Mathematical modelling and statistical analysis of indocyanine green and other biomarkers of hepatic function and drug-induced liver injury

Joseph Leedale, Chantelle L Mason, Nathalie Brillant, Steven D Webb, James W Dear

Research output: Contribution to journalArticlepeer-review

Abstract

The diagnosis and evaluation of drug-induced liver injury is a complex problem that often relies on the measurements of biomarkers of hepatic function. The utility and translatability of these biomarkers is dependent on many factors such as practicality, invasiveness, cost, relevance and ease of use. All of this is underlined by assumptions regarding the validity of a given biomarker as a metric that directly relates to hepatic function and consequently, liver injury and even necrosis. To understand biomarker validity it is important to understand the system mechanisms that influence the measurements of these biomarkers and how they are affected by hepatotoxic drug doses. Mathematical modelling allows for the explicit representation of biomarker mechanisms that influence blood dynamics and is therefore a useful tool in enhancing the understanding of the impact of liver injury upon biomarker dynamics. Indocyanine green (ICG) is used as a biomarker of hepatic function due to properties such as exclusive hepatic clearance. Additionally, the biomarker can be used as a visual tool for non-invasive imaging techniques such as multi-spectral optoacoustic tomography (MSOT). A combined systems toxicology framework includes a pharmacokinetic model of ICG clearance that represents underlying biological processes. The framework enhances mechanistic understanding of the relationship between drug dose, ICG kinetics and liver injury over time. Statistical analysis techniques whereby ICG is combined and compared with other established biomarkers of DILI are able to further assess the pre-clinical potential, relevance and translatability of ICG clearance measured by photoacoustic imaging as a metric of liver function.
Original languageEnglish
JournalComputational Toxicology
Volume16
Early online date29 Aug 2020
DOIs
Publication statusPublished - Nov 2020

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