Introduction: Herpes zoster (HZ) can adversely influence patients' quality of life and sometimes it can develop postherpetic neuralgia (PHN). To date, it remains challenging manage well using currently available therapies. Tender point infiltration (TPI) might have the potential to be an analgesic therapy for acute and subacute HZ pain. However, current evidence remains insufficient. Therefore, the purpose of this protocol is to design a study to: 1) evaluate the analgesia efficacy and safety of TPI for acute and subacute HZ; 2) to explore the positive predictors of TPI for prevention of PHN. Methods and Analysis: This study is designed as a randomized, prospective, multicenter, blinded endpoint, open-label controlled trial including a 12-month follow-up period. 176 qualified participants will be randomly split into the standard group or TPI group in a ratio of 1:1. Primary outcome will be the presence of PHN 12 months posttreatment. Secondary outcomes include the presence of PHN at month 3 and month 6 posttreatment. Visual Analogue Scales (VAS) for assessment of pain, consumption of oral analgesia drugs, patient satisfaction scores on the 5-point Likert scale, patients' quality of life scored on the WHOQOL-BREF at day 1, week 2, month 1, month 3, month 6 and month 12, proportion of patients receiving repeated TPIs and block points each time for TPI group. Multivariable logistic analyses will be performed to identify predictive factors for the prevention of PHN at month 12 after treatment. Safety evaluation will be determined by adverse events during the trial.

High-dose methotrexate (HD-MTX)-induced nephrotoxicity remains a critical clinical challenge in primary central nervous system lymphoma (PCNSL) treatment, yet quantitative tools for individualized risk prediction are currently lacking. Here, a population pharmacokinetic/pharmacodynamic analysis was performed using 5,918 plasma concentration samples from 743 Chinese adult patients. Nonlinear mixed-effects modeling was employed to establish toxicodynamic structural models driven by MTX alone, 7-Hydroxy-MTX alone, or their combination. Although all models exhibited comparable predictive performance, the MTX linear model was selected as it offered the optimal balance between predictive performance and clinical feasibility. Covariate analysis identified hemoglobin as the most significant predictor, with higher levels correlating with reduced susceptibility to renal injury. To facilitate clinical translation, exposure-toxicity probability curves were constructed, enabling clinicians to identify dynamic concentration thresholds tailored to specific toxicity grades (Grade 1 vs. ≥2 nephrotoxicity) and individualized risk tolerance. Using a 10% risk probability for Grade ≥2 nephrotoxicity as the safety monitoring threshold, the corresponding MTX concentration thresholds at 24, 48, and 72 h were 9.71, 0.81, and 0.26 μmol/L, respectively. This quantitative framework serves as an exploratory tool to complement therapeutic drug monitoring, assisting in the early identification of MTX-induced renal injury among PCNSL patients with normal to mildly impaired renal function.

Background Medication errors pose critical threat to patient safety. The implementation of intelligent pharmacy technologies demonstrate great potential to reduce medication risks. This study aimed to investigate the current status of intelligent pharmacy technology implementation in Chinese hospitals. Methods A nationwide survey was conducted from May 29 to June 10, 2025, covering 1,126 hospitals across China. Based on the Intelligent pharmacy products (IPDs) identified through the Delphi method, this survey mainly collected data on current utilization rates of the identified IPD. Pearson’s chi-square test was applied to compare usage rates across different hospital types. Results A total of 36 kinds of commonly used IPDs were identified and classified into six categories: intelligent drug supply (IDS), prescription review (IPR), drug preparation (IDP), drug distribution (IDD), medication consultation (IMC), and medication monitoring systems (IMM). Usage rates of these 6 types of systems varied significantly in hospital of different levels and number of beds (P < 0.001). Hospitals with a higher number of pharmacists per 100 beds (PPB≥ 7) reported significantly lower utilization rates of IPR, IMC, and IMM systems (P<0.001). Regionally, eastern coastal provinces such as Zhejiang, Jiangsu, and Shandong demonstrated higher levels of intelligent pharmacy implementation. Conclusion The implementation of intelligent pharmacy in China was uneven across regions, hospital levels, sizes, and pharmacist numbers. Future development should be supported by policy incentives and tailored to specific clinical needs, ensuring a balanced and sustainable advancement of intelligent pharmacy.

Xiangjun Zhou,1 Rong Han,2 Zhigang Zhao,2 Fang Luo1 1Department of Pain Management, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, People&rsquo;s Republic of China; 2Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, People&rsquo;s Republic of ChinaCorrespondence: Fang Luo, Department of Pain Management, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, People&rsquo;s Republic of China, Tel +86 010 59976664, Email 13611326978@163.com

Introduction Postoperative intracranial infections present significant therapeutic challenges in neurosurgery, with inadequate cerebrospinal fluid drainage and antimicrobial resistance constituting primary risk factors. The escalating prevalence of extensively drug-resistant pathogens necessitates combination therapy, particularly vancomycin-meropenem-sulbactam (VCM-MER-SUL) regimens for multidrug-resistant infections. Marked interpatient pharmacokinetic variability complicates dosing, prompting the development of a UHPLC-MS/MS method for precise therapeutic drug monitoring (TDM) of these agents. Methods Chromatographic separation was achieved on a BEH C18 column (Waters, 2.1 x 50 mm, 1.7 mu m particles) with 0.1% formic acid in water (A) and methanol (B) under gradient elution conditions: 95% A; 0.7 min, 70% A; 1.0 min, 5% A; 2.0 min, 5% A; 2.1 min, 95% A; 3.5 min, 95% A. The column oven and autosampler temperatures were maintained at 37 degrees C and 15 degrees C, respectively. The mobile phase flow rate was set at 0.4 mL/min. The ion transition was m/z 725.5 > 144.0 for VCM, m/z 383.7 > 141.1 for MER, m/z 234.1 > 124.0 for SUL, and m/z 390.0 > 114.1 for MER-D6 (internal standard). The one-step precipitation by acetonitrile was used for sample pretreatment.Methods Chromatographic separation was achieved on a BEH C18 column (Waters, 2.1 x 50 mm, 1.7 mu m particles) with 0.1% formic acid in water (A) and methanol (B) under gradient elution conditions: 95% A; 0.7 min, 70% A; 1.0 min, 5% A; 2.0 min, 5% A; 2.1 min, 95% A; 3.5 min, 95% A. The column oven and autosampler temperatures were maintained at 37 degrees C and 15 degrees C, respectively. The mobile phase flow rate was set at 0.4 mL/min. The ion transition was m/z 725.5 > 144.0 for VCM, m/z 383.7 > 141.1 for MER, m/z 234.1 > 124.0 for SUL, and m/z 390.0 > 114.1 for MER-D6 (internal standard). The one-step precipitation by acetonitrile was used for sample pretreatment. Results The calibration range for VCM and SUL was established between 0.1 and 40 mg/L, whereas for MER, it was set from 0.02 to 8 mg/L. In all cases, the linearity of the calibration curves was deemed satisfactory; the method demonstrated acceptable accuracy and precision, with intra-day and inter-day bias ranging from -9.98% to 13.40%, while the corresponding imprecision remained below 12.85%. Additionally, the variability in internal standard-normalized recovery and matrix effects was controlled, showing coefficients below 12.76% and 8.77%, respectively. Stability assessments confirmed that all analytes remained within acceptable limits under the tested conditions. Discussion Initial trials with an acetonitrile-water mobile phase yielded poor peak symmetry and inadequate resolution for all analytes. Subsequent systematic evaluation of organic modifiers (acetonitrile vs methanol) and formic acid concentrations identified a 0.1% formic acid aqueous/methanol gradient as optimal. This condition, implemented on a BEH C18 column at 37 degrees C, delivered symmetric peaks for vancomycin, meropenem, and sulbactam within 3.5 min-markedly faster than most reported methods. A single-step acetonitrile protein precipitation doubled vancomycin recovery relative to methanol while maintaining high efficiency for the other analytes. The method's calibration ranges, matrix effects, recoveries, and long-term stability under clinically relevant storage and handling conditions were fully validated and concordant with literature benchmarks. Conclusion The method is fast, sensitive, accurate, and reliable, and has been verified in the study. The streamlined one-step protein precipitation method for sample preparation, coupled with rapid chromatographic separation (3.5 minutes), demonstrated suitability for clinical applications.

In situ vaccination (ISV) has emerged as a promising strategy in cancer immunotherapy. However, systemic administration of immunostimulatory agents or chemotherapeutics often results in toxicity and immune-related adverse events. Herein, we developed a tumor microenvironment (TME)-responsive nanoprodrug by conjugating the Toll-like receptor 4 (TLR4) agonist monophosphoryl lipid A (MPLA) with the chemotherapeutic agent doxorubicin (DOX) via a matrix metalloproteinase-9 (MMP-9)-cleavable peptide and polyethylene glycol (PEG), forming an amphiphilic conjugate that spontaneously self-assembled into stable, injectable nanoparticles (named as MPPD NPs). Upon reaching the TME, MMP-9-mediated cleavage of the peptide linker triggered the disintegration of the nanoparticle outer structure, resulting in localized DOX release. The released DOX induced immunogenic cell death of tumor cells, promoting the release of damage-associated molecular patterns and initiating immune responses. Simultaneously, the residual MPLA-PEG core nanoparticles (MP NPs) were internalized by dendritic cells, triggering the TLR4 signaling pathway to enhance tumor antigen presentation and cytokines secretion, thereby eliciting robust CD8+ T cell-mediated cytotoxic immune response. In vivo, this nanoprodrug not only effectively suppressed tumor progression and extended survival time in tumor-bearing mice but also significantly reduced DOX-associated cardiotoxicity. RNA sequencing of tumor tissue further confirmed that MPPD NPs potentiate ISV by activating immune-related pathways. This study presented a safe and effective ISV strategy that elicits robust tumor-specific immunity and provides a new insight into the design of combinatorial chemo-immunotherapeutic nanoplatform.

Objective: The primary objective of this study revolves around the development of a population pharmacokinetic (PPK) model for vancomycin in neonatal subjects, with the objective of providing a theoretical basis for judicious therapeutic interventions. Methods: In this study, a retrospective collection encompassed 75 neonatal patients, contributing to a total of 89 vancomycin blood concentration monitoring datasets. The establishment of the PPK model is carried out utilizing the nonlinear mixed effects model methodology. The PPK model was constructed employing a one‐compartment model with proportional residual error, and the influence of covariates on pharmacokinetic parameters was systematically assessed through forward stepwise addition and backward elimination methods. The stability and predictive accuracy of the final model were assessed using goodness‐of‐fit plots, nonparametric bootstrap validation, visual predictive checks, and normalized prediction distribution errors. Furthermore, Monte Carlo simulations were employed to predict vancomycin concentrations in neonatal patients with typical characteristics. Results: The final PPK model yielded population‐typical values of 0.24 L/h for vancomycin clearance (CL). Noteworthy contributors to vancomycin CL were identified as body weight, gestational age, creatinine clearance rate (CLcr), and sex. Internal validation results of the model indicate that it possesses stability, efficacy, and demonstrates a favorable predictive capacity. Monte Carlo simulations indicate that for a male neonatal patient characterized by a gestational age of 37 weeks, a body weight of 2.5 kg, and a CLcr of 60 mL/min, the recommended dosing regimen is 25.5 to 41.5 mg every 8 h. Conclusion: This investigation has successfully formulated a PPK model for vancomycin in neonatal patients, offering the capacity to estimate individual CL. The dosing regimen for neonates should take into account factors such as body weight, gestational age, CLcr, and sex.

Background: Perampanel is a promising epilepsy treatment with an innovative mechanism of action. This study was performed to investigate the factors affecting perampanel clearance in a population pharmacokinetic (PPK) model of Chinese pediatric and adult patients with epilepsy. Methods: A total of 135 perampanel plasma concentrations from 125 patients with epilepsy were analyzed using the PPK model with nonlinear mixed-effects modeling. One-compartment and proportional residual models best described the pharmacokinetics of perampanel. Covariate effects on the model parameters were assessed using forward and backward elimination. Goodness-of-fit, bootstrapping, visual predictive checks, and normalized prediction distribution errors were used to evaluate the model. Monte Carlo simulations were conducted to assess the impact of covariate combinations on perampanel plasma concentrations at different dosages. Results: In the final PPK model, body weight (BW), concomitant carbamazepine (CBZ), oxcarbazepine (OXC), and C-reactive protein (CRP) levels significantly influenced perampanel clearance. The interindividual clearance was calculated as follows: 0.84 × (BW/70) 0.53 × e CBZ × e OXC × e CRP (CBZ = 0.98, when comedicated with carbamazepine; OXC = 0.43, when comedicated with oxcarbazepine; CRP = −0.69, when CRP >15 mg/L, otherwise = 0). The estimates (relative standard error) for clearance and apparent volume of distribution of the final model were 0.84 L/h (8.75%) and 64.35 L (19.78%), respectively. The model maintained its stability and effectiveness with moderate predictability. Conclusions: BW and CBZ, OXC, and CRP levels may influence perampanel clearance in both pediatric and adult patients with epilepsy according to a population pharmacokinetic model that included real-world data.

High-dose methotrexate (HDMTX) is the cornerstone of the treatment for primary central nervous system lymphoma (PCNSL). The prevention of drug-induced toxicities is critical. This study aims to identify key factors associated with HDMTX-induced toxicities (hematotoxicity, hepatotoxicity and nephrotoxicit) in 713 Chinese PCNSL patients undergoing 3021 HDMTX treatment courses. Demographic data, administration information, laboratory tests, area under the curve, co-medications, and 30 single nucleotide polymorphisms were collected to analyze the association of HDMTX-related toxicities using PLINK and SPSS. Higher ALB level, female, ABCB1 rs1045642, MTHFR rs1801131, and MTHFD1 rs2236225 were associated with lower risk of anemia, while the combination of furosemide, torasemide, bumetanide, and levetiracetam associating with higher risk. Co-use of torasemide had higher incidence of neutropenia. Higher level of ALB was correlated with less leukopenia; torasemide and rs2236225 were related to more leukopenia. Female, furosemide, rs1801133, ABCG2 rs2231142, ABCC2 rs717620 were related to more thrombocytopenia, while rs1045642 and high ALB were related to less. Rs1801131 and female were correlated with more hepatotoxicity, whereas furosemide was correlated with less. In nephrotoxicity, female and rs1801394 were correlated with less, MTHFR rs1801131 and rs1801133 were correlated with more. In conclusion, higher ALB levels had a lower risk of HDMTX toxicities; loop diuretics and levetiracetam generally accelerated the occurrence of toxicities. Rs1801133 GG, rs1128503 GG + AG, rs2231142 AA+ AC, rs717620 TT + GT were associated with increased risk of toxicity; rs1045642 TT and rs1801394 GG + AG were less likely to develop toxicity.

This study aims to investigate the pharmacokinetics of methotrexate (MTX) in Chinese patients with intracranial germ cell tumors (iGCTs) and to develop a robust population pharmacokinetic (PPK) model. A two-compartment model with an exponential inter-individual variability and a proportional residual model was established using nonlinear mixed-effects modeling. The model was based on 5,470 plasma concentration data points from 505 Chinese iGCT patients, including 370 children. The impact of covariates on model parameters was evaluated using forward addition and backward elimination strategies. Goodness-of-fit plots, bootstrap, visual predictive check and normalized prediction distribution errors were used to assess model performance. In the final model, the clearance of the central compartment (CL) was determined using the following equation CL=12.88×eGFR/102.20.23×BW/470.39×eBLM×TBIL/15.3−0.05×ALB/40.9−0.18 (BLM = 0.08 when combined with bleomycin, otherwise = 0). The apparent volume of the central compartment (Vc) was Vc=72.04×BW/470.31. The apparent volumes of the peripheral compartments (Vp) and the inter-compartmental clearance (Q) were fixed as 94.94 L and 1.08 L/h, respectively. Co-administration with bleomycin could increase MTX CL by a factor of 1.08. Elevated total bilirubin and albumin levels were associated with decreased MTX CL. Goodness-of-fit and model evaluation confirmed the final model’s adequacy, stability, and predictive performance. In our study, a PPK model was developed to identify the key factors influencing MTX pharmacokinetics, thereby optimizing and personalizing MTX therapy for Chinese patients with iGCTs.

The whole blood concentrations of Tacrolimus, Cyclosporine A, Sirolimus, and Everolimus are critical for managing organ transplant rejection, hypersensitivity reactions, and autoimmune diseases. Routine monitoring of these immunosuppressive drugs aids in dose adjustment and toxicity prevention. An ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for simultaneous determination of whole blood Tacrolimus, Cyclosporine A, Sirolimus, and Everolimus was developed, validated, and applied in clinical samples. The ion transitions were m/z 821.5 > 768.4 for Tacrolimus, m/z 1219.7 > 1208.8 for Cyclosporine A, m/z 931.5 > 864.5 for Sirolimus, and m/z 975.6 > 908.4 for Everolimus. The flow rate was 0.6 mL/min with a run time of 3.5 min. The calibration ranges were 1.25-42.9 ng/mL for Tacrolimus, 21.7-1270 ng/mL for Cyclosporine A, 1.38-45.6 ng/mL for Sirolimus, and 1.22-44.6 ng/mL for Everolimus, respectively. The intra-day and inter-day inaccuracy and imprecision were within +/- 15 % for all analytes. Consistent internal standard -normalized recovery and minimal matrix effects were observed at all quality control levels. The UHPLC-MS/MS method offers significant advantages over traditional immunoassays and HPLC methods, including higher specificity, sensitivity, and the ability to detect multiple drugs simultaneously. It simplifies therapeutic drug monitoring (TDM) procedures, increases detection throughput, and supports optimizing immunosuppressive therapy and improving patient outcomes.

BACKGROUND:This study aimed to evaluate the predictive performance of published lamotrigine (LTG) population pharmacokinetic (PPK) models using an external data set of Chinese patients with epilepsy or postneurosurgery. METHODS:In total, 348 concentration measurements from 94 Chinese children and 254 Chinese adults with epilepsy or postneurosurgery were used for external validation. Data on published LTG PPK models were obtained from the literature. The predictability of the models was assessed using prediction-based diagnostics (eg, F20 and F30), simulation-based diagnostics, and Bayesian forecasting. RESULTS:The results of prediction-based diagnostics for all 10 models were unsatisfactory. The best-performing models, characterized as one-compartment models with nonlinear pharmacokinetics, incorporated weight as a key covariate and included interindividual variability for both clearance and volume of distribution. These models achieved exceptional predictive performance in simulation-based diagnostics and Bayesian forecasting, with IF 30 values of 90.32%, 97.23%, and 99.61%, respectively, demonstrating superior precision and accuracy. Bayesian forecasting improved the predictive accuracy of 80% of the models, significantly enhancing model predictability. CONCLUSIONS:The published PPK models show extensive variation in predictive performance for extrapolation among Chinese patients with epilepsy or postneurosurgery. The lack of key covariates (such as concomitant medications, genetic polymorphisms, and age stratification) and fixed parameters of volume of distribution and absorption rate constant in the PPK modeling of LTG may explain its unsatisfactory predictive performance. Bayesian forecasting significantly improves the model predictability and may help individualize LTG dosing.

PURPOSE:This study develops and compares population pharmacokinetics (PopPK) models and machine learning methods, including neural networks, to predict steady-state trough concentrations in pediatric patients and provide improved dosing recommendations. METHODS:Valproic acid concentration data were collected from 490 pediatric epilepsy patients treated at Beijing Tiantan Hospital and Beijing Children's Hospital. We developed predictive models employing PopPK, maximum a posteriori Bayesian (MAPB), multiple linear regression (MLR), machine learning (including Random Forest, XGBoost, and LightGBM for feature selection), and neural network techniques. The predictive accuracy of these models was then rigorously tested through external validation using the independent dataset from Beijing Children's Hospital. Upon identifying the optimal model, dosing regimens for various clinical scenarios were derived and presented. RESULTS:Under the same dataset modeling conditions, the original PopPK models showed limited predictive performance. Transforming these models into multiple linear regression enhanced prediction accuracy. Moreover, when prior data was available, the MAPB method significantly boosted prediction performance. Machine learning and neural networks showed higher accuracy, with neural networks achieving an F30 value above 80%. CONCLUSION:This study explored model optimization strategies and compared machine learning and neural network models alongside traditional PopPK. It introduced an advanced method to predict drug concentrations and stable trough dosing regimens in pediatric epilepsy treatment, reducing the need for frequent, invasive blood tests in TDM. These improvements enhanced the efficacy and safety of valproic acid therapy for children, supporting the development of personalized treatment plans.

Coronary artery disease (CAD) and stroke are leading causes of global morbidity and mortality. Their frequent comorbidities and overlapping risk profiles highlight the importance of understanding shared genetic mechanisms, particularly in identifying therapeutic targets relevant to personalized pharmacotherapy. This study aimed to explore the shared genetic architecture between stroke and CAD, identify common therapeutic targets, and provide implications for clinical pharmacy practice. We integrated multi-ancestry genome-wide association study (GWAS) summary statistics (stroke: 110,182 cases; CAD: 210,842 cases) and employed linkage disequilibrium score regression to assess genetic correlations. Bidirectional two-sample Mendelian randomization (MR) was employed to infer causal inference. Shared genetic variants were identified through cross-trait meta-analyses (MTAG and CPASSOC) and validated using Bayesian colocalization. Pharmacogenomic pathways associated with shared genes were linked to approved drugs using a pathway-pairing score to assess the therapeutic alignment. A score of ≥ 0.5 indicated a strong alignment between a drug’s pharmacological mechanism and the disease’s genetic pathophysiology. A significant genetic correlation was observed between stroke and CAD (rg = 0.48, P = 3.38 × 10−34). Eight pleiotropic SNPs and five colocalized causal variants were identified, implicating ten disease-shared genes. Drug-target analyses prioritized the 19 approved cardiovascular agents. Beta-blockers (e.g., bisoprolol, esmolol) and antihypertensives (e.g., fenoldopam bromide/mesylate) demonstrated strong therapeutic potential (pathway score ≥ 0.5). This study provides genomic evidence to support integrated therapeutic strategies for stroke and CAD. Pharmacogenomic insights into shared genetic determinants can assist clinical pharmacists in optimizing treatment selection, mitigating polypharmacy risks, and guiding precision medicine in patients with dual cardiocerebrovascular risks.

Purpose:Over the past five years, China's pharmaceutical industry has rapidly developed but still lags behind global leaders. This study aims to analyze and compare the trends in new drug approvals in China, the United States (US), the European Union (EU), and Japan from 2019 to 2023. Methods:Data on new drug approvals were collected from the National Medical Products Administration (NMPA), Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceuticals and Medical Devices Agency (PMDA), including information on the generic name, trade name, applicants, target, approval date, drug type, approved indications, therapeutic area, the highest R&D status in China, and special approval status. The approval time gaps between China and other regions were calculated. Results:From 2019 to 2023, China led with 256 new drug approvals, followed by the US (243 approvals), the EU (191 approvals), and Japan (187 approvals). Oncology, hematology, and infectiology were identified as the leading therapeutic areas globally and in China. Notably, PD-1 and EGFR inhibitors saw substantial approval, with 8 drugs each approved by the NMPA. China significantly reduced the approval timeline gap with the US and the EU since 2021, approving 15 first-in-class drugs during the study period. Conclusion:Despite COVID-19 challenges, China has improved in both the quantity and speed of new drug approvals, narrowing timeline gaps with major markets and enhancing its global pharmaceutical presence.

Drug-induced hearing loss (DIHL) is highly prevalent, but a comprehensive picture of ototoxicity associated with drugs are still lacking. In order to comprehensively summarize the hearing safety information of current drugs, we used the real-world data from 2004 to 2023 in the FDA Adverse Event Reporting System (FAERS) database to integrate the reported ototoxicity information of drugs and applied disproportionality analysis to evaluate the hearing impairment risk induced by drugs. A total of 108,435 adverse event (AE) reports of hearing impairment were extracted from the FAERS database, involving 1300 reported culprit-drugs. On the whole, acetylsalicylic acid was the most frequently reported potential ototoxic drug, followed by levothyroxine sodium, adalimumab, omeprazole, and ergocalciferol. Immunosuppressants was the most frequently reported drug class, followed by analgesics, psychoanaleptics, agents acting on the renin-angiotensin system, and antineoplastic agents. In risk signal detection, 432 of 1300 drugs exhibited potential ototoxic risk, in which tafenoquine showed the strongest statistical correlation with hearing impairment, followed by teprotumumab, amyl nitrite, potassium iodide, and paromomycin. Among main drug classes, antibacterials for systemic use was the drug class contained the maximum number of drugs with positive ototoxic risk signals, followed by psychoanaleptics, agents acting on the renin-angiotensin system, antineoplastic agents, and analgesics. In conclusion, our study summarized a comprehensive drug list containing 1300 reported potential ototoxic drugs in the FAERS database and profiled their ototoxicity risk characteristic from the aspect of reporting frequency and risk signal strength, which can provide reference for clinical medical staff to strengthen monitoring and management of DIHL.

Background Alzheimer's disease (AD) is a major neurodegenerative disorder with limited treatment options. Objective This study aimed to identify novel therapeutic targets for AD using proteome-wide Mendelian randomization (MR) and colocalization analyses. Methods We conducted a large-scale, proteome-wide MR analysis using data from two extensive genome-wide association studies (GWASs) of plasma proteins: the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study. We extracted genetic instruments for plasma proteins from these studies and utilized AD summary statistics from European Bioinformatics Institute GWAS Catalog. Colocalization analysis assessed whether identified associations were due to shared causal variants. Phenome-wide association studies and drug repurposing analyses were performed to assess potential side effects and identify existing drugs targeting the identified proteins. Results Our MR analysis identified significant associations between genetically predicted levels of 9 proteins in the deCODE dataset and 17 proteins in the UKB-PPP dataset with AD risk after Bonferroni correction. Four proteins (BCAM, CD55, CR1, and GRN) showed consistent associations across both datasets. Colocalization analysis provided strong evidence for shared causal variants between GRN, CR1, and AD. PheWAS revealed minimal potential side effects for CR1 but suggested possible pleiotropic effects for GRN. Drug repurposing analysis identified several FDA-approved drugs targeting CR1 and GRN with potential for AD treatment. Conclusions This study identifies GRN and CR1 as promising therapeutic targets for AD. These findings provide new directions for AD drug development, but further research and clinical trials are warranted to validate the therapeutic potential of these targets.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. The emergence of programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors offers new therapeutic options for patients with advanced NSCLC, but a comprehensive evaluation of their efficacy and safety is still lacking. In the present study randomized controlled trials (RCTs) published from January 2005 to May 2023 were identified through searches of PubMed, the Cochrane Library and Embase. Analysis focused on 10 PD-1/PD-L1 inhibitors for stages III and IV NSCLC in studies evaluating overall survival (OS), progression-free survival (PFS), the objective response rate, the disease control rate (DCR) and the incidence of severe treatment-related and immune-related adverse events. A total of 37 RCTs involving 31,779 patients were included in the analysis. Compared with chemotherapy, tislelizumab, pembrolizumab and nivolumab all significantly improved OS, with tislelizumab showing the highest probability of being the best treatment for improving OS and DCR. While cemiplimab and tislelizumab had the highest probabilities of improved PFS, no significant differences were observed across all PD-1/PD-L1 inhibitors. Combination therapies, such as nivolumab or cemiplimab with chemotherapy, increased OS and PFS but also increased the incidence of severe treatment-related adverse events. In particular, cemiplimab and pembrolizumab were associated with a greater risk of severe immune-related adverse events. In conclusion, PD-1/PD-L1 inhibitors, especially tislelizumab, pembrolizumab and nivolumab, were effective first-line treatments for NSCLC, providing survival benefits. However, the combination of PD-1/PD-L1 inhibitors with chemotherapy increased the risk of severe adverse events. Further research is needed to optimize treatment strategies.