From smoke to disease risk modelling. An integrated approach for risk assessment of potential reduced-risk tobacco products
Presented at Society for Risk Analysis (SRA) 2008
Philip Morris International is committed to the development of reduced-risk tobacco products (RRTP). This requires a state-of-the-art scientific approach to disease risk assessment, integrating toxicological and clinical assessment together with disease risk modelling. Evaluation of the relative toxicity of smoke constituents from a potential RRTP is the first stage of this process. Further investigations are then performed to evaluate the toxicological profile and to assess the potential for reduced risk. Short-term confinement studies to evaluate changes in biomarkers of exposure in subjects switching to potential RRTPs are performed. If clear reductions in exposure are demonstrated, longer-term clinical studies are performed to evaluate biomarkers of effect in real-life situations. Another important component is human smoking topography, which involves evaluating changes in the smoking behavior of subjects switching to potential RRTPs. Effectively quantifying risk is a complex task. Predictive mathematical and computational models are being developed and refined for three major smoking-related diseases (cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer). This case history illustrates a model of cardiovascular disease which comprises the pathophysiology of cholesterol metabolism, atherosclerosis, thrombosis, and plaque rupture. Using longitudinal data from clinical studies, changes in biomarkers of effect are translated into an index of plaque instability. When extrapolated over time, this can predict cardiovascular risk for both an individual and a virtual population. The certainty of the risk is evaluated using a measurement certainty index based on the validity and reliability of the integrated data. This abstract describes a unique integrated risk assessment process which enables objective categorization of potential RRTPs according to their predicted risk.