Quantification of Microplastics from Primary Drug Packaging Materials

Background: Microplastics (MPs) have emerged as an environmental and human health concern, yet their presence in primary pharmaceutical packaging remains poorly quantified. Packaging materials such as polyvinyl chloride (PVC), polyethylene terephthalate (PET), polypropylene (PP), and high-density polyethylene (HDPE) may shed MPs under mechanical, thermal, and photolytic stress during manufacturing, transport, and storage, posing risks of drug contamination and downstream environmental release. Objectives: This study aimed to develop and validate a standardized, multi-modal analytical framework to detect, quantify, and characterize MPs released from pharmaceutical primary packaging under realistic stress conditions. Methods: Five common packaging types (PVC/aluminum blisters, PET syrup bottles, HDPE bottles, PP closures, and cyclic olefin polymer [COP] vials) were exposed to combined thermal cycling, mechanical vibration, and UV aging. Released particles were isolated using hybrid enzymatic–oxidative digestion, analyzed for size and morphology via stereomicroscopy and SEM, chemically identified by µFTIR and Raman spectroscopy, and quantified in mass by pyrolysis–GC/MS. Automated particle classification and predictive modeling were performed using support vector machines (SVM) and gradient boosting regression to reduce operator bias and predict release potential. Results: PVC blisters and PET bottles released the highest number and mass of MPs (2.8 × 10⁵ particles/m² and 124 µg/m², respectively), while COP vials showed minimal fragmentation. PET and HDPE generated the greatest fraction of sub-10 µm particles, highlighting potential patient exposure risks not addressed by current pharmacopeial particulate testing. Combining µFTIR, Raman (with shifted excitation), and Pyr-GC/MS enabled comprehensive particle identification and mass quantification. SVM-based image analysis improved detection of fine particles by ~20% compared with manual counting, and gradient boosting regression predicted release potential with R² = 0.92. Conclusions: This work establishes a reproducible, GMP-compatible workflow for quantifying MPs from drug packaging and demonstrates significant release potential from widely used PVC and PET materials. The findings support the need to expand particulate matter specifications to include sub-10 µm plastics and to adopt dual reporting of particle count and polymer mass. The proposed approach also enables predictive material screening, informing the design of low-fragmentation, sustainable packaging and aligning with emerging regulatory and environmental safety frameworks.