Reassessing adult surfactant replacement therapy with mechanics-informed reinforcement learning
Abstract
Surfactant replacement therapy (SRT) remains clinically limited to neonatal applications, in part because the mechanical feasibility of achieving efficient delivery in adult lungs is poorly understood. Previous computational studies have largely been descriptive or based on parameter sweeps, providing limited guidance on how to design efficient adult protocols under anatomical constraints. Here, we introduce a computational framework that integrates mechanistic modeling of surfactant propagation in anatomically motivated airway trees with deep reinforcement learning (DRL) to identify efficient delivery strategies across scales and airway geometries. The approach leverages a reduced-order model of plug transport and redistribution that captures the key mechanics of surfactant coating in complex airway networks while remaining lightweight enough for large-scale optimization. A custom DRL agent autonomously explores delivery parameters—including aliquot volume, flow rate, patient posture, and surfactant properties—to optimize protocol-level performance across diverse anatomical and physiological conditions. Under the branch-level coverage-based reward adopted here, systematic optimization of delivery parameters improves distal delivery at both pediatric and adult scales, while clarifying how these gains depend on prescribed volume, airway asymmetry, and control complexity. Increasing the number of aliquots improves access to distal regions and, in favorable geometries, shifts the onset of high-coverage regimes toward lower prescribed volumes, whereas posture becomes especially informative in asymmetric trees. Extending the control space to include surfactant rheology provides an additional lever, particularly in constrained adult settings, but does not overcome the structural limitations imposed by strong geometric asymmetry. Overall, these results establish a physics-based framework for AI-assisted optimization of intrapulmonary liquid delivery and clarify the respective roles of dose partitioning, posture, and formulation tuning. They also show that the interpretation of delivery success depends strongly on the evaluation metric: branch-level coverage provides a functionally oriented measure across heterogeneous airway trees, whereas stricter homogeneity metrics yield substantially more pessimistic assessments, especially in adult asymmetric geometries. These findings do not predict clinical efficacy; rather, they provide a controlled mechanistic feasibility benchmark and testable design hypotheses within physiologically realistic bounds.
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BibTeX
@article{meliga2026reassessing,
title = {{Reassessing adult surfactant replacement therapy with mechanics-informed reinforcement learning}},
author = {Philippe Meliga and Gregor Roncin and Alejandro Yepes Peñaranda and Elie Hachem},
journal = {PLoS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014629},
url = {https://hal.science/hal-05749214v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Reassessing adult surfactant replacement therapy with mechanics-informed reinforcement learning
AU - Philippe Meliga
AU - Gregor Roncin
AU - Alejandro Yepes Peñaranda
AU - Elie Hachem
PY - 2026
DA - 2026/09/14
JO - PLoS Computational Biology
DO - 10.1371/journal.pcbi.1014629
UR - https://hal.science/hal-05749214v1
AB - Surfactant replacement therapy (SRT) remains clinically limited to neonatal applications, in part because the mechanical feasibility of achieving efficient delivery in adult lungs is poorly understood. Previous computational studies have largely been descriptive or based on parameter sweeps, providing limited guidance on how to design efficient adult protocols under anatomical constraints. Here, we introduce a computational framework that integrates mechanistic modeling of surfactant propagation in anatomically motivated airway trees with deep reinforcement learning (DRL) to identify efficient delivery strategies across scales and airway geometries. The approach leverages a reduced-order model of plug transport and redistribution that captures the key mechanics of surfactant coating in complex airway networks while remaining lightweight enough for large-scale optimization. A custom DRL agent autonomously explores delivery parameters—including aliquot volume, flow rate, patient posture, and surfactant properties—to optimize protocol-level performance across diverse anatomical and physiological conditions. Under the branch-level coverage-based reward adopted here, systematic optimization of delivery parameters improves distal delivery at both pediatric and adult scales, while clarifying how these gains depend on prescribed volume, airway asymmetry, and control complexity. Increasing the number of aliquots improves access to distal regions and, in favorable geometries, shifts the onset of high-coverage regimes toward lower prescribed volumes, whereas posture becomes especially informative in asymmetric trees. Extending the control space to include surfactant rheology provides an additional lever, particularly in constrained adult settings, but does not overcome the structural limitations imposed by strong geometric asymmetry. Overall, these results establish a physics-based framework for AI-assisted optimization of intrapulmonary liquid delivery and clarify the respective roles of dose partitioning, posture, and formulation tuning. They also show that the interpretation of delivery success depends strongly on the evaluation metric: branch-level coverage provides a functionally oriented measure across heterogeneous airway trees, whereas stricter homogeneity metrics yield substantially more pessimistic assessments, especially in adult asymmetric geometries. These findings do not predict clinical efficacy; rather, they provide a controlled mechanistic feasibility benchmark and testable design hypotheses within physiologically realistic bounds.
SN - 1553-734X
SN - 1553-7358
ER -