Real-time monitoring of aluminum phosphate sedimentation and resuspension using electrical resistance tomography.

Authors

Riccardo Pellicari, Federico Alberini, Alessandro Paglianti, Stephen L J Luckham, Carlo Pergola, Andrea Albano

Published in

International journal of pharmaceutics. Pages 127503. Oct 03, 2026. Epub Oct 03, 2026.

Abstract

Aluminum phosphate (AP) is widely used as a vaccine adjuvant, but its tendency to sediment and form consolidated layers can affect suspension homogeneity during manufacturing, transfer, storage, and filling operations. This work investigates Electrical Resistance Tomography (ERT) as a non-invasive Process Analytical Technology for real-time monitoring of AP sedimentation and resuspension in a stirred tank at formulation-relevant concentrations below 1% v/v. A custom-designed bottom linear ERT probe was used to detect conductivity changes associated with solids accumulation near the vessel bottom and clarification of the bulk liquid phase. Rheological characterization showed that AP suspensions become increasingly structured and shear-sensitive with increasing concentration, supporting the interpretation of sedimentation as a concentration-dependent, hindered-settling process. ERT measurements enabled visualization of sediment formation, identification of different settling regimes consistent with Kynch theory, and extraction of apparent settling velocities. Complete settling times decreased markedly with dilution, from approximately 180 min at 1 mg/mL to 55 min at 0.5 mg/mL and 10 min at 0.25 mg/mL. The ERT-derived sedimentation profiles showed good agreement with optical measurements and literature data obtained under comparable conditions. These results demonstrate that ERT can provide process-relevant, spatially resolved information on suspension homogeneity, sediment growth, and resuspension dynamics in aluminum-adjuvanted vaccine systems. The approach may support formulation development, agitation strategy definition, and real-time process control for vaccine manufacturing.

PMID:
42829012
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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