The Brunaugh Lab studies the rules of transport in complex, dynamic environments — biological barriers, formulated solid-state systems, and the cryopreservation landscape — and engineers therapeutic interventions that exploit them.
These three domains look different on the surface. A drug navigating airway mucus, a droplet drying into a respirable particle, and a cell surviving a freeze-thaw cycle are the kinds of problems that usually live in separate fields, separate journals, separate labs. We study them together because the underlying physics is the same. What changes is the system and the scale of observation, not the logic.
The shape of every problem
A perturbation propagates through a structured medium, and the outcome depends on what the medium does to it.
A drug enters mucus and is sorted by charge, hydrophobicity, and mesh size into wells, channels, and dead ends. A solution droplet dries into a solid particle whose internal architecture is determined by how fast water leaves relative to how fast the polymer can rearrange. A cell exposed to subzero temperature survives or dies depending on whether water leaves the cytoplasm before ice nucleates inside it. Different systems, different stakes — but the question being asked of the physics is the same.
Recognizing this shape lets us reuse hard-won understanding across domains. A measurement we develop for mucus mechanics informs how we think about the drying droplet. A model we build for cryopreservation kinetics tells us where to look in a synergistic drug combination. The lab’s competitive advantage is not domain expertise. It is the ability to see the same logic in different systems and act on it.
Three constructs
The physics that determines whether a perturbation reaches its target reduces, across every problem we work on, to three composable ideas.
Timescale competitions
Which process wins the race.
In synergistic antibiotic combinations, two drugs must arrive at the bacterium within the same window for the synergy mechanism to engage. If the second drug is cleared from the airway before the first one acts, the combination collapses into two single-agent treatments, neither sufficient. Synergy is a timescale competition between co-exposure and clearance.
In cryopreservation, water has a finite time to leave the cell before ice nucleates inside it. The cooling rate sets the deadline; the membrane’s water permeability sets how fast water can escape. Cell survival is a timescale competition between dehydration and ice nucleation.
The mathematics is identical across these examples. A dimensionless ratio of competing timescales — a Damköhler number, a Péclet number, a Deborah number — tells you which regime the system is in and what outcome is possible.
Energy landscapes
Barriers are not uniform; topology determines what gets through.
In mucus, the landscape is built from the electrostatic charges of mucin glycoproteins, the hydrophobic patches contributed by lipid-associated regions, and the steric mesh of the polymer network. A cationic drug experiences this landscape as a series of deep wells; an anionic drug of the same size experiences it as nearly transparent. The lab develops measurement methods that resolve this topology at the scale where it operates — not the scale where bulk averages flatten it into a single number.
In a drying droplet, the landscape is itself dynamic. As water leaves, viscosity rises, polymer chains slow, and what was a fluid environment becomes a glassy one over the course of seconds. The architecture of the final particle — drug crystalline or amorphous, dispersed or phase-separated — is the imprint of that landscape evolving on a specific timescale.
Effective reach
A perturbation does not extend infinitely.
It is diluted by transport, sequestered by reversible binding, and lost to irreversible degradation. The distance over which it remains intense enough to produce an effect — its effective reach — is what determines whether it gets the work done.
When a synergistic drug combination is delivered to an infected lung, the active question is not “did both drugs reach the target tissue?” but “did the second drug reach the first one’s working volume before the first one was cleared?” Reach is what determines whether the combination behaves as designed or as two separate suboptimal monotherapies.
How this drives the work
These constructs are not abstractions we apply after the fact. They are how we set up problems from the beginning.
We build instruments at the scale where the physics operates — because bulk-averaged measurements collapse the very heterogeneity that determines outcomes. We design formulations across domains simultaneously rather than optimizing one property at a time — because every material choice propagates consequences across stability, transport, and biological activity at once. We engineer interventions that reshape landscapes rather than fighting them — because reshaping is more durable than overpowering.