CBC Talk on Computational Models of Doxorubicin Mitochondria Cardiotoxicity - March 11, 2016
Drug cardiotoxicity is difficult to predict and is one of the major causes of preclinical and clinical drug withdraw. The in vitro experimental data in order to assist in the prediction and understanding of the underlying mechanisms of cardiotoxicity.
The first case study is can exacerbate the mitoch includes several key components for the analysis: TCA cycle, transporters, the respiratory chain, ROS production, and ROS scavenging systems. The redox cycling activity of the drug was represented by an increase in the ROS production on Complex I. The activities and densities of Complexes I to IV were reduced to represent the direct effects of doxorubicin on the respiratory chain. The chronic effects were represented by including an equation for the mtDNA content that scales the densities of proteins encoded by it. The mtDNA content can be reduced by damage caused by increased levels of ROS and increased by mtDNA repair. Variations in the levels of ATP, ROS, antioxidants and the mitochondrial membrane potential were used to quantify the contribution of each of the components to mitochondrial dysfunction.
Multiple features associated a dose dependent depolarization of the mitochondrial membrane, reduction in the ATP and antioxidant levels and an increase in the levels of ROS. Our results support the hypothesis that redox cycling and inhibition of the respiratory chain have a secondary role in doxorubicin cardiotoxicity and direct mtDNA damage is the main trigger for the vicious cycle that leads to chronic cardiotoxicity
The methods used in this work serve as a framework for the development of computational tools to assist in the prediction of cardiotoxicity of new compounds. Using in vitro experiments to acquire data of the effects a drug on specific subcellular components and incorporating this information in detailed biophysical models, it is possible to estimate the effects in vivo in a fast and economical way, with no ethical implications. Future work will involve extending our results to more compounds, acquiring new data and refining our models to include more drug targets.