Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning Study.

The aim of this study was to apply machine learning methods to deeply explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1,746 patients aged between 28 days and 18 years (mean age = 3.84 years) were included in the study from January 1, 2013, to December 31, 2015, in the Children's Hospital of Chongqing Medical University. There were 247 cases of ADE occurrence, of which the most common drugs inducing ADEs were antibacterials. Seven algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, AdaBoost, LightGBM, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and TPOT, were used to select the important risk factors, and GBDT was chosen to establish the prediction model with the best predicting abilities (precision = 44%, recall = 25%, F1 = 31.88%). The GBDT model has better performance than Global Trigger Tools (GTTs) for ADE prediction (precision 44 vs. 13.3%). In addition, multiple risk factors were identified via GBDT, such as the number of trigger true (TT) (+), number of doses, BMI, number of drugs, number of admission, height, length of hospital stay, weight, age, and number of diagnoses. The influencing directions of the risk factors on ADEs were displayed through Shapley Additive exPlanations (SHAP). This study provides a novel method to accurately predict adverse drug events in Chinese pediatric inpatients with the associated risk factors, which may be applicable in clinical practice in the future.

Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence.

Tacrolimus is a widely used immunosuppressive drug in patients with autoimmune diseases. It has a narrow therapeutic window, thus requiring therapeutic drug monitoring (TDM) to guide the clinical regimen. This study included 193 cases of tacrolimus TDM data in patients with autoimmune diseases at Southern Medical University Nanfang Hospital from June 7, 2018, to December 31, 2020. The study identified nine important variables for tacrolimus concentration using sequential forward selection, including height, tacrolimus daily dose, other immunosuppressants, low-density lipoprotein cholesterol, mean corpuscular volume, mean corpuscular hemoglobin, white blood cell count, direct bilirubin, and hematocrit. The prediction abilities of 14 models based on regression analysis or machine learning algorithms were compared. Ultimately, a prediction model of tacrolimus concentration was established through eXtreme Gradient Boosting (XGBoost) algorithm with the best predictive ability (R 2 = 0.54, mean absolute error = 0.25, and root mean square error = 0.33). Then, SHapley Additive exPlanations was used to visually interpret the variable's impacts on tacrolimus concentration. In conclusion, the XGBoost model for predicting blood concentration of tacrolimus on the basis of real-world evidence has good predictive performance, providing guidance for the adjustment of regimen in clinical practice.

Mannan-modified Ad5-PTEN treatment combined with docetaxel improves the therapeutic effect in H22 tumor-bearing mice.

BACKGROUND: It has been reported that the tumor suppressor gene, PTEN, which is inactivated in many malignant tumors, plays an important role in apoptosis, cell cycle arrest, cell migration, and cell spread. For cancer gene therapy, one of the most important problems is low gene transfection efficiency. METHODS: In the present study, to take full advantage of adenovirus in gene expression, we prepared mannan-modified recombinant adenovirus using the PTEN gene (Man-Ad5-PTEN) and investigated the effect of Man-Ad5-PTEN combined with docetaxel (Man-Ad5-PTEN-docetaxel) on tumor growth in a murine model of hepatocellular carcinoma. RESULTS: Man-Ad5-PTEN effectively suppressed tumor growth and induced significant apoptosis of murine H22 hepatoma in vivo. Apoptosis levels in tumor-bearing mice treated with Man-Ad5-PTEN-docetaxel were significantly higher than those in tumor-bearing mice treated with naked Ad5-PTEN, Man-Ad5-PTEN, or docetaxel alone. Treatment with Man-Ad5-PTEN-docetaxel resulted in a significant inhibitory effect in this tumor model. Compared with the controls treated with phosphate-buffered solution, the tumor inhibition rate with naked Ad5-PTEN, docetaxel, Man-Ad5-PTEN, and Man-Ad5-PTEN-docetaxel was 48.69%, 49.98%, 75.88%, and 96.93%, respectively. CONCLUSION: These results suggest that combined treatment with Man-Ad5-PTEN and other chemotherapeutic agents may be a potent adjuvant therapeutic approach for the treatment of hepatocellular carcinoma.