Novel in vitro and mathematical models for the prediction of chemical toxicity

• Published in: Toxicology research (Cambridge) 2013-01, Vol.2 (1), p.40-59
• Main Authors: Williams, Dominic P, Shipley, Rebecca, Ellis, Marianne J, Webb, Steve, Ward, John, Gardner, Iain, Creton, Stuart
• Format: Article
• Language: English
• Subjects: Chemistry
• ISSN: 2045-452X; 2045-4538
• DOI: 10.1039/c2tx20031g

The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. The scientific basis of drug safety is very complex and currently remains poorly understood, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ∼21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of (1) the physiological gap between cells currently used and human hepatocytes existing in their native state, (2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, (3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, (4) lack of consideration of systemic effects. Reproduction of centrilobular and periportal hepatocyte phenotypes in culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen and hormone gradients. Application of bioartificial liver (BAL) technology can encompass predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of experiments, and will enable both refinement and reduction of animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct and research, and can be used as a framework for other areas of drug safety science.