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Demin et al., (2013). Systems Pharmacology Models Can Be Used to Understand Complex Pharmacokinetic-Pharmacodynamic Behavior: An Example Using 5-Lipoxygenase Inhibitors

April 2015, model of the month by Vincent Knight-Schrijver
Original model: BIOMD0000000490


Introduction

With increasing interest in Systems Pharmacology, it becomes of key importance to demonstrate their worth in modelling clinically relevant systems to result in emergent properties and contribute to effective drug development scenarios. This model, by Demin et al., (2013) [1, BIOMD0000000490], illustrates a neat example whereby a properly modelled system can address the key fulfilments of a Systems Pharmacology model.

Development of therapy for asthma was considered in this case study. The number of people affected by asthma globally is estimated to be upto 300 million [2] and considering that in 2011, a quarter of a million deaths were attributed to asthma, the generation and proper understanding of novel intervention is an important study. This particular research considers the role of the arachidonic acid (AA) pathway and its metabolism into inflammatory mediators.


Model

The model aims to describe the AA pathway including the activity of 5-lipoxygenase (5LO), an enzyme prominent in AA metabolism, and key inflammatory metabolites of AA, leukotrienes (LTs). Drugs have been developed which antagonistically target the LT receptor (montelukast (ML)) as well as the 5LO enzyme (zileuton (ZL)). Different doses of zileuton exert a similar initial response but why does disparity between doses suddenly appear 2 weeks post-administration? Which protein in the pathway is the more effective clinical target? Can these therapies be refined in terms of dosage, regimen or target?

Bolstered from previous work, the model was constructed from 6 submodels to evaluate emergent aspects of AA metabolism that were previously unaccounted for in a single model. The completed model describes LT regulation, eosinophil (EO) dynamics and bronchial reaction to the system (Figure 1).

Figure 1

Figure 1. Schematic view of the model's 3 compartments. EO maturation occurs through interleukin-5 (IL-5) activity. EOs subsequently migrate into the blood compartment where they are activated by cysteinyl LTs (CysLT). Activated EOs (EOa) propagate further CysLT and IL-5 production thereby inducing a positive feedback. CysLT also stimulates accumulation of all EOs into the airway. Both EOs also produce histamine (Hn). The asthmatic bronchoconstriction is caused by the high concentrations of Hn and CysLT (the edges directed at FEV1). Also displayed here are the targets of zileuton (ZL) and montelukast (ML). Figure taken from [1].

Figure 2

Figure 2. Time course and parameter scan of the different drug doses over a period of 100 days. The long term effects of high-dose ZL can be seen here as different from the effects of low-dose ZL. ML seen here does not appear to reach the maximal efficacy achievable in this system. The plot was generated in R using the simulation result obtained from Copasi, version 4.14 (Build 89).

Results

The model produced results qualitatively comparable to clinical data recorded in-vivo[1]. The long term effects of ML and ZL at two different doses can be seen in Figure 2. ZL was able to change the disease state dramatically after 24 days at 600 mg but not at 400 mg so that the forced expiratory volume (FEV1) increased by 23%. ML managed to increase FEV1 at 50 mg in a more modest manner by around 10%.

Dose responses for each drug indicate key differences in potency (Figure 3). Simulations predict that for ML to elicit a response of maximal efficacy (comparable to the the effects of ZL), considerably higher doses were required than those considered in normal clinical use (Figure 3b).

Figure 3a Figure 3b

Figure 3. Dose response curves for zileuton (a) and montelukast (b). Both ZL and ML display dose-dependent properties. Maximal efficacy is achieved at a lower dose with ZL than with ML highlighting the differences in mechanisms. The plots were generated in R using the simulation results obtained from Copasi, version 4.14 (Build 89).

Conclusion

Utilising a systems pharmacology approach, this model explores different interventions upon the same pathway. It sought to explain the observations seen in the clinic and demonstrated a clear use of quantitative systems pharmacology (QSP) in developmental strategy. The authors describe that LT receptor antagonists would benefit greatly from boosts in affinity compared to current 5LO inhibitors. As a QSP case study, with emphasis on target selection, the model serves as an indication of the boon that translational techniques can provide in the early discovery stages.

References

  1. Demin et al. Systems Pharmacology Models Can Be Used to Understand Complex Pharmacokinetic-Pharmacodynamic Behavior: An Example Using 5-Lipoxygenase Inhibitors CPT Pharmacometrics Syst Pharmacol. 2013 Sep 11;2:e74. doi: 10.1038/psp.2013.49.
  2. Global Initiative for Asthma. Pocket Guide for Asthma Management and Prevention. 2015.
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