Prolonged usage of fosaprepitant for prevention of delayed chemotherapy-induced nausea and vomiting(CINV) in patients receiving highly emetogenic chemotherapy.

BACKGROUND: Even though chemotherapy-induced nausea and vomiting (CINV) can be well controlled in the acute phase, the incidence of delayed CINV remains high. In this study, we intend to investigate whether prolonged use of NK-1 receptor antagonist (RA) in addition to 5-HT3 RA and dexamethasone (DEX) was more effective in preventing delayed CINV. METHODS: This randomised, open-label, controlled study was designed to compare the efficacy and safety of fosaprepitant 150 mg given on days 1,3 (prolonged group) versus on day 1 (regular group) in patients receiving highly emetogenic chemotherapy (HEC). All patients also treated with palonosetron on day 1 and DEX on days 1-3. The primary endpoint was the incidence of delayed nausea and vomiting. The second endpoint was AEs. All the above endpoints were defined according to CTCAE 5.0. RESULTS: Seventy-seven patients were randomly assigned to prolonged group and seventy-nine to regular group. Prolonged group demonstrated superiority in controlling delayed CINV to regular group, with statistically significant lower incidence of nausea (6.17% vs 12.66%, P = 0.0056), and slightly lower incidence of grade 1 vomiting (1.62% vs 3.80%, P = 0.0953) in the delayed phase. In addition, prolonged use of fosaprepitant was safe. No significant difference was found between the two groups regarding constipation, diarrhea, hiccough, fatigue, palpitation and headache in delayed phase. CONCLUSIONS: Prolonged use of fosaprepitant can effectively and safely prevent delayed CINV in patients receiving HEC.

A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction. METHODS: Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross-validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model-Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score. RESULTS: The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780-0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non-standard antiemetic regimen. CONCLUSION: The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC.

A multiple-function stationary phase based on perhydro-26-membered hexaazamacrocycle for high-performance liquid chromatography.

A perhydro-26-membered hexaazamacrocycle-based silica (L(1)GlySil) stationary phase for high-performance liquid chromatography (HPLC) was prepared using 3-glycidoxypropyltrimethoxysilane as coupling reagent. The structure of new material was characterized by infrared spectroscopy, elemental analysis and thermogravimetric analysis. The chromatographic performance and retention mechanism of the new phase were evaluated in reversed-phase (RP) and normal-phase (NP) modes using different solute probes including aromatic compounds, organophosphorus pesticides, carbamate pesticides and phenols. The results showed that L(1)GlySil was a sort of multimode-bonded stationary phase with excellent chromatographic properties. The new phase could provide various action sites for different solutes, such as hydrophobic, hydrogen bonding, pi-pi, dipole-dipole interactions and acid-base equilibrium. The presence of phenyl rings, secondary amino groups and alkyl linkers in the resulting material made it suitable for the separation of above-mentioned analytes by multimode retention mechanisms.