Post-marketing safety concerns with nirmatrelvir: A disproportionality analysis of spontaneous reports submitted to the FDA Adverse Event Reporting System.

AIMS: Nirmatrelvir is an antiviral drug with a novel mechanism of action, targeting the 3-CL protease, and is used in the treatment of COVID-19. However, the potential side effects have not yet been fully studied. The aim of this study was to identify potential safety signals of nirmatrelvir by analysing post-marketing safety data based on the largest publicly available worldwide pharmacovigilance database. METHODS: We analysed nirmatrelvir adverse events to identify and characterize relevant safety signals based on the FDA Adverse Event Reporting System database in 2022. The case/non-case approach was used to estimate the reporting odds ratio (ROR) and information component (IC) with relevant confidence intervals (95% CI) for adverse events (AEs) that numbered 4 or more. RESULTS: A total of 26 846 cases were included. Disease recurrence (ROR [95% CI] = 413.2 [395.6-431.59]), dysgeusia (ROR [95% CI] = 110.84 [106.04-115.85]), anosmia (ROR [95% CI] = 15.21 [12.76-18.11]), ageusia (ROR [95% CI] = 9.80 [8.50-11.3]) and urticaria (ROR [95% CI] = 1.91 [1.69-2.17]) were the main safety signals. In addition, abdominal pain upper and skin toxicity were two specific safety signals of nirmatrelvir. In the pregnant population, there was a significant increased ROR for life-threatening conditions (ROR [95% CI] = 8.00 [1.77-36.20]). CONCLUSIONS: Our study identified that the main and specific safety signals of nirmatrelvir were disease recurrence, dysgeusia, abdominal pain upper and skin toxicity. Clinicians and pharmacists should be vigilant of these AEs, although differentiating between COVID-19 symptoms and AEs can be challenging. Notably, a potential safety concern of nirmatrelvir should be a warning based on a small number of events in the pregnant population. However, the available data are insufficient, and further continued pharmacovigilance and surveillance is needed to fully understand this issue.

Drug Delivery and Therapy Strategies for Osteoporosis Intervention.

With the advent of the aging society, osteoporosis (OP) risk increases yearly. Currently, the clinical usage of anti-OP drugs is challenged by recurrent side effects and poor patient compliance, regardless of oral, intravenous, or subcutaneous administration. Properly using a drug delivery system or formulation strategy can achieve targeted drug delivery to the bone, diminish side effects, improve bioavailability, and prolong the in vivo residence time, thus effectively curing osteoporosis. This review expounds on the pathogenesis of OP and the clinical medicaments used for OP intervention, proposes the design approach for anti-OP drug delivery, emphatically discusses emerging novel anti-OP drug delivery systems, and enumerates anti-OP preparations under clinical investigation. Our findings may contribute to engineering anti-OP drug delivery and OP-targeting therapy.

Assessing safety concerns of interstitial lung disease associated with antibody-drug conjugates: a real-world pharmacovigilance evaluation of the FDA adverse event reporting system.

BACKGROUND: Antibody-drug conjugates have revolutionized cancer therapy due to their selectivity and efficacy. However, concerns have been raised regarding the potential effects of trastuzumab deruxtecan in interstitial lung diseases. AIM: This study aimed to investigate the safety signals and time to onset of antibody-drug conjugates induced interstitial lung disease. METHOD: We utilized the FDA Adverse Event Reporting System database (2004-2022) to identify interstitial lung disease safety signals in 13 FDA-approved antibody-drug conjugates. Disproportionality analysis was conducted to estimate the reporting odds ratios for interstitial lung disease. RESULTS: Seven antibody-drug conjugates exhibited safety signals of interstitial lung disease: trastuzumab deruxtecan [reporting odds ratio, ROR (95% confidence intervals, CI) = 64.15 (57.07-72.10)], enfortumab vedotin [ROR (95% CI) = 5.24 (3.25-8.43)], trastuzumab emtansine [ROR (95% CI) = 3.62 (2.90-4.53)], brentuximab vedotin [ROR (95% CI) = 3.22 (2.49-4.17)], polatuzumab vedotin [ROR (95% CI) = 2.56 (1.59-4.12)], gemtuzumab ozogamicin [ROR (95% CI) = 2.53 (1.70-3.78)], and inotuzumab ozogamicin [ROR (95% CI) = 2.33 (1.21-4.49)]. Five antibody-drug conjugates with limited reports were excluded from further analysis: belantamab mafodotin, loncastuximab tesirine, mirvetuximab sorafenib, tisotumab vedotin, and moxetumomab pasudotox. Japan and the United States were the primary reporting countries. CONCLUSION: This real-world study highlights high safety signals of interstitial lung disease associated with antibody-drug conjugates. Clinicians should be aware of these safety concerns and risk factors and implement early identification measures for their patients. Future research should prioritize comprehensively exploring the relationship between antibody-drug conjugates and lung diseases.

Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model.

This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the accuracy of the algorithm on the telecom user analysis platform under big data, they do not solve the limitations of single machine computing and cannot effectively improve the training efficiency of the model. To solve this problem, this article establishes a telecom customer churn prediction model with the help of backpropagation neural network (BPNN) algorithm, and deploys the MapReduce programming framework on Hadoop platform. Using the data of a telecom company, this article analyzes the loss of telecom customers in the big data environment. The research shows that the accuracy of telecom customer churn prediction model in BPNN is 82.12%. After deploying large data sets, the learning and training time of the model is greatly shortened. When the number of nodes is 8, the acceleration ratio of the model remains at 60 seconds. Under big data, the telecom user analysis platform not only ensures the accuracy of the algorithm, but also solves the limitations of single machine computing and effectively improves the training efficiency of the model. Compared with that of the existing research, the accuracy of the model is improved by 25.36%, and the running time is shortened by about twice. This business model based on BPNN algorithm has obvious advantages in processing more data sets, and has great reference value for the digital-driven business model transformation of the telecommunications industry.

Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder.

OBJECTIVE: Social media disorder (SMD) is an increasing problem, especially in adolescents. The lack of a consensual classification for SMD hinders the further development of the research field. The six components of Griffiths' biopsychosocial model of addiction have been the most widely used criteria to assess and diagnosis SMD. The Bergen social media addiction scale (BSMAS) based on Griffiths' six criteria is a widely used instrument to assess the symptoms and prevalence of SMD in populations. This study aims to: (1) determine the optimal cut-off point for the BSMAS to identify SMD among Chinese adolescents, and (2) evaluate the contribution of specific criteria to the diagnosis of SMD. METHOD: Structured diagnostic interviews in a clinical sample (n = 252) were performed to determine the optimal clinical cut-off point for the BSMAS. The BSMAS was further used to investigate SMD in a community sample of 21,375 adolescents. RESULTS: The BSMAS score of 24 was determined as the best cut-off score based on the gold standards of clinical diagnosis. The estimated 12-month prevalence of SMD among Chinese adolescents was 3.5%. According to conditional inference trees analysis, the criteria "mood modification", "conflict", "withdrawal", and "relapse" showed the higher predictive power for SMD diagnosis. CONCLUSIONS: Results suggest that a BSMAS score of 24 is the optimal clinical cut-off score for future research that measure SMD and its impact on health among adolescents. Furthermore, criteria of "mood modification", "conflict", "withdrawal", and "relapse" are the most relevant to the diagnosis of SMA in Chinese adolescents.

Synthesis and evaluation of artificial antigens for astragaloside IV

The objective of this study was to produce artificial antigens for astragaloside IV that could be used to prepare antibodies against astragaloside IV screened in Radix astragali (Astragalus membranaceus (Fisch) Bunge, Fabaceae) and its preparations, using an indirect ELISA. Astragaloside IV was coupled to carrier proteins, bovine serum albumin and ovalbumin using the sodium periodate method and was then evaluated using SDS-PAGE, MALDI-TOF MS and animal immunizations. The coupling ratio of astragaloside IV to bovine serum albumin ratio was determined to be thirteen, and the indirect ELISA demonstrated that three groups of mice immunized with astragaloside IV-bovine serum albumin produced anti-astragaloside IV- bovine serum albumin-specific antibody, with a minimum serum titer of 1:9600. A method for synthesizing highly immunogenic astragaloside IV artificial antigens was successfully developed thus indicating its feasibility in the establishment of a fast immunoassay for astragaloside IV content determination in Radix astragali and its products.