Solutions
The right physics for every development decision.
We scope each engagement around a specific design or regulatory question — then deliver a validated answer and a calibrated digital twin you can reuse.
Drug delivery & LAIs
Drug delivery and long-acting injectables
Depot injections and implantable systems are governed by coupled mass transport, polymer degradation, and in-vivo fluid dynamics. We model the full release pathway — from formulation to PK — so you can predict in-vivo performance before committing to a batch.
Questions we answer
How will my depot formulation release over 30, 60, or 90 days in vivo?
What polymer molecular weight and loading fraction hit my target release profile?
How does injection site geometry affect early burst and steady-state release?
Can I link my in vitro dissolution data to a PK prediction?
Combination products
Combination products
Devices that deliver a drug or biologic introduce coupled physics that neither a pure drug model nor a pure device model captures. We bridge the gap — modeling fluid-structure interaction, drug elution, and device mechanics in a single validated framework.
Questions we answer
How does stent strut geometry affect local drug concentration in the vessel wall?
What coating thickness and drug loading minimize restenosis risk?
How does catheter tip design influence drug delivery uniformity?
Can I predict elution profiles from a coated implant under physiological flow?
Medical devices
Medical device design modeling
From implant stress analysis to heat transfer in ablation catheters, we apply validated computational models to the physical questions that drive your design iterations and 510(k) or PMA submissions.
Questions we answer
Will this implant geometry withstand cyclic loading over 10 years?
What is the thermal dose delivered to target tissue during RF ablation?
How does fluid shear stress vary across my microfluidic channel design?
Can simulation replace or reduce animal studies for this design change?
AI drug discovery & TechBio
AI drug discovery and TechBio
Neural-operator surrogates trained on validated physics solvers compress formulation screening from weeks to hours. We build and calibrate these models for your specific design space — giving AI drug discovery teams a physics-grounded engine rather than a black-box predictor.
Questions we answer
Can I screen 10,000 formulation candidates against a release target without running 10,000 simulations?
How do I know when my surrogate model is operating outside its valid range?
Can I calibrate a PK/PD model to my clinical data and use it to guide next-cycle dosing?
What physics constraints should I impose on my generative model to avoid physically impossible candidates?