Fecha de publicación: -- Fuente: PubMed "hive" Int J Pharm. 2026 Sep 27:127407. doi: 10.1016/j.ijpharm.2026.127407. Online ahead of print.ABSTRACTTablet capping is a critical manufacturing defect whose early prediction is essential to prevent product failures and process interruptions. This study investigated the processability and capping tendency of fluidized bed melt granules prepared according to a Design of Experiments (DoE), integrating conventional granule characterization, compression analysis, machine vision, and multivariate data analysis. Capping was observed in 6 of 15 formulations, predominantly, but not exclusively, in those processed at lower temperatures with capping onset typically occurring at the higher end of the applied compression pressure range. An OPLS-DA model (Q2 = 0.615) discriminated capping from non-capping formulations based on granulation process variables. Hierarchical cluster analysis on compression-derived parameters identified three distinct groups: the failure-free group exhibited lower yield pressure, higher tensile strength (mean 2.29 MPa vs. 1.44 MPa in the capping group), lower detachment stress (5.90 MPa vs. 7.34 MPa), and reduced ejection forces. Process optimisation identified 16.2% Macrogol and a granulation temperature of 63 °C as the robust setpoint, within a design space characterised by only 1.4% probability of failure. Machine vision, while unsuitable for precise particle sizing, demonstrated good capacity to indirectly estimate flowability through agglomerate detection, yielding predictive models for bulk density and angle of repose (Q2 > 0.9). OPLS-DA models on machine vision data discriminated capping from non-capping batches with Q2 of 0.99 and 0.98, respectively. A data fusion approach further enabled accurate prediction of tablet breaking force and disintegration time, supporting feed-forward process control in pharmaceutical manufacturing.PMID:42801997 | DOI:10.1016/j.ijpharm.2026.127407