RESUMO
Due to the complexity in the interactions of variables and mechanisms leading to blend segregation, quantifying the segregation propensity of an Active Pharmaceutical Ingredient (API) has been challenging. A high-throughput segregation risk prediction workflow for early drug product development has been developed based on the dispensing mechanism of automated powder dispensing technology. The workflow utilized liquid handling robots and high-performance liquid chromatography (HPLC) with a well-plate autosampler for sample preparation and analysis. Blends containing three different APIs of varying concentrations and particle sizes of different constituents were evaluated through this automated workflow. The workflow enabled segregation evaluation of different API blends in very small quantities (~7g) compared to other common segregation testers that consume hundreds of grams. Segregation patterns obtained were well explained with vibration induced percolation-based segregation phenomena. Segregation risk was translated quantitatively using relative standard deviation (RSD) calculations, and the results matched well with large-scale segregation studies. The applied approach increased the throughput, introduced a simple and clean walk-up method with minimized equipment space and API exposures to conduct segregation studies. Results obtained can provide insights about optimizing particle size distributions, as well as selecting appropriate formulation constituents and secondary processing steps in early drug product development when the amount of available API is very limited.
Assuntos
Química Farmacêutica , Tecnologia Farmacêutica , Excipientes , Pós , TecnologiaRESUMO
The segregation of an active pharmaceutical ingredient (API) within a powder blend is one of the major manufacturing obstacles in achieving content uniformity. Segregation can be due to differences in physicochemical properties of formulation components and/or perturbations experienced during secondary processing steps, such as granulation, fluidization, die-filling and compression. A near-infrared (NIR) spectrometer embedded segregation tester, which could mimic the external stimulations (vibration and fluidization) experienced by a blend in a manufacturing facility, was used to evaluate and predict blend segregation. Two different GlaxoSmithKline (GSK) product blends with variations in the API particle size and concentration were tested. Drug content was further measured at different locations along the powder bed by NIR to sketch the segregation profile and calculate the overall segregation intensity of each blend. The study indicated that the segregation potential was dependent on the particle sizes of API and excipients, as well as the type of stimulus applied (vibration vs fluidization). Drug concentration profiles obtained from this mode of analysis decoded the underlying segregation mechanisms (sieving, trajectory and air elutriation) easily. The employed NIR-based segregation tester proved to be a useful small-scale predictive tool to evaluate and rank the segregation risk of the studied pharmaceutical blends.
