Investigation of mixing time and relative concentration effects in adhesive mixtures for drug inhalation

University essay from Lunds universitet/Ergonomi och aerosolteknologi

Abstract: A common approach to treat respiratory diseases is with adhesive powder mixtures. These are dispersed into an aerosol upon inhalation. It has been found that the performance of such mixtures can be improved by adding fine particle lactose, but exactly why is yet to be established. Several theories have been proposed by previous studies, but the fundamental mechanism is still unknown. This study was done in order to examine the impact of lactose fines (LF) in adhesive mixtures. This was approached by manufacturing different formulations using a high shear mixer. The concentration of added fines was kept at a constant 8% for all batches, whereas the relative concentration between the active pharmaceutical ingredient (API) Beclomethasone Dipropionate (BDP) and LF was varied systematically. Two sets of formulations were made, one containing a force control additive (1% Magnesium Stearate), referred to as coated formulations, whereas the other contained no such additive, referred to standard formulations. Several samples were extracted at different time points during the mixing stage in order to get a better understanding on how the mixing process influences the dispersion properties. The aerodynamic particle size distributions of the API and LF were assessed using a Next Generation Impactor (NGI), which is a widely practiced technique within pharmaceutical development work. Quantification of BDP component was done using a well-developed method consisting of an ultra-performance liquid chromatography -system and an internal standard solution. However, this technique is not suitable to quantify the LF component. Overall, the analysis of lactose intended for inhalation is poorly described in literature. Therefore, one objective of this thesis was to find and evaluate such method. Two techniques were tried out; the first one being LC-PAD system based on electrochemical analysis. This was however disregarded due to instrumental errors and instead, a high pressure liquid chromotography system coupled with a mass spectrometer was used. However, this technique needs more tuning to be as good as the one used for BDP, as the assessed results did sometime display unreasonably high values and it is still unclear how much impact the carriers have on the analysis. Regarding the performance of the manufactured batches, it was observed that the Fine Particle Fraction (FPF) for the coated formulations were consistently higher than for the standard formulations, as was expected. The coated formulations did also show a dependency on the mixing process as regards the FPF values. No such effect could be seen for the standard formulations. It was also found that the FPF of LF were consistently higher than the FPF of BDP. The FPF of BDP was consistently higher for formulations with a higher amount of LF. It was proposed that the LF and BDP form co-agglomerates during mixing, which lies in agreement with one of the existing theories that explains the behavior of LF. The NGI is, despite yielding high quality results, a rather time consuming method which bottlenecks the pharmaceutical development process. A faster method to assess the PSD of an adhesive mixture is by the usage of Laser Diffraction (LD). This technique is however chemically non-specific, which render analysis of adhesive mixtures including both LF and API a bit troublesome. It would be favorable for future work if there was a way of correcting for LF when analyzing using LD, and a second part of this thesis was thus to evaluate how well data assessed from the NGI correlated with data assessed with LD setups. It was found that it is possible to screen a formulation using LD if an appropriate dispersion method is used. However, in order to correct for LF in LD measurements, more research is needed as the LF seems to disperse differently in different formulations. Formulations with only LF as added fines correlated well with data assessed by the NGI.

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