Acetil-szalicilsav porok és tiszta hatóanyagot tartalmazó kapszulák kioldódásának jóslása a szemcseméret-eloszlás ismeretében Predicting the dissolution of acetylsalicylic acid powder and capsules with pure active ingredients depending on the particle size distribution

In pharmaceutical manufacturing, the idea of non-destructive and real-time testing of raw materials and drugs is receiving increasing attention due to the implementation of continuous manufacturing. There is ample evidence in the literature that the particle size of an active ingredient significantly affects its dissolution rate. However, first-principles-based models have been used so far, instead of including robust, data-rich empirical models. Although first-principles models can provide adequate prediction for simple problems, the empirical approach could have several benefits when complex formulations are studied. In my work, I developed an empirical model that aimed to predict the dissolution of acetylsalicylic acid from the particle size distribution (PSD). For this purpose, the PSD of different acetylsalicylic acid powder fractions and mixtures was measured, and the dissolution curve of the powder mixture and capsules was studied. I compared and optimized different methods (Artificial Neural Networks, PLS regression) depending on the bin number and type of the PSD (i.e. numberand volume-based). The results could contribute to establish a good modeling approach when the effect of PSD on the dissolution of more complex formulations (e.g. tablets) needs to be modeled.