Cost-effectiveness analysis of elacestrant versus standard endocrine therapy for second-/third-line treatment of patients with HR+/HER2- advanced or metastatic breast cancer: a US payer perspective

BackgroundThis study evaluated the cost-effectiveness of elacestrant (ELA) and standard-of-care (SOC) as second-/third-line treatment for pretreated estrogen receptor (ER)– positive/human epidermal growth factor receptor 2 (HER2)–negative advanced or metastatic breast cancer (A/MBC) in the US.MethodsThe 3 health states partitioned survival model (PSM) was conducted from the perspective of the US third-party payers. The time horizon for the model lasted 10 years. Effectiveness and safety data were derived from the EMERALD trial (NCT03778931). Costs were derived from the pricing files of Medicare and Medicaid Services, and utility values were derived from published studies. One-way sensitivity analysis as well as probabilistic sensitivity analysis were performed to observe model stability.ResultELA led to an incremental cost-effectiveness ratio (ICER) of $8,672,360/quality-adjusted life year (QALY) gained compared with SOC in the overall population and$2,900,560/QALY gained compared with fulvestrant (FUL) in the ESR1(estrogen receptor 1) mutation subgroup. The two ICERs of ELA were significantly higher than the willingness-to-pay (WTP) threshold values of $150,000/QALY.ConclusionsELA was not cost-effective for the second-/third-line treatment of patients with ER+/HER2–A/MBC compared with SOC in the US

From population to individuals: a new indicator for evaluating the appropriateness of clinical application of antibiotics

Abstract Background This study aims to establish a new indicator based on the anatomical therapeutic chemical/defined daily dose (ATC/DDD) system. Methods Utilization data of antibiotics of inpatients in a university hospital were used to calculate the indicators of use rate (UR), use density (UD), and ratio of use density to use rate (UD/UR). According to the professional characteristics, the recommended values of UD/UR in different departments were established respectively. Crosswise comparison and appropriateness evaluation between different treatment groups with the same profession were performed. For individual inpatients with abnormally increased drug utilization index (DUI) and ratios of antimicrobial course to length of stay (C/S), detailed analysis was performed to examine whether any irrational drug utilization occurred. Results The indicator UD/UR combines both dose and duration of treatment, which were the two main factors affecting the appropriateness of clinical application of antibiotics. Thus, it can more sensitively reveal the drug utilization of inpatients receiving antibiotics. UD/UR is also more suitable for evaluating the clinical appropriateness of antibiotic application than the macroscopic indicator, total UD, and could be applied at the macroscopic and microscopic levels. Conclusions The ratio UD/UR has great practical value and can serve as a reference for evaluating the appropriateness of clinical application of antibiotics

Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice

Evaluation of automated systems for aminoglycosides and fluoroquinolones susceptibility testing for Carbapenem-resistant Enterobacteriaceae

Abstract Background Automated systems (MicroScan WalkAway 96 Plus, Phoenix 100, and Vitek 2 Compact) are widely used in clinical laboratories nowadays. The aim of this study is to evaluate the performance of these three systems for susceptibility testing of aminoglycosides and fluoroquinolones against Carbapenem-resistant Enterobacteriaceae (CRE). Methods A total of 75 CRE isolates were used in this study. Quinolone resistance determinants (QRDs) (qnrA, qnrB, qnrC, qnrD, qnrS, aac(6′)-Ib-cr, oqxAB and qepA) and aminoglycoside resistance determinants (ARDs) (aac(6′)-Ib, armA, npmA, rmtA, rmtB, rmtC, rmtD and rmtE) of these CRE were screened by PCR. The MICs of aminoglycosides (gentamicin and amikacin) and fluoroquinolones (ciprofloxacin and levofloxacin) to CRE obtained with the automated systems were compared with the reference method (agar dilution method). Results Totally, 97.3% (73/75) of CRE harbored QRDs. The qnr gene was the most common QRD determinant identified in 68 (96.7%), followed by aac (6′)-Ib-cr in 56 (74.7%), oqxAB in 23 (30.7%), and qepA in 2 (2.7%), respectively. 22.7% (17/75) of CRE harbored ARD determinants. rmtA, rmtB and npmA were identified among these isolates in 6 (8.0%), 6 (8.0%) and 5 (6.7%), respectively. A total of 900 results were obtained in this study. Overall, the total error rate was 9.89%. Twenty-eight very major errors (3.11%), 22 major errors (2.44%) and 39 minor errors (4.33%) were identified against agar dilution method. The very major errors were almost evenly distributed between results for fluoroquinolones (2.89%) and aminoglycosides (3.33%), while the major errors and minor errors were more commonly found in the results of fluoroquinolones (3.11% and 6.44%, respectively) than aminoglycosides (1.78% and 2.22%, respectively). Conclusions Our study shows that testing difficulties in susceptibility testing do exist in automated systems. We suggest clinical laboratories using automated systems should consider using a second, independent antimicrobial susceptibility testing method to validate aminoglycosides and fluoroquinolones susceptibility

Gelsemium elegans Benth: Chemical Components, Pharmacological Effects, and Toxicity Mechanisms

Gelsemium elegans Benth (GEB), also known as heartbreak grass, is a highly poisonous plant belonging to the family Loganiaceae and genus Gelsemium that has broad application prospects in medicine. This article reviews its chemical components, pharmacological effects, toxicity mechanisms, and research progress in clinical applications in recent years. Indole alkaloids are the main active components of GEB and have a variety of pharmacological and biological functions. They have anti-tumor, anti-inflammatory, analgesic, and immunomodulation properties, with the therapeutic dose being close to the toxic dose. Application of small-dose indole alkaloids fails to work effectively, while high-dose usage is prone to poisoning, aggravating the patient’s conditions. Special caution is needed, especially to observe the changes in the disease condition of the patients in clinical practice. In-depth research on the chemical components and mechanisms of GEB is essential to the development of promising lead compounds and lays the foundation for extensive clinical application and safe usage of GEB in the future

Comparison of immune checkpoint inhibitors related to pulmonary adverse events: a retrospective analysis of clinical studies and network meta-analysis

Abstract Background Immune checkpoint inhibitors (ICIs) have transformed tumor treatment. However, the risk of pulmonary adverse events (PAEs) associated with ICI combination therapy is still unclear. We aimed to provide a PAE overview and risk ordering of ICIs used in tumor treatment. Methods We searched the databases of PubMed, PsycINFO, Embase, Cochrane Library, CINAHL, Web of Science, Scopus, and clinical trial websites during January 2011–April 2023 to identify phase II and III randomized clinical trials (RCTs) and single-arm clinical trials wherein at least one treatment arm received ICIs (e.g., ICI monotherapy, a combination of two ICIs, or ICIs in combination with conventional cancer therapy). We reported the results of PAEs. Additionally, we compared risks of PAEs between different drug classes using a Bayesian network meta-analysis. Results Among 143 RCTs and 24 single-arm trials, the incidence of all-grade and grade 3–4 PAEs were highest with programmed death L1 (PD-L1) plus cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and plus chemotherapy and anti-PD1 plus anti-CTLA4, the lowest with targeted therapy drug plus chemotherapy and anti-PD1 plus anti-PDL1. Anti-PD1 plus anti-CTLA4 and plus chemotherapy was the intervention with the highest risk for all-grade and 3–4 grade PAEs, and the intervention with the lowest risk was chemotherapy and anti-PD1 plus anti-PDL1. In terms of all-grade PAEs, chemotherapy was safer than ICI monotherapy. Except for the anti-PD1 plus anti-PDL1 regimen, no significant difference in the risk of grade 3–4 PAEs was detected between dual-ICIs and single-ICIs. Furthermore, the risk of PAEs associated with nivolumab, pembrolizumab, and atezolizumab may be dose dependent. Conclusions In the single-drug regimen, anti-PD1 caused the greatest incidence of PAEs. The risk of PAEs was higher with all single-ICIs than with chemotherapy. However, no significant difference in the risk of PAEs was detected between single-ICIs. In the combined regimen, anti-PD1 plus anti-CTLA4 and plus chemotherapy showed the greatest risk of PAEs, but there were no significant differences in risk between dual-ICIs and single-ICIs

sj-docx-1-tam-10.1177_17588359221122733 – Supplemental material for First-line nivolumab plus ipilimumab or chemotherapy versus chemotherapy alone for advanced esophageal cancer: a cost-effectiveness analysis

Supplemental material, sj-docx-1-tam-10.1177_17588359221122733 for First-line nivolumab plus ipilimumab or chemotherapy versus chemotherapy alone for advanced esophageal cancer: a cost-effectiveness analysis by Xueqiong Cao, Hongfu Cai, Na Li, Bin Zheng, Zhiwei Zheng and Maobai Liu in Therapeutic Advances in Medical Oncology</p

Exploration of the potential association between GLP-1 receptor agonists and suicidal or self-injurious behaviors: a pharmacovigilance study based on the FDA Adverse Event Reporting System database

Abstract Background Establishing whether there is a potential relationship between glucagon-like peptide 1 receptor agonists (GLP-1RAs) and suicidal or self-injurious behaviors (SSIBs) is crucial for public safety. This study investigated the potential association between GLP-1RAs and SSIBs by exploring the FDA Adverse Event Reporting System (FAERS) database. Methods A disproportionality analysis was conducted using post-marketing data from the FAERS repository (2018 Q1 to 2022 Q4). SSIB cases associated with GLP-1RAs were identified and analyzed through disproportionality analysis using the information component. The parametric distribution with a goodness-of-fit test was employed to analyze the time-to-onset, and the Ω shrinkage was used to evaluate the potential effect of co-medication on the occurrence of SSIBs. Results In total, 204 cases of SSIBs associated with GLP-1RAs, including semaglutide, liraglutide, dulaglutide, exenatide, and albiglutide, were identified in the FAERS database. Time-of-onset analysis revealed no consistent mechanism for the latency of SSIBs in patients receiving GLP-1RAs. The disproportionality analysis did not indicate an association between GLP-1RAs and SSIBs. Co-medication analysis revealed 81 cases with antidepressants, antipsychotics, and benzodiazepines, which may be proxies of mental health comorbidities. Conclusions We found no signal of disproportionate reporting of an association between GLP-1RA use and SSIBs. Clinicians need to maintain heightened vigilance on patients premedicated with neuropsychotropic drugs. This contributes to the greater acceptance of GLP-1RAs in patients with type 2 diabetes mellitus or obesity. Graphical Abstrac

Saliva as a noninvasive sampling matrix for therapeutic drug monitoring of intravenous busulfan in Chinese patients undergoing hematopoietic stem cell transplantation: A prospective population pharmacokinetic and simulation study

Abstract Therapeutic drug monitoring (TDM) of busulfan (BU) is currently performed by plasma sampling in patients undergoing hematopoietic stem cell transplantation (HSCT). Saliva samples are considered a noninvasive TDM matrix. Currently, no salivary population pharmacokinetics (PopPKs) model for BU available. This study aimed to develop a PopPK model that can describe the relationship between plasma and saliva kinetics in patients receiving intravenous BU. The performance of the model in predicting the area under the concentration‐time curve at steady state (AUCss) based on saliva samples is evaluated. Sixty‐six patients with HSCT were recruited and administered 0.8 mg/kg BU intravenously. A PopPK model for saliva and plasma was developed using the nonlinear mixed effects model. Bayesian maximum a posteriori (MAP) optimization was used to estimate the model's predictive performance. Plasma and saliva PKs were adequately described with a one‐compartment model and a scaled central compartment. Body surface area correlated positively with both clearance and apparent volume of distribution (Vd), whereas alkaline phosphatase correlated negatively with Vd. Simulations demonstrated that the percentage root mean squared prediction error and lower and upper limits of agreements reduced to 10.02% and −16.96% to 22.86% based on five saliva samples. Saliva can be used as an alternative matrix to plasma in TDM of BU. The AUCss can be predicted from saliva concentration by Bayesian MAP optimization, which can be used to design personalized dosing for BU

Additional file 1 of Comparison of immune checkpoint inhibitors related to pulmonary adverse events: a retrospective analysis of clinical studies and network meta-analysis

Additional file 1: Supplementary Methods. Search Terms. Table S1. PRISMA NMA Checklist. Table S2. Study and Patient Characteristics. Table S3. Results of individual studies. Table S4. Fatal Pulmonary Adverse Events Outcome: League Table. Table S5. All-grade Treatment-related Pulmonary Adverse Events in Tumors of Different Systems Outcome: League Table. Table S6. Grade 3–4 Treatment-related Pulmonary Adverse Events in Tumors of Different Systems Outcome: League Table. Table S7. Model selection of All Outcomes. Table S8. Inconsistency Test. Figure S1. Flowchart of Study Selection. Figure S2. Risk of Bias Assessment for Studies. Figure S3. Comparison-adjusted funnel plots. Figure S4. Network plots. Figure S5. The distribution of SUCRA values stratified by cancer types. Figure S6. Subgroup analysis by dose of drug