Chimeric antigen receptor T (CAR-T) cell therapy has undergone vigorous development in recent years, yet it still faces significant challenges and difficulties in its clinical application and further development. A systematic synthesis of global trends in CAR-T clinical trials is essential to identify knowledge gaps, optimize treatment strategies, and guide future research directions. This review analyzed 1,580 CAR-T clinical trials registered at ClinicalTrials.gov as of April 2024, and extracted characteristic data in multiple dimensions, including target specificity, treatment indication, and development stage etc. The transparency of trial outcomes was assessed by validation with articles published in PubMed/Google Scholar. Additionally, it is complemented by investigator surveys assessing to barriers to CAR-T development, prospects, and recommendations.

The pharmacokinetics of vancomycin exhibit significant inter-individual variability, particularly among elderly patients. This study aims to develop a predictive model that integrates machine learning with population pharmacokinetics (popPK) to facilitate personalized medication management for this demographic. A retrospective analysis incorporating 33 features, including popPK parameters such as clearance and volume of distribution. A combination of multiple algorithms and Shapley Additive Explanations was utilized for feature selection to identify the most influential factors affecting drug concentrations. The performance of each algorithm with popPK parameters was superior to that without popPK parameters. Our final ensemble model, composed of support vector regression, light gradient boosting machine, and categorical boosting in a 6:3:1 ratio, included 16 optimized features. This model demonstrated superior predictive accuracy compared to models utilizing all features, with testing group metrics including an R2 of 0.656, mean absolute error of 3.458, mean square error of 28.103, absolute accuracy within ± 5 mg/L of 81.82%, and relative accuracy within ± 30% of 76.62%. This study presents a rapid and cost-effective predictive model for estimating vancomycin plasma concentrations in elderly patients. The model offers a valuable tool for clinicians to accurately determine effective plasma concentration ranges and tailor individualized dosing regimens, thereby enhancing therapeutic outcomes and safety.

Background: Biktarvy, a second-generation integrase strand-transfer inhibitor, is approved for treating human immunodeficiency virus (HIV) in patients with no known resistance to its components. This study compared adverse events (AEs) associated with biktarvy based on data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS) database and real-world evidence from a hospital setting to inform clinical practice. Methods: AE reports were extracted from the FAERS database and our hospital’s internal records. The Reporting Odds Ratio (ROR) and the Bayesian Confidence Propagation Neural Network (BCPNN) were used to assess the reporting of AEs associated with biktarvy. A positive signal was defined as meeting all of the following criteria: case number ≥ 3, ROR 95% confidence interval lower limit > 1.0, (IC-2SD) > 0. Results: A total of 10,424 AE reports were retrieved (6,850 from FAERS and 3,574 from hospital data). Males and patients aged 18–64.9 years accounted for a larger proportion of AE reports in terms of raw counts. Overall, 343 unique AEs met the positive signal threshold (263 from FAERS and 80 from the hospital). Hospital data identified signals including elevated urine vitamin C, triglycerides, and uric acid. FAERS data revealed signals for cerebrovascular accident, myocardial infarction, and cardiomegaly. Conclusion: Hospital data indicated a risk of eye disorders and certain abnormal investigation findings not mentioned in the package insert, while the FAERS data pointed to a potential risk domain of vascular diseases such as cerebrovascular accident and myocardial infarction. Our study provides evidence for the use of biktarvy in the treatment of HIV.

Background: Newer second-generation antipsychotics (SGAs) differ in pharmacology and approved indications, and extrapyramidal symptoms (EPS) remain clinically important. We investigated whether lurasidone, brexpiprazole, cariprazine, and lumateperone showed disproportionate reporting of selected EPS in the FDA Adverse Event Reporting System (FAERS). Methods: We conducted a retrospective pharmacovigilance signal-detection study of spontaneous reports using OpenVigil 2.1-MedDRA-v24 (FAERS Q1 2004-Q3 2025). Analyses were restricted to reports in which the target drug was the primary suspect. Narrow MedDRA sub-SMQs for Akathisia, Dyskinesia, and Dystonia and their Preferred Terms (PTs) were evaluated with the reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian information component (IC). Sensitivity analyses used a common calendar window (Q1 2020-Q3 2025) and the first 24 post-approval quarters for each drug. Results: All four drugs had positive reporting signals for the three selected outcomes, and Akathisia had the largest within-drug IC025. Drug-specific PT patterns included akathisia/restlessness, tardive dyskinesia, and focal dystonia terms. The four separately calculated drug-versus-database estimates were not tested against one another and are not comparative risk estimates. All 24 estimates in the two time-window sensitivity analyses retained a lower ROR confidence bound above one and IC025 above 0, although signal magnitudes varied across windows. Conclusion: The findings generate pharmacovigilance hypotheses for selected EPS only; they do not estimate incidence, comparative risk, or causality and should not guide preferential prescribing. Although positive signals persisted in both time-window sensitivity analyses, variation in signal magnitude, reporting behavior, differences in indication and concomitant treatment, the unexplained denominator discrepancy, and omission of Parkinson-like events require cautious interpretation and independent validation.

Background: Sphingosine-1-phosphate receptor modulators (S1PRMs) are an effective class of disease-modifying therapies (DMTs) for the treatment of multiple sclerosis (MS). While cardiac arrhythmias such as bradycardia and atrioventricular (AV) block are recognized class effects of S1PRMs, comparative real-world safety profiles across all four approved agents-particularly newer agents with limited post-marketing data-remain insufficiently characterized. Methods: We conducted a retrospective pharmacovigilance analysis using data from the FDA Adverse Event Reporting System (FAERS) from the fourth quarter of 2010 to the first quarter of 2024. Disproportionality analysis was performed using the Reported Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS). Adverse events were classified using Standardised MedDRA Queries narrow-scope terms and Preferred Terms (PTs). Univariate logistic regression was used to explore the association between demographic factors and arrhythmia reporting. Results: We identified 2,059 arrhythmia-related adverse events associated with S1PRMs, including 1,860 cases for fingolimod, 153 for siponimod, 38 for ozanimod, and 8 for ponesimod. Patients aged 18–65 and female patients constituted the majority of reports. Disproportionality analysis based signal detection identified heart rate decreased, cardiac flutter, and extrasystoles as prominent signals across S1PRMs. Conclusion: This pharmacovigilance study provides a comparative real-world overview of arrhythmia-related adverse event signals across four approved S1PRMs. The findings are concordant with the cardiac effects anticipated from the known pharmacology of S1PRMs and reinforce the importance of cardiac monitoring during treatment initiation. As a disproportionality analysis of spontaneous reports, these results reflect statistical associations rather than causal relationships and should be regarded as hypothesis-generating; cross-drug comparisons are further influenced by differential reporting, market duration and cumulative exposure. These results may complement clinical trial data for informed risk-benefit assessment, particularly for newer S1PRMs with limited post-marketing surveillance data.

Correction on: Pan X, Tian Q, Wang Q, Xie L and Xie J (2026) Comparative safety profile of biktarvy: insights from a clinical cohort and the FAERS database. Front. Pharmacol. 17:1837474. doi: 10.3389/fphar.2026.1837474Missing Authors [Xinmei Pan 1 † , Qingqing Tian 2 † , Qian Wang 1 † ] were erroneously omitted as equal contributing author. The original version of this article has been updated.