Our Modeling and Simulation team uses computational and predictive methods to provide insights, scientific evidence and recommendations for decision making during product lifecycles.

Aerosol Dynamics

We use comprehensive aerosol dynamics models to predict the nucleation, condensation and particle size distribution and aerosol mass delivery from an aerosol generator. The results from these models help us better understand the operation and behavior of aerosol-based platforms. 

Exposure

As we have developed product platforms in several potentially reduced-risk categories, we have also built a multitude of comprehensive computational models in collaboration with highly recognized subject matter experts in universities and research institutes. These models predict user and non-user exposure to ingredients in potentially reduced-risk products compared to cigarette smokers. We use an expanded version of Physiologically Based Pharmacokinetic (PBPK) model that we developed to estimate PK profiles.

Product Design and Development

Key to our work is developing and implementing first principle physiochemical-based computational methods and models to make recommendations to improve the performance and quality of our products.

Product Quality, Shelf Life and Performance

Shelf life is an important consideration for every packaged consumer product, including tobacco products. We use physics-based deterministic models to help predict product shelf life under different storage conditions, which ensures our product quality remains equivalent to fresh products and aligns with our product stewardship program.