Development and Scale-Up of Diversion Strategy for Twin Screw Granulation in Continuous Manufacturing.

Successful implementation of Continuous Manufacturing technology requires real time product quality monitoring that can result into rejection strategies for material manufactured outside process control limits. In a twin screw granulation process, parameters like water content, powder feed rate, and granulator screw speed can influence granule quality. Deviations in any of these parameters from the set-point may affect granule quality. Having a sound diversion strategy in place can help divert these implicated granules to waste. Residence time distribution experiments were conducted on a 16-mm Thermo Fisher twin screw granulator (TSG) for a range of process parameters, and the data was modelled to predict the needed diversion time as a function of process parameters. Scale-up from the 16-mm to 24-mm granulator was evaluated and data was found to scale based on mass per unit volume of granulator (channel fill), thus enabling 16-mm data to scale to 24-mm. The diversion strategy proposed is based on utilizing a wash out curve derived from residence time distribution to quantify the maximum concentration of implicated material that could be present in the next downstream unit operation(s) (e.g. a fluid bed dryer) and ensuring it is less than a suitable threshold to prevent product quality impact.

Kinetic Model Development for Accelerated Stability Studies.

Accelerated stability coupled with modeling to predict the stability of compounds, blends, and products at long-term storage conditions provides significant benefits in science-based decision-making throughout drug substance and drug product development. The study can often be completed, including data analysis in the space of three working weeks, and the information gathered and learning made in this time period can rival years of traditional analysis. The speed of the studies allows an earlier assessment of risk to quality enabling appropriate risk mitigation strategies to be implemented in a timely manner. The scientific foundation is based upon Arrhenius kinetic equations that can be linear or nonlinear in time, and can be based upon water vapor pressure or liquid water activity (relative humidity). A variety of kinetic models are evaluated, and the best model is chosen based upon both Bayesian information criteria and an automated assessment of kinetic model parameters fitting within acceptable ranges. Confidence intervals are estimated based upon a bootstrapping approach. Moisture vapor transmission rate models are applied on top of the resulting kinetic models in order to simulate different packaging types and the use of desiccant. The kinetic models are integrated with the prediction of packaging humidity over time to create a long-term prediction of impurities and other phenomena. The resulting models have been shown to be useful for not only the prediction of drug product impurities in long-term storage but other physical phenomena as well such as hydrate development and solvate loss.