(547f) Predictive Residence Time Distribution Models for Continuous Powder Blending

Continuous manufacturing (CM) is an emerging technology with potential to increase the efficiency, flexibility, agility, and robustness of pharmaceutical manufacturing. The U.S. Food and Drug Administration (FDA), along with other regulatory authorities, has taken multiple steps to support the implementation of CM [1]. This includes the recent finalization of the quality guideline on CM of drug substances and drug products from the International Council for Harmonization (ICH) [2]. Knowledge of process dynamics is essential for identification and mitigation of risks to product quality for continuous processes. In this regard, characterizing the residence time distribution (RTD) of a flow process offers powerful insight into the process. The RTD can be used to trace the flow and dispersion of material through a continuous process. Development of a traceability algorithm for the RTD, also known as an RTD model, enables prediction of how disturbances propagate through the process, allowing for isolation/diversion of out-of-specification material. Given that RTD models used for feed-forward process control may be considered medium-impact, properly accounting for the effects of material properties and process parameters in such models is critical to ensure product quality in CM processes.

In the current work, predictive models for the RTD of an API in a commercial scale continuous powder blender were developed at two different API loadings (5% and 30% w/w). For each drug loading, the developed RTD model considered the effects of total flow rate, blender speed, and bulk material properties (e.g., bulk density). The influence of powder flow rate on the robustness of the near infrared spectroscopy (NIR) calibration models associated with each drug loading was also investigated. Acetaminophen with semi-fine particle size was used as the model API, while the bulk material consisted of two commonly used tablet fillers (lactose and microcrystalline cellulose). The impacts of bulk material and total flow rate were assessed via adjusting flow rate of the individual components fed into the blender. In all cases, the composition of the exiting blend was monitored in-line via NIR and confirmed via off-line HPLC measurement. The predictive RTD models developed for each API loading were then validated against independent blending runs to assess their performance. Although reasonable predictability was observed for both API loadings, the 5% API model exhibited better performance. Additionally, a series of challenge runs involving different particle sizes of API were performed to test the robustness and accuracy of the RTD model in different particle size ranges (d₅₀s of 9.0 and 130 µm). The RTD model exhibited better predictive performance for blends made using smaller API particle size than blends made using larger API particle size.

Disclaimer: This abstract reflects the views of the authors and should not be construed to represent FDA’s views or policies.

1. Lee, S.L., et al., Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. Journal of Pharmaceutical Innovation, 2015. 10(3): p. 191-199.
2. ICH, Continuous Manufacturing of Drug Substances and Drug Products Q13. 2022, International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use.