Rule-based whole body modeling for analyzing multi-compound effects

• Authors: Woochang Hwang;Youngdeuk Hwang;Sunjae Lee;Doheon Lee
• Venue: Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
• Year: 2012

Essential reasons including robustness, redundancy, and crosstalk of biological systems, have been reported to explain the limited efficacy and unexpected side-effects of drugs. Many pharmaceutical laboratories have begun to develop multi-compound drugs to remedy this situation, and some of them have shown successful clinical results. Simultaneous application of multiple compounds could increase efficacy as well as reduce side-effects through pharmacodynamics and pharmacokinetic interactions. However, such approach requires overwhelming cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models. Here we propose a rule-based whole-body modeling platform. We have tested this platform with Type 2 diabetes (T2D) model, which involves the malfunction of numerous organs such as pancreas, circulation system, liver, and muscle. We have extracted T2D-related 117 rules by manual curation from literature and different types of existing models. The results of our simulation show drug effect pathways of T2D drugs and how combination of drugs could work on the whole-body scale. We expect that it would provide the insight for identifying effective combination of drugs and its mechanism for the drug development.