Authos: Marwa J. Mohammed
Abstract
Pharmaceutical manufacturing relies on powders, granules, and agglomerated materials whose particle-scale interactions can determine blend uniformity, feeding stability, die filling, compaction, coating, milling, and aerosol performance. The Discrete Element Method (DEM) offers direct access to these interactions, but its engineering value depends less on visualization than on how convincingly model parameters are calibrated, transferred, and validated against process- or product-relevant measurements. This critical review evaluates DEM across powder handling, continuous and batch blending, feeding, die filling, granulation, milling, tablet compaction, coating, and pulmonary drug delivery. Building on earlier application-focused and calibration-oriented reviews, the present review applies an evidence-oriented framework that asks whether material characterization is linked to calibration, whether numerical verification and independent validation are documented, whether numerical scaling preserves the relevant physics, and whether the final comparison is made against a meaningful pharmaceutical endpoint. Studies reviewed from 2020-2026 increasingly report production-scale blending, experimentally validated continuous processing, calibrated compression models, DEM-based coating prediction, digital-twin workflows, and CFD-DEM analysis of dry-powder inhalers. At the same time, parameter non-uniqueness, coarse-graining, cohesion, computational cost, and weak transferability between formulations and operating conditions remain major barriers to predictive use. DEM is therefore most useful when it is embedded in experimentally anchored, uncertainty-aware multiscale workflows that connect particle-scale mechanisms with measurable critical quality attributes and dose-delivery outcomes.
Keywords: Discrete element method; pharmaceutical engineering; tablet manufacturing; die filling; powder blending; tablet coating; dry powder inhaler; CFD-DEM.