Abstract Pharmaceutical compounding of capsules is still an important corner stone in today's health care. It allows for a more patient specific treatment plan as opposed to the “one size fits all”-approach, used by the pharmaceutical industry when producing fixed dose finished drug products. However, loss of active pharmaceutical ingredient (API) powder during pharmaceutical capsule compounding can lead to under-dosed finished drug products and annul the beneficiary therapeutic effects for the patient. The amount and location of API loss was experimentally determined during capsule compounding of five different preparations: 10 and 20mg hydrocortisone capsules, 4mg triamcinolone capsules and 0.25mg dexamethasone capsules, using a 10% m/m self-made or commercial trituration. The total API amount present in the five capsule preparations varied between 90.8% and 96.6%, demonstrating that for certain preparations, significant API mass loss occurred during the pharmaceutical compounding of capsules. Swabbing results of the different compounding equipment and working areas indicated the mortar surface as the largest API loss location. An agate mortar accounted for the least amount of API loss, whereas an extensively used porcelain mortar accounted for the highest amount of API loss. Optical microscopy and roughness (Ra) determination by profilometry of the different mortar surfaces revealed a significant influence of the mortar surface wear and tear on the observed API loss. This observation can be explained by physical deformation, or scratch formation, of the relatively soft porcelain mortar surface, in which the API particles can become adsorbed. Furthermore, a small effect of the capsulation device material on the API loss was also observed. The presence of a chemical molecule effect on the API loss was demonstrated through data mining using a set of assay results containing 17 different molecules and 1922 assay values. The 17 median assay values were modeled in function of corresponding molecular descriptors, using stepwise multiple linear regression. The obtained MLR model, containing RDF060m, R6e+ and R3m+ variables, explained 92.5% of the observed variability between the 17 median assay values. More...
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