# Yang et al. (2007), Arachidonic Acid

August 2008, model of the month by Dominic P. Tolle
Original model: BIOMD0000000106

 Arachidonic acid (AA) is the biochemical precursor of inflammatory mediators such as prostaglandins (PGs) and leukotrienes (LTs). As a consequence, the AA metabolic network is a target of many of the pharmaceutical industries most popular and widely used drugs, such as Nonstroidal anti-inflammatory drugs (NSAIDs). Despite the many years of anti-inflammatory drug research, unexpected and undesirable side-effects of drug usage still plague the industry costing vast sums of money [1]. In recent years, the pharmaceutical industry has sent out tentative feelers towards Systems Biology as an aid to drug discovery and a means to understand and control the emergence of side-effects for drug candidates [2]. The model of the AA metabolic network created by Yang et al ([3], BIOMD0000000106) is an example of the use of biological pathway modelling to elucidate the pharmacological effectiveness of drugs for treatment of inflammation. The authors create a model of the AA metabolic network (see Figure 1) and use it to investigate flux through the various branches of the network, as well as, the effects of inhibition of various branches of the network. Model parameters for the ODE-based model are taken from the primary literature, as well as by fitting of calculated output to experimental data. The branches of the AA metabolic network of interest to the authors are the 15-LOX, the 5-LOX and the COX-2 pathway, with PGE2, LTA4, and LTB4 the major inflammatory mediators observed in the model. Figure 1. The AA Metabolic Network. From [3].

Flux analysis (Figure 2A) shows that the first 5 min are important for the flux through the 5-LOX pathway and production of LTs. Following the initial phase, the flux through the 5-LOX pathway is shut off by negative feedback and the flux through the 15-LOX pathway becomes dominant. Simulations of COX-2 inhibition (Figure 2B), 5-LOX inhibition (Figure 2C) and a combination (Figure 2D) of both are also shown, and let the authors argue that a combination of inhibition of the 5-LOX and COX-2 branch would give more effective therapeutic results.

Figure 2. Flux analysis of the three main pathways: 5-LOX (red), 15-LOX (blue) and COX-2 (green). No inhibitors (A); COX-2 inhibitor (B); 5-LOX inhibitor (C); both inhibitors (D). After [3].

To further analyse the pharmacological effects of inhibiting the 5-LOX branch and the COX branch, two strategies were simulated: dual function inhibitors and a mix of two types of inhibitors, one for each enzyme. The potency of these two strategies is dependent on two related concepts: the relative inhibition constant to different enzymes (DR) for the dual function inhibitor and the mixing ratio (MR) for the mixture of two types of inhibitors. The inhibition intensity (I) on the production of LTs and PGs was calculated to evaluate the efficacy of inhibition for the dual-function inhibitor and the mixture of inhibitors. Both, effect of relative inhibitor/enzyme concentrations and the ratio of DR/MR value to relative activity of the two enzymes (ER) was investigated (Figure 3). The dual-function inhibitor and the inhibitor mixture both had the largest effective concentration region when the DR/MR value was close to the ER of the two enzymes, however the dual-function inhibitor was more effective than the mixture of inhibitors at low concentrations.

Figure 3. Effect of mixture and dual-function COX-2/5-LOX Inhibitors. Mixture of two inhibitors (A); Dual-function inhibitor (B); Comparison of two inhibitor strategies (C). After [3]

Many of the pharmaceutical industries most successful drugs act on the AA metabolic network. The model of Yang et al. describes the dynamics of this well studied systemfollowing pharmacologically relevant perturbations. The authors use the model as an effective example of the use of Systems Biology to aid understanding of drug action.

## Bibliographic References

1. D. Singh. Merck withdraws arthritis drug worldwide. BMJ 329:816, 2004. [SRS@EBI]
2. E.C. Butcher, E.L. Berg and E.J. Kunkel. Systems Biology in drug discovery. Nat. Biotechnology 22:1253-1259, 2004. [SRS@EBI]
3. K. Yang, W. Ma, H. Liang, Q. Ouyang, C. Tang and L. Lai. Dynamic Simulations on the Arachidonic Acid Metabolic Network. PLOS Computational Biology 3(3):e55, 2007. [SRS@EBI]