Prediction of saxagliptin stability using a new approach based on Partial Least Squares and Design of Experiments.

The objective of this study was to assess the possibility of applying Partial Least Squares (PLS) statistics with the use of experimental design approach towards stability evaluation of the Saxagliptin drug product. The influences of temperature, time, dose, packaging, batch, and oxygen protection were analyzed for identification of critical factors responsible for degradation of saxagliptin and prediction of impurity levels at various storage conditions. Predicted levels of the impurity DP-2 were lower for at least 0.2 % when the drug product was protected from oxygen after its manufacture. Additionally, the PLS model revealed that the lower strength is at least twice less stable concerning impurity DP-1. Based on this analysis shelf life for Zone II was proposed at 24 months with high reliability. Comparison of the PLS model estimates with the measured stability data at shelf life revealed good predictive ability of the developed model. Moreover, PLS predictions of DP-1 and Total impurities were more accurate than those obtained with a standard linear least squares regression, while DP-2 predictions were at least as accurate. We can thus propose a more extensive use of this approach for stability evaluation of pharmaceuticals.

Applying the methodology of Design of Experiments to stability studies: a Partial Least Squares approach for evaluation of drug stability.

The aim of the present research is to show that the methodology of Design of Experiments can be applied to stability data evaluation, as they can be seen as multi-factor and multi-level experimental designs. Linear regression analysis is usually an approach for analyzing stability data, but multivariate statistical methods could also be used to assess drug stability during the development phase. Data from a stability study for a pharmaceutical product with hydrochlorothiazide (HCTZ) as an unstable drug substance was used as a case example in this paper. The design space of the stability study was modeled using Umetrics MODDE 10.1 software. We showed that a Partial Least Squares model could be used for a multi-dimensional presentation of all data generated in a stability study and for determination of the relationship among factors that influence drug stability. It might also be used for stability predictions and potentially for the optimization of the extent of stability testing needed to determine shelf life and storage conditions, which would be time and cost-effective for the pharmaceutical industry.

Optimization of UPLC Method for Simultaneous Determination of Rosuvastatin and Rosuvastatin Degradation Products.

An ultra-performance liquid chromatographic method for simultaneous determination of rosuvastatin and rosuvastatin degradation products was developed and optimized by using fractional factorial experimental design. Optimized method is capable to accurately determine all potential degradation products of rosuvastatin. During the optimization the effect of four chosen chromatographic factors was evaluated. The analytical method operational design region was modeled using Umetrics MODDE software and optimal chromatographic conditions were predicted. The results of the model show that the most important factors to reach good separation between the peaks of rosuvastatin impurities are the pH of buffer solution and the amount of ACN and THF in the mobile phase. The final optimized method using QbD approach was validated for linearity, accuracy and precision for determination of rosuvastatin and rosuvastatin degradation products in rosuvastatin pharmaceutical dosage forms. Limit of detection and quantification were determined for two known specified impurities. The use of experimental designs enabled us to obtain the maximum amount of information about the analytical method design region. Optimization of the method was done without additional experiments, only weighing the responses and rebuilding the statistical model. This approach is very cost-effective when evaluating a variety of different factors and their interactions.

Quality by design based optimization of a high performance liquid chromatography method for assay determination of low concentration preservatives in complex nasal formulations.

The effects of seven different chromatographic parameters and five sample preparation parameters in a high performance liquid chromatography (HPLC) method for assay determination of benzalkonium chloride (BKC) in a nasal formulation were evaluated using two fractional factorial experimental designs. The design space of the analytical method was modeled using Umetrics Modde software and the optimal method conditions were predicted. The optimum HPLC chromatographic conditions were obtained using a Luna CN column (150 x 4.6 mm, 3 µm). The results show that mobile phase pH, amount of acetonitrile in the mobile phase and column temperature are the most important factors in obtaining good separation of BKC homologs from an interfering peak. In the sample preparation step, the use of an aqueous solution for dissolving the samples was the most important factor since it eliminated the interfering effect of the active compound. The optimal method was validated for linearity, accuracy and precision. The use of experimental designs enables obtaining the maximum amount of information with the least possible number of experiments. Such designs are an economical manner in evaluating a variety of different factors and their interactions.

Using different experimental designs in drug-excipient compatibility studies during the preformulation development of a stable solid dosage formulation.

Different types of factorial experimental designs can be used in compatibility studies of drug development, where many different factors and their interactions should be evaluated to predict their effects on the degradation of the drug substance under study. All possible main and interaction effects of different potential excipients that can constitute the drug product should be evaluated in order to select the best combination of excipients that give the lowest possible degradation, i.e., the most stable drug product. Statistical experimental designs enable the user to obtain the maximum amount of information, i.e., the degradation effects of excipients and their interactions on the stability of the drug substance, on the basis of the smallest possible number of experiments. The use of full and two different fractional factorial designs is described using a real example where the excipients that stabilize the drug substance or cause as little degradation as possible are selected for a solid dosage formulation. It was shown that the type and the sequence of design used during the studies are also important to get reliable and valuable results. A thorough explanation of the statistical evaluation of data and different presentations of final solutions are given.

Predicting drug hydrolysis based on moisture uptake in various packaging designs.

An attempt was made to predict the stability of a moisture sensitive drug product based on the knowledge of the dependence of the degradation rate on tablet moisture. The moisture increase inside a HDPE bottle with the drug formulation was simulated with the sorption-desorption moisture transfer model, which, in turn, allowed an accurate prediction of the drug degradation kinetics. The stability prediction, obtained by computer simulation, was made in a considerably shorter time frame and required little resources compared to a conventional stability study. The prediction was finally upgraded to a stochastic Monte Carlo simulation, which allowed quantitative incorporation of uncertainty, stemming from various sources. The resulting distribution of the outcome of interest (amount of degradation product at expiry) is a comprehensive way of communicating the result along with its uncertainty, superior to single-value results or confidence intervals.