Beyond depolymerization rates: Health‑aligned benchmarking for PET‑degrading enzymes in enzyme and microbial technology

Abstract

Polyethylene terephthalate (PET) hydrolases are commonly benchmarked according to depolymerization efficiency, monomer yield, and reaction kinetics. Recent advances—including anchor-peptide fusion strategies and computationally engineered enzymes—have substantially improved catalytic performance under laboratory and industrially relevant conditions. However, performance-focused benchmarking alone does not capture how enzymatic degradation may influence particle fate, transient fragment formation, or downstream process compatibility. Here, we propose an exposure-aligned benchmarking framework that complements conventional depolymerization metrics by integrating physicochemical indicators linked to potential human and environmental exposure pathways. The framework is structured around three tiers representing inhalation-relevant PET microfibres, ingestion-relevant packaging fragments, and industrial post-consumer PET feedstocks. For each tier, we outline harmonized reporting parameters including crystallinity, surface aging, particle-size distribution trajectories, and downstream compatibility with filtration and monomer-purification steps. Importantly, the framework does not assume adverse health outcomes from enzymatic recycling products. Instead, it provides standardized descriptors that enable comparison of particle behavior across enzyme systems, substrates, and reaction conditions. We further clarify the mechanistic role of polymer crystallinity in defining residual fragments and highlight experimental gaps requiring matched-yield comparisons between engineered and wild-type enzymes. By aligning enzyme benchmarking with exposure-relevant physicochemical metrics—without imposing routine toxicological testing—this approach supports reproducibility, industrial relevance, and responsible innovation in PET biocatalysis. Adoption of such complementary benchmarks may facilitate the transition from rate-driven optimization toward decision-grade evaluation of next-generation PET-degrading enzymes.