Cal McLennan1, Jakob Richter2, Laura Burton1, Nicolas Schwenck2, Ben Wade, Peter Harley1, Karl-Robert Wichmann2, Jonas Biehler2 & Kei W. Müller2
1Crux Product Design Limited, Flatiron Building, 332-336 Paintworks, Bristol, BS4 3AR, UK
2Ebenbuild GmbH, Holzstr. 28, 80469 Munich, Germany
Summary
The precision of aerosol deposition prediction in inhaled drug delivery is advancing through the integration of in-vitro experimentation and in-silico modelling. Building on the foundational work of Grill et al. [4], who demonstrated that the ability to predict dose deposition at the local site of action enables optimisation of aerosol and inhalation parameters, we present a novel data-fusion workflow linking physical aerosol testing with high-resolution human lung simulations in a time-resolved, geometry-matched manner. This collaboration between Crux Product Design Ltd. and Ebenbuild GmbH centres on high-dose, high-velocity aerosols delivered via the RS01 Dry Powder Inhaler (DPI), representative of a delivery device for the next generation of low-potency pulmonary therapies. Anatomically representative upper airway induction ports were derived from patient CT data, 3D printed, and integrated with a Malvern Spraytec system to enable real-time Particle Size Distribution (PSD) measurement during an inhalation event.
Tests were performed at 2 kPa and 6 kPa pressure drops using sieved Lactohale 300 lactose powder in a healthy airway model. Transient PSD and mass flow data were provided to Ebenbuild’s human lung digital twin platform, Twinhale®, to simulate full-lung deposition outcomes using a patient-specific in silico model derived from the same CT dataset from which the anatomical airway induction port was created.
Results show that the anatomically representative upper airway geometry meaningfully influences the PSD and increases the Fine Particle Fraction (FPF), with downstream effects on predicted alveolar and regional lung deposition. In-silico analysis showed that higher flow rates, despite producing a smaller PSD, may increase conducting airway deposition due to greater particle inertia. By linking in-vitro and in-silico datasets, this study demonstrates an enhanced end-to-end methodology that could enable more effective and efficient optimisation of formulation, device and inhalation parameters to support future tuned and targeted inhalation therapies. It is plausible to consider that insights gained from this methodology could unlock new human-device interaction insights which could enable future novel drug-device design, including advanced design considerations for different disease states combined with disease severity and cohort. The approach may also enable future reductions in design and development timescales by potentially reducing experimental campaigns such as impactor testing, enabling a faster future pathway to human trials.