A Dynamic Modeling Framework for Real-Time Monitoring of Porcine Lung Tissue Decellularization

Decellularized extracellular matrix (dECM) scaffolds are critical for tissue engineering and regenerative medicine, yet standardized methods for real-time monitoring of decellularization remain lacking. Current protocols typically use fixed treatment durations to match endpoint control quality attributes (CQAs), such as final dsDNA content in the scaffold, without a mechanistic understanding of DNA removal kinetics. This knowledge gap leads to either incomplete decellularization (CQA rejections) or unnecessary processing time, compromising dECM scaffold quality and process efficiency. In this paper, we develop a model-based mathematical monitoring framework for rapid assessment of DNase-treated porcine lung tissue that integrates dual-optical sensing of supernatant DNA release via ultraviolet-visible (UV-vis) absorbance and fluorescence. Decellularization supernatant was collected every 10 min over a 100-min treatment window in each sample group. Signals were well described by a first-order accumulation model C(t) = C0 + (Cmax - C0) (1 - e-kt), yielding an interpretable kinetic descriptor k and a model-based progress metric. A linear relationship was observed between the initial tissue mass and rate constant (R2 = 0.9998), enabling mass-aware prediction of release kinetics. When explored in dimensionless coordinates, normalized trajectories collapsed onto the universal curve f(τ) = 1 - e-τ that supports shared kinetics across sensing modalities. Model-derived in-process metrics were associated with endpoint residual tissue dsDNA quantified by QuantiFluor tissue digests and revealed an accessibility-limited gap between supernatant signal completion and tissue-level DNA removal within the treatment window. Together, these results provide the foundation of a practical process analytical technology style framework for quantitative decellularization monitoring and quality control, supporting mass-aware forecasting of process progress and adaptive protocol development.