Nausea, Vomiting And Quality Of Life Of Patients With Cancer Undergoing Antineoplastic Treatment: An Evaluation By Pharmacists.

This study aims to evaluate the frequency and severity of nausea and vomiting using two different instruments and relate them to quality of life (QOL) in patients with cancer receiving antineoplastic treatment. Severity of chemotherapy-induced nausea and vomiting (CINV) was measured by Common Terminology Criteria for Adverse Events (CTCAE) and a numerical scale. QOL was assessed using the Functional Assessment of Cancer Therapy-General questionnaire. Of the 50 patients studied, 60.0% reported nausea (40.0% CTCAE grade 1; 66.7% moderate intensity on numerical scale) and 30.0% reported vomiting (46.7% CTCAE grades 1 and 2, each; 66.7% moderate intensity on numerical scale). CINV did not influence overall QOL. The frequency of CINV was high. There was no association between nausea/vomiting and overall QOL.

Study Of The Homogeneity Of Drug Loaded In Polymeric Films Using Near-infrared Chemical Imaging And Split-plot Design.

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.