Effectively quantifying and communicating disease risk assessment information to consumers is notoriously difficult. Confusion can be caused by the inherent complexity of the information, and can be compounded by uncertainty, knowledge gaps and a lack of consensus among those experts providing information. This presentation consolidates current scientific understanding of the progression of chronic obstructive pulmonary disease (COPD) and highlights a novel approach that uses a computational model to explore mechanisms of COPD and facilitates quantification and communication of risk. This is achieved through provision of a risk estimate, framed within a sound understanding of the associated uncertainties. This is derived by combining a Bayesian risk assessment model with an evaluation of the uncertainty associated with the model’s output. The technique combines epidemiological and experimental data in conjunction with disease mechanism models to derive risk metrics. The uncertainty assessment is based on a classification scheme for evaluating the data used for risk assessment. Combination of these elements can allow categorization of products according to their risk profile. An illustrative matrix is proposed to compare products in different risk categories. The use of model hierarchies and the approximation of complex models by simpler, more interpretable variants will be considered as an aid to stakeholders. Understanding and using biologically-based risk assessment models may potentially allow regulators and consumers to make more informed choices among a range of consumer products with different estimated levels of risk.