Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches
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Objectives: Orally disintegrating tablets (ODTs) can be utilized without any drinking water; this feature makes ODTs easy touse and suitable for specific groups of patients. Oral administration of drugs is the most commonly used route, and tabletsconstitute the most preferable pharmaceutical dosage form. However, the preparation of ODTs is costly and requires longtrials, which creates obstacles for dosage trials. The aim of this study was to identify the most appropriate formulation usingmachine learning (ML) models of ODT dexketoprofen formulations, with the goal of providing a cost-effective and timereducingsolution. 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), randomforest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods, as well as the t-test, topredict the quantity of various components in the dexketoprofen formulation within fixed criteria. Results: All the modelswere developed with Python libraries. The performance of the ML models was evaluated with R2 values and the root meansquare error. Hardness values of 0.99 and 2.88, friability values of 0.92 and 0.02, and disintegration time values of 0.97 and10.09 using the GBM algorithm gave the best results. Conclusions: In this study, we developed a computational approach toestimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was foundthat they complied with Food and Drug Administration criteria.
Abstract
Objectives: Orally disintegrating tablets (ODTs) can be utilized without any drinking water; this feature makes ODTs easy touse and suitable for specific groups of patients. Oral administration of drugs is the most commonly used route, and tabletsconstitute the most preferable pharmaceutical dosage form. However, the preparation of ODTs is costly and requires longtrials, which creates obstacles for dosage trials. The aim of this study was to identify the most appropriate formulation usingmachine learning (ML) models of ODT dexketoprofen formulations, with the goal of providing a cost-effective and timereducingsolution. 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), randomforest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods, as well as the t-test, topredict the quantity of various components in the dexketoprofen formulation within fixed criteria. Results: All the modelswere developed with Python libraries. The performance of the ML models was evaluated with R2 values and the root meansquare error. Hardness values of 0.99 and 2.88, friability values of 0.92 and 0.02, and disintegration time values of 0.97 and10.09 using the GBM algorithm gave the best results. Conclusions: In this study, we developed a computational approach toestimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was foundthat they complied with Food and Drug Administration criteria.
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