Inflammation and other common disorders including diabetes,
cardiovascular disease, and
cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target
drug discovery approach. Though
inflammation is a normal bodily reaction, uncontrolled and misdirected
inflammation can cause inflammatory diseases such as
rheumatoid arthritis and
asthma. Nonsteroidal anti-inflammatory drugs including
aspirin,
ibuprofen,
naproxen, or
celecoxib are commonly used to relieve
aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory
drug discovery, a balanced treatment strategy should be developed at the
biological network level. In this Account, we focus on our recent progress in modeling the
inflammation-related
arachidonic acid (AA) metabolic network and subsequent multiple
drug design. We first constructed a mathematical model of
inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established
bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling
inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of
cyclooxygenase-1 and -2 and
leukotriene A4 hydrolase, as well as
5-lipoxygenase and
prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of
drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target
drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of
traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several
enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover
enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control
inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and
drug discovery can provide novel solutions for disease control, which also calls for new developments in
drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe
drug discovery.