New Formula for the Hydrogen-Bonding Hansen Component of Methanol, Ethanol, and n-Propanol for Non-ambient Conditions-Application in Gas Antisolvent Fractionation-Based Optical Resolution.

Optical resolution by diastereomeric salt formation based on gas antisolvent fractionation is influenced by the chemical equilibrium of the salt formation, the solubility, and the extraction of the compounds. Selectivity, also known as resolution efficiency, is highly solvent-dependent and is also affected by process parameters both in atmospheric and supercritical processes. For the first time in the literature, a mathematical model that employs all three Hansen parameters and operating parameters is constructed to describe the selectivity of a gas antisolvent fractionation process. The satisfying goodness of fit of the models suggests that the outcome of the three subprocesses in the gas antisolvent fractionation process (i.e., salt formation reaction, precipitation, and extraction) can be described in a single model. A new formula for pressure and temperature correction of the hydrogen-bonding component of the Hansen parameter for non-ambient conditions for liquid methanol, ethanol, and n-propanol is also suggested in this paper.

Regression control chart with unknown parameters for detection of out-of-trend results in pharmaceutical on-going stability studies.

The main purpose of on-going stability studies in pharmaceutical industry is to test whether the regulatory requirement regarding the shelf life is fulfilled when the drug is already produced for patients. That is, the estimated shelf life of the products agrees with the shelf life claimed during the registration of the drug. The monitored process is the change in certain attributes (e.g. pH) of the drug products over time and the estimated shelf life for a given batch is determined based on the results of the monitoring. Out-of-trend (OOT) data points in stability studies distort the estimated trend, which results in distorted estimation of shelf life. Undetected OOT points could lead to overestimated shelf life, which may results in drug products with non-acceptable quality, continuously distributed to patients. In this paper, the regression control chart method is adapted for pharmaceutical stability studies to detect OOT points within the studies. The challenge is that the sample size is small. Usually, in a study the collected data is limited to 8-10 points. The parameters (true regression line and residual variance) of the process cannot be taken as known and the uncertainty of the estimated parameters is rather large. Therefore, the generally used regression control chart that utilizes the Shewhart method in which the parameters are known, may not be used. The proposed method in this paper takes the uncertainty of the parameters into account. Also adaptations of the proposed method for different ANCOVA models - such as a) every batch has the same true intercept and slope, b) every batch has the same slope but the intercepts differ, c) batches have different intercepts and slopes as well - are presented. When the proposed method employs the information obtained from the ANCOVA test, the statistical power of OOT detection is increased, i.e. the OOT detection is more effective.