A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE

• Main Authors: Kevin eMcNally (Author), George D Loizou (Author)
• Format: Book
• Published: Frontiers Media S.A., 2015-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1663-9812; 10.3389/fphar.2015.00213
• Keywords: PBPK; Prior distribution; Human variability; Reverse dosimetry; QIVIVE; Therapeutics. Pharmacology; RM1-950
• Source: Frontiers in Pharmacology, Vol 6 (2015)

The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico and in chemico systems can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus a realistic description of the variation in the physiology of the human population being modelled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.