A Validated HPTLC Densitometric Method for The Quantitative Determination of Ubidecarenone in Bulk and in Capsule Formulation

A new, simple, precise, accurate and rapid high performance thin layer chromatographic method has been developed and validated for the estimation of ubidecarenone in bulk and in capsule formulation. The chromatographic separation was performed on aluminium TLC plates precoated with silica gel 60F254 as a stationary phase and methanol:water (7:3) as a mobile phase. Detection was performed densitometrically in the absorbance mode at 280nm for the evaluation of chromatograms. The system has given well sharp peak of ubidecarenone (Rf=0.51±0.02). The linearity of the method was established in the range of 1-6 ng/µL with correlation coefficient (r2) of 0.9995. The method was validated for precision, accuracy, robustness, ruggedness, LOD, and LOQ as per ICH guidelines. The limit of detection was found to be 0.0392 ng/µL, whereas the limit of quantitation was found to be 0.1189 ng/µL. The percentage label claim for ubidecarenone in the capsule formulation was found to be 99.96±0.4703. The accuracy of the method was confirmed by recovery studies. The percentage recovery was found to be in the range of 100.10-101.45% for ubidecarenone. The % RSD value was found to be less than 2. The low %RSD value indicates that there is no interference due to excipients used in the formulation. Hence, the developed method was found to be simple, precise, accurate, and rapid for the analysis of ubidecarenone in bulk and pharmaceutical formulation and it can be effectively applied for the quality control analysis of ubidecarenone in bulk and pharmaceutical formulation.

New Cost-Effective RP-HPLC Method Development and Validation for Quantitative Estimation of Ivacaftor in Pharmaceutical Formulation

In this article “new cost-effective RP-HPLC method development and validation for quantitative estimation of ivacaftor in the pharmaceutical formulation” developed. This study includes RP-HPLC Spectrophotometric method development, such as economical and simple HPLC method was optimized during development and validated accordingly in tablets of ivacaftor. The developed method may utilize for the analysis of ivacaftor at the laboratory level. The result shows that developed methods are cost-effective, rapid (Short retention time), simple, accurate (the value and %RSD between 2-5), precise, and can be used for the intended purpose on the tablet dosage form. The present proposed method is capable of better separation of analyte and qualifies on the point of analytical validation such as linearity, specificity, accuracy, precision, robustness, LOD, and LOQ on a marketed formulation. The simplicity, rapidity, and reproducibility of the developed method qualify the objective of the research. Results of analysis of the ivacaftor tablet formulations are arranged in the experimental, result, and discussion section. The portion of ivacaftor found in terms of quantity was between 98-102% and also within USP 29 chapter (541) acceptance criteria.

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.