MEDIAN-BASED ROBUST REGRESSION METHODS IN PREDICTION OF DRUG STABILITY

The classical isothermal approach for the prediction of drug stability exploits least squares regression. In this paper the use of some robu st regression techniques to estimate the rate constants at different t emperatures has been evaluated. These techniques are able to give accu rate estimates when data are contaminated by the presence of outliers. The successful application of two robust methods, single median and r epeated median, to real stability data from the literature is shown. M oreover, the authors have modified the original methods in order to ap ply them to data sets with replicates, typical of stability studies. T he performances of the modified techniques have been investigated with simulated data sets containing outliers and with real data. They appe ar suitable for preliminary stability studies, especially on solid dos age forms. For a quick implementation of these methods, macroprograms written for a widely used spreadsheet are reported.