Data mining techniques applied in the analysis of historical data

Abstract

Understanding the effect of the characteristics of formulation and process parameters on the physicochemical properties of a pharmaceutical product is very significant for the development of solid dosage forms, as the knowledge gained on small scale batches in the early phase of development is used in the later phases of product lifecycle or in the development of other products. One of the approaches for gaining a better understanding of the effects of the formulation and production process on the quality of the finished product is to apply a systematical approach which concerns performing experiments according to a predefined factorial or fractional factorial experimental plan. However, often it is the case that there are available data gathered in a non-systematic way, because experiments were not performed according to a predetermined experimental plan. In such a case, data mining techniques could be used to extract useful data from the historical data set. In this research, the possibility of using several data mining techniques to build models that describe the effect of formulation characteristics on acid resistance and dissolution profile of a model drug from gastro-resistant pellets. The model drug used in the research is duloxetine hydrochloride from the group of antidepressants. It belongs to the BCS 2 class of active pharmaceutical ingredients, and it is therefore necessary for the release profile of duloxetine to be characterized by a higher number of tested time points. The developed models can be used for planning future laboratory trials, or in the development of other products.