 32 cases (those with clinical adverse events) and 163 controls (without events). Genotyping of 20 SNPs from 17 genes was executed. Statistical analyses (Fisher’s exact test, logistic regression) were applied to determine associations. Results: The ITGA2 rs1126643 (C807T) and rs1062535 (G873A) polymorphisms were significantly correlated with adverse clinical events. Specifically, the mutant frequency of allele C (ITGA2 rs1126643) and allele G (ITGA2 rs1062535) was significantly higher in cases compared to controls (OR = 2.97, 95%CI = 1.702-5.172, P = 0.0001; OR = 3.27, 95%CI = 1.762-6.066, P = 0.0002, respectively). Other genotypes showed no significant differences between the groups. Conclusion: The ITGA2 C807T and G873A polymorphisms may elevate the risk of vascular events in Chinese patients. Detecting these polymorphisms may be valuable in identifying patients at risk of recurrent ischemic events.

An outbreak of COVID-19 has spread worldwide, and it is urgent to evaluate risk factors associated with severe cases. We aimed to identify risk factors for severe patients with COVID-19. A total of 52 patients with COVID-19 admitted to Henan Provincial People’s Hospital were enrolled in this study, and patients’ information was extracted from electronic medical records. The severity of symptoms of COVID-19 was divided into “mild” and “severe.” Univariate analysis was used to select potential risk factors. A risk model was constructed using multivariable logistic regression analysis. Advanced age (OR = 1.098, 95% CI = 1.020-1.183) and number of comorbidities (OR = 6.067, 95% CI = 1.078-34.143) were significant risk factors for severe patients with COVID-19. The comorbidities included hypertension, diabetes, cardiovascular disease, respiratory disease, and past surgical history. A risk score was developed based on this model, and the developed risk prediction model had good discriminative power and was well-calibrated. This study indicates that advanced age and comorbidities are risk factors for severe patients with COVID-19. More attention should be paid to high-risk patients during hospitalization.