Quantifying the risk-reduction potential of new Modified Risk Tobacco Products

Area of Science:

• Tobacco control research
• Quantitative risk assessment
• Public health modeling

Background:

• Assessing novel Modified Risk Tobacco Products (MRTPs) requires indirect measures before epidemiological data is available.
• Population Health Impact Models (PHIMs) are crucial for estimating disease reduction from MRTP adoption.
• The F-factor, representing effective dose reduction, is a key parameter in PHIMs.

Purpose of the Study:

• To develop a method for estimating the F-factor using biomarker data from clinical studies.
• To inform the Population Health Impact Model (PHIM) for quantitative risk assessment of MRTPs.
• To establish a transparent framework for translating biomarker evidence into risk reduction estimates.

Main Methods:

• Utilized biomarker data from clinical studies comparing MRTP use to smoking cessation.
• Formulated a link function to translate biomarker effects into the F-factor.
• Introduced concepts of 'lack of sufficiency' and 'necessity' for link function parametrization.
• Developed a method for uniformly sampling link functions to create different translation scenarios.

Main Results:

• Established a transparent methodology to link biomarker data to the F-factor.
• Enabled scenario-based F-factor estimation through parametrized link functions.
• Provided a framework for informing PHIMs with biomarker-derived evidence for MRTP risk assessment.

Conclusions:

• Biomarker data analysis offers a viable method for estimating MRTP's effective dose (F-factor).
• The developed framework allows for flexible and transparent translation of biomarker evidence into PHIM parameters.
• This approach supports quantitative risk assessment of novel tobacco products in the absence of long-term epidemiological data.