Helen E. Benson1, Steven Watterson2, Joanna L. Sharman1, Chido Mpamhanga3, Christopher Southan1, Peter Ghazal4,5

1IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Hugh Robson Building, Edinburgh, EH8 9XD, UK (enquiries@guidetopharmacology.org)2Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Altnagelvin Hospital Campus, Derry, Northern Ireland, BT47 6SB, UK3MRC Technology, 1-3 Burtonhole Lane, Mill Hill, London, NW7 1AD, UK4Division of Pathway Medicine, University of Edinburgh Medical School, 49 Little France Crescent, Edinburgh, EH16 4SB, UK5Centre for Synthetic and Systems Biology, CH Waddington Building, King’s Buildings, Mayfield Road, Edinburgh, EH9 3JD, UK

Systems Pharmacology as a tool for future therapy development: a feasibility study on the cholesterol

biosynthesis pathway

Results

Examples of GPCR database tables

Introduction to systems pharmacology

Introduction

“Systems pharmacology… seeks to understand how medicines work on various systems of the body. Instead of considering the effect of a drug to be the result of one specific drug-protein interaction, systems pharmacology considers the effect of a drug to be the outcome of the network of interactions a drug may have.” Wikipedia.

Systems pharmacology has the potential to facilitate a novel range of medical interventions. Databases such as the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) provide information on drugs and their pharmacological effects. Combining these resources with understanding of biological systems gives us the opportunity to predict, model and quantify the effects of drug administration on whole systems. We can also ask how multiple drugs can be used together in new types of therapies that outperform conventional single target therapies.

Here, we explore the feasibility of undertaking a systems pharmacology analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

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Methods

Pathway parameterisation

We used the BRENDA enzyme database to identify kinetic parameters (Km, Kcat) for each reaction and verified the values against the primary literature references.

Inhibitor list

We used BRENDA, the ChEMBL med-chem database, and primary literature searches to identify inhibitors and reaction-specific inhibition constants (Ki). We cross-checked chemical structures reported in references with online chemistry databases and the structures of the marketed statin drugs.

Hypothesis generation

We built kinetic models of the pathway as systems of Ordinary Differential Equations (ODE) and combined these with the pathway and inhibitor parameters to create a model of the mevalonate pathway.

We used our model to predict the best drug combination that would suppress the production of squalene as a precursor for cholesterol, but maintain production of geranylgeranyl-PP at the same level as in the absence of any inhibitors in the pathway system.

We compared this to the wild-type state and in the presence of the statin drug rosuvastatin.

A systems pharmacology study of the cholesterol biosynthesis pathway

The cholesterol biosynthesis pathway is critical to cardiovascular health and is implicated in innate immunity. It includes the target of blockbuster statin drugs, hydroxymethylglutaryl-CoA reductase (HMGCR), and farnesyl diphosphate synthase (FDPS), the target of bisphosphonates used to treat osteoporosis. The geranylgeranyl-PP fork mediates the innate immune response and the myopathy side-effects of statin treatment (Figure 1).

Therefore, we would expect this pathway to be amongst the most thoroughly characterised in the literature, and this is the reason we chose it.

Figure 1. The mevalonate branch of the cholesterol biosynthesis pathway.

The results of our pathway and inhibitor curation are available at http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=104.

Our pathway model will be available in the BioModels database.

Pathway parameters

We obtained only 12 out of 24 kinetic parameters required, even after pooling across human, mouse and rat.

Inhibitor list

We obtained Ki values of inhibitors for 8 out of 10 enzymes when pooling human and rat data.

Problems encountered included:

- Incorrect recording of values and units in databases and literature

- Ambiguous reporting of substrate and ligand stereoisomers and name-structure relationships

- Varying experimental conditions

- Incorrect target or species assignation in databases

Hypothesis generation

Our model predicted the combination of drugs in Figure 2 as optimal to suppress production of squalene, while maintaining production of geranylgeranyl-PP at wild-type levels (Figure 3).

In contrast, a dosage of 2006 nM of rosuvastatin alone was required to achieve the same level of squalene suppression.

Figure 3. A) Flux through the pathway for three treatment regimes: wild-type (treatment free), optimised multidrug intervention, and rosuvastatin intervention. The branch to squalene continues horizontally and the branch to geranylgeranyl-PP continues vertically. B) The flux through the endpoints of the squalene branch (blue) and the geranylgeranyl-PP branch (red) in each of the three regimes.

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Discussion

Multidrug approaches can be designed to minimise a specific off-target effect of an intervention directly as demonstrated here. The combined dose of all the multiple drugs could be significantly lower than the dose of the single drug. However, their development leads to significant challenges as it essentially replicates the single drug development process several times.

We found that there are several common practices currently holding back systems pharmacology, and suggest some steps to resolve these for future studies (Table 1).

Problem Proposed solution

Incomplete parameterisation, lack of systematic recording

Systematic studies, possibly automated, using in vivo or in vitro models

Ambiguous chemical structures, incorrect data reporting

Introduction of data capture standards and guidelines

Curation errors in databases Implementing QC steps such as independentcuration

No systematic mechanism of accessing data Ensure databases implement standards and tools for data access e.g. APIs

Table 1. Common practises currently holding back systems pharmacology and our proposed solutions.

Figure 2. The optimal drug combination predicted by our model to suppress production of squalene, a precursor of cholesterol, while maintaining production of geranylgeranyl-PP at wild-type levels.