Optimization of Electrospun PCL-PLGA Substrates for Lung Basement Membrane Applications Using Response Surface...

Electrospun membranes are widely used in tissue modeling because their tunable fibrous architecture can capture key structural features of the extracellular matrix of native tissues. For functional in vitro lung barrier models, membrane substrates should ideally combine stability and reproducibility with basement-membrane-relevant features, including fine fibers, reduced stiffness, and sufficient strength and extensibility to tolerate breathing-mimetic deformation. Here, poly-(ε-caprolactone) (PCL)-poly-(lactic-co-glycolic acid) (PLGA) blends were optimized to develop fully synthetic electrospun membranes that balance these competing structural and mechanical requirements. Response surface methodology (RSM) was first applied to a Box-Behnken electrospinning design, in which fiber diameter, Young's modulus, ultimate tensile strength (UTS), and strain at break were treated as coequal responses. The model predicted membranes with a fiber diameter of 0.386 ± 0.004 μm, Young's modulus of 152.156 ± 1.751 MPa, UTS of 41.850 ± 0.149 MPa, and strain at break of 2.948 ± 0.020, with confirmatory experiments deviating by less than 10% from predicted values. Machine learning (ML) extended this experimentally anchored framework by adding validated augmentation, inverse property-to-parameter prediction, and multiresponse desirability optimization. A six-generator fidelity screen selected Gaussian-Copula augmentation over WGAN-GP for downstream modeling, while leakage-free leave-one-real-observation-out evaluation showed that CatBoost most robustly recovered the polymer-composition targets, with (leave-one-out) LOO R ² values of 0.675 for PCL concentration and 0.747 for PLGA concentration. XGBoost forward-surrogate optimization achieved the highest model-derived desirability at 0.895 ± 0.065, compared with 0.782 for the experimentally confirmed design of experiments reference. Overall, the workflow demonstrates that ML can complement RSM by prioritizing candidate electrospinning windows, quantifying uncertainty, and clarifying property-to-parameter coupling in multiresponse fibrous membrane design.