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.

T-DM1 and T-DXd are two promising antibody-drug conjugates for treating advanced HER2-positive breast cancer and HER2-mutated lung cancer. Understanding the differences in the adverse events (AEs) ...

Background: The purpose of this study is to identify and characterize ocular adverse events (AEs) that are significantly associated with anti-VEGF drugs for treatment of neovascular age-related macular degeneration and compare the differences between each drug, and provide clinical reference.Methods: Ocular AEs submitted to the US Food and Drug Administration were analyzed to map the safety profile of anti-VEGF drugs. The Pharmacovigilance tools used for the quantitative detection of signals were reporting odds ratio and bayesian confidence propagation neural network.Results: A total of 10,608,503 AE reports were retrieved from FAERS, with 20,836 for ranibizumab, 19,107 for aflibercept, and 2,442 for brolucizumab between the reporting period of Q1, 2004 and Q3, 2021. We found and analyzed the different AEs with the strongest signal in each drug—ranibizumab-macular ischaemia (ROR = 205.27, IC-2SD = 3.70), retinal pigment epithelial tear (ROR = 836.54, IC-2SD = 7.19); aflibercept-intraocular pressure increased (ROR = 31.09, IC-2SD = 4.61), endophthalmitis (ROR = 178.27, IC-2SD = 6.70); brolucizumab-retinal vasculitis (ROR = 2930.41, IC-2SD = 7.47) and/or retinal artery occlusion (ROR = 391.11, IC-2SD = 6.10), dry eye (ROR = 12.48, IC-2SD = 2.88).Conclusion: The presence of AEs should bring clinical attention. The use of anti-VEGF drugs should be based on the patient’s underlying or present medical condition to reduce any adverse event associated with the treatment.

Objective: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method.Methods: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model.Results: Three algorithms (SVR, GBRT, and RF) with high R² scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R² = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors.Conclusion: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.

Purpose: Voriconazole (VCZ) is a first-line treatment for invasive fungal disease, characterized by a narrow therapeutic window and significant inter-individual variability. It is primarily metabolized by the liver, the function of which declines with age. Pathological and physiological changes in elderly patients contribute to increased fluctuations in VCZ plasma concentrations. Thus, it is crucial to develop a model that accurately predicts the VCZ plasma concentrations in elderly patients. Patients and Methods: This retrospective study incorporated 31 features, including pharmacokinetic parameters derived from a population pharmacokinetic (PPK) model. Feature selection for machine learning (ML) models was performed using Recursive Feature Elimination with Cross-Validation (RFECV). Multiple algorithms were selected and combined into an ML ensemble model, which was interpreted using Shapley Additive exPlanations (SHAP). Results: The predictive performance of ML models was significantly improved by incorporating pharmacokinetic parameters. The ensemble model consisting of XGBoost, random forest (RF), and CatBoost (1:1:8) achieved the highest R² (0.828) and was selected as the final ML model. Feature selection reduced the number of features from 31 to 9 without compromising predictive performance. The R², mean absolute error (MAE), and mean squared error (MSE) of the external validation dataset were 0.633, 1.094, and 2.286, respectively. Conclusion: Our study is the first to incorporate pharmacokinetic parameters into ML models to predict VCZ plasma concentrations in elderly patients. The model was optimized using feature selection and may serve as a reference for individualized VCZ dosing in clinical practice, thereby enhancing the efficacy and safety of VCZ treatment in elderly patients.