Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches.

OBJECTIVES: Orally disintegrating tablets (ODTs) can be utilized without any drinking water; this feature makes ODTs easy to use and suitable for specific groups of patients. Oral administration of drugs is the most commonly used route, and tablets constitute the most preferable pharmaceutical dosage form. However, the preparation of ODTs is costly and requires long trials, which creates obstacles for dosage trials. The aim of this study was to identify the most appropriate formulation using machine learning (ML) models of ODT dexketoprofen formulations, with the goal of providing a cost-effective and timereducing solution. METHODS: This research utilized nonlinear regression models, including the k-nearest neighborhood (k-NN), support vector regression (SVR), classification and regression tree (CART), bootstrap aggregating (bagging), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods, as well as the t-test, to predict the quantity of various components in the dexketoprofen formulation within fixed criteria. RESULTS: All the models were developed with Python libraries. The performance of the ML models was evaluated with R2 values and the root mean square error. Hardness values of 0.99 and 2.88, friability values of 0.92 and 0.02, and disintegration time values of 0.97 and 10.09 using the GBM algorithm gave the best results. CONCLUSIONS: In this study, we developed a computational approach to estimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was found that they complied with Food and Drug Administration criteria.

Wind tunnel studies on spray deposition on leaves of tree species used for windbreaks and exposure of honey bees.

A wind tunnel study was conducted to determine pesticide deposition on commonly used windbreak tree species used as spray drift barriers and associated exposure of honey bees. Although it has been known that windbreaks are effective in reducing chemical drift from agricultural applications, there is still an enormous information and data gap on details of the dependence of the mechanism on the biological materials of the barriers and on standardization of relevant assessment methods. Beneficial arthropods like honey bees are adversely affected by airborne drift of pesticides. A study was initiated by first establishing a wind tunnel to create a controlled environment for capture efficiency work. Suitable spray parameters were determined after a preliminary study to construct and develop a wind tunnel protocol. A tracer dye solution was sprayed onto the windbreak samples and honey bees located in the wind tunnel at various simulated wind speeds. Analysis of data from this work has shown that needle-like foliage of windbreak trees captures two to four times more spray than broad-leaves. In addition, it was determined that, at lower wind speeds, flying bees tend to capture slightly more spray than bees at rest.